bluuebunny/crossref_metadata_2025 huggingface dataset:
Pharmacological interactions of jadomycin B with topoisomerase poisons in MDA-MB-231 human breast cancer cells
Authors: McKeown Brendan T. , Goralski Kerry B.
Published: October 2024

Jadomycin B, a natural product isolated from Streptomyces venezuelae, exerts an anti-cancer effect on human triple negative breast cancer cells in vitro and has anti-tumoral effects in vivo in animal models of breast cancer. One proposed mechanism for this anti-cancer effect is through interaction with topoisomerase 2 (TOP2). Based on the previously described interactions between jadomycin B and TOP2 we hypothesized that jadomycin B will act additively with TOP2 poisons and produce a similar functional outcome in eliciting cell cycle arrest. Combined treatments of jadomycin B and the TOP2 poisons doxorubicin or mitoxantrone produced moderately synergistic to additive cytotoxicity (combination index values ranging from 0.72–0.94) in MDA-MB-231 cells. In comparison, combined mitoxantrone and doxorubicin produced additive cytotoxicity (combination index values 0.96–1.11). Jadomycin B combined with the proteosome inhibitor MG132 had additive cytotoxicity (combination index values 0.76–1.18). In contrast, mitoxantrone or doxorubicin cytotoxicity was antagonized by MG132 (combination index values 1.21–2.31). Jadomycin B treatment arrested cells in S-phase ( P = 0.0024) as opposed to mitoxantrone which caused G2/M-phase arrest ( P < 0.0001). In conclusion, jadomycin B interacts differently than known TOP2 poisons in combination, supporting a novel pharmacological mechanism(s) of action for jadomycin B cytotoxicity.
Tracking the Early-life of PtNi/C Shape-Controlled Catalysts upon their Integration in PEMFC
Authors: Roiron Camille , Ripa Masuma Sultana , Pierinet Lisa , Charlot Frédéric , Maillard Frédéric , Brimaud Sylvain , Dubau Laetitia
Published: October 2024

The integration of promising bimetallic electrocatalytic active materials for oxygen reduction reaction (ORR) into practical and functional proton-exchange membrane fuel cell (PEMFC) electrodes remains largely impeded by the poor performances that these exhibit at high current loads. The early life of PtNi/C catalysts presenting either structurally faceted/ordered or defective/disordered surface morphology is compared to that of both spherical PtNi/C and Pt/C catalysts. Different single-cell operating conditions were studied. At low current density, in the kinetically limited region, a good agreement between liquid electrolyte and single-cell configuration is reported and the kinetic benefit of PtNi/C catalysts compared to Pt/C is (at least partially) maintained. However, PtNi/C catalysts show severe limitations in the O2 mass transport limited region. Morphological and compositional changes were monitored at each stage showing that Ni atoms are leached at every step from ink formulation to the first PEMFC test. We show that Ni is already redistributed in the membrane in the fresh membrane electrode assembly (MEA) state. Ni2+ cationic contamination of the ionomer/membrane contributes to the disappointing results obtained in MEA configuration. In addition, for shaped-controlled PtNi/C, the surface faceting loss combined with restructuring via coalescence and crystallite growth further compromise their transfer in technological devices.
Tailored PEO/PEG-PPG Polymer Electrolyte for Solid-State Lithium-Ion Battery
Authors: Helaley Ahmad , Zhan Guodong (David) , Liang Xinhua
Published: October 2024

Solid polymer electrolytes (SPEs) offer a promising avenue for advancing the performance and safety of all-solid-state batteries. In this study, we investigated the effects of blending poly(ethylene oxide) (PEO) with amorphous poly(propylene glycol) (PPG) or poly(ethylene glycol-ran-propylene glycol) (PEG-PPG) to engineer SPEs with enhanced electrochemical properties. By blending PEO with PEG-PPG or PPG, we effectively lowered the crystallinity of PEO, facilitating the diffusion of lithium ions through the SPE and broadening its operational temperature range. This optimization strategy enhanced ionic conductivity and significantly reduced the interfacial resistance between the electrolyte and electrode, thus improving electrochemical stability. The solid-state lithium/lithium iron phosphate (LiFePO4 or LFP) with PEO blended with 7% PEG-PPG showed excellent charge/discharge cycling stability, with maximal discharge capacities of 165 mAh g−1 and 162 mAh g−1 at a current rate of 0.2 C at 60 and 125 °C, respectively. The PEG-PPG-based SPE outperformed the PPG polymer-based SPE in terms of better electrochemical performance and a wider temperature range due to its longer polymer chain. This combination offered improved ionic conductivity, interface stability, and electrolyte compatibility with electrodes, making it highly suitable for high-demand applications of lithium-ion batteries.
Effect of Alkaline Etching on Formation Behavior and Corrosion Resistance of a Cr-Zr-Ti TCP Conversion Coating on AA2024 Aluminum Alloy
Authors: Li Minghao , Ma Yanlong , Yan Jiangrong , Ma Minyu , Wu Liang , Wu Haisheng
Published: October 2024

Trivalent chromium process conversion coating is considered to be the ideal alternative to traditional chromate conversion coating due to its high corrosion resistance and low toxicity. However, achieving a high corrosion resistance trivalent chromium conversion coating on 2xxx aluminum alloys has been a great challenge. Herein, based on a newly developed trivalent chromium conversion solution (CN patent, ZL 202210804263.2), the effect of alkaline etching on the formation behavior and corrosion resistance of the coating on AA2024-T351 aluminum alloy was investigated. It was found that under the same conditions, the mechanically polished sample and the sample alkaline etched for 30 s showed the highest corrosion resistance. When the alkaline etching time was too short or too long, the corrosion resistance of the coating deteriorated. This is ascribed to changes in the surface microstructure of the alloy during alkaline etching, i.e., reduced number of intermetallic particles and formation of a copper enrichment layer. Reducing the number of intermetallic particles on the alloy surface is beneficial for uniform growth of the coating. However, the copper enrichment layer participated in formation of the coating, resulting in doping of copper species in the coating, which is detrimental to the corrosion resistance of the coating.
Table 1 from Characterization of the Breast Cancer Liver Metastasis Microenvironment via Machine Learning Analysis of the Primary Tumor Microenvironment
Authors: Goodin Dylan A. , Chau Eric , Zheng Junjun , O’Connell Cailin , Tiwari Anjana , Xu Yitian , Niravath Polly , Chen Shu-Hsia , Godin Biana , Frieboes Hermann B.
Published: October 2024

Characteristics of patients with breast cancer. TNM classification was 0 and 1 for primary tumor and liver metastatic disease, respectively
Supplementary Table S8 from Characterization of the Breast Cancer Liver Metastasis Microenvironment via Machine Learning Analysis of the Primary Tumor Microenvironment
Authors: Goodin Dylan A. , Chau Eric , Zheng Junjun , O’Connell Cailin , Tiwari Anjana , Xu Yitian , Niravath Polly , Chen Shu-Hsia , Godin Biana , Frieboes Hermann B.
Published: October 2024

Table S8. Variable Importance for predicting BCLM CD56+ using primary tumor clusters.
Supplementary Table S10 from Characterization of the Breast Cancer Liver Metastasis Microenvironment via Machine Learning Analysis of the Primary Tumor Microenvironment
Authors: Goodin Dylan A. , Chau Eric , Zheng Junjun , O’Connell Cailin , Tiwari Anjana , Xu Yitian , Niravath Polly , Chen Shu-Hsia , Godin Biana , Frieboes Hermann B.
Published: October 2024

Table S10. Variable Importance for predicting BCLM CD68+CD163+CD206+ using primary tumor clusters.
Supplementary Table S1 from Characterization of the Breast Cancer Liver Metastasis Microenvironment via Machine Learning Analysis of the Primary Tumor Microenvironment
Authors: Goodin Dylan A. , Chau Eric , Zheng Junjun , O’Connell Cailin , Tiwari Anjana , Xu Yitian , Niravath Polly , Chen Shu-Hsia , Godin Biana , Frieboes Hermann B.
Published: October 2024

Table S1. Hyperparameters used for AUROC-optimized Machine Learning models.
Supplementary Figure S6 from Characterization of the Breast Cancer Liver Metastasis Microenvironment via Machine Learning Analysis of the Primary Tumor Microenvironment
Authors: Goodin Dylan A. , Chau Eric , Zheng Junjun , O’Connell Cailin , Tiwari Anjana , Xu Yitian , Niravath Polly , Chen Shu-Hsia , Godin Biana , Frieboes Hermann B.
Published: October 2024

S6. PLS-DA score plots of classifying BCLM patient IMC clusters into Low (
Supplementary Figure S12 from Characterization of the Breast Cancer Liver Metastasis Microenvironment via Machine Learning Analysis of the Primary Tumor Microenvironment
Authors: Goodin Dylan A. , Chau Eric , Zheng Junjun , O’Connell Cailin , Tiwari Anjana , Xu Yitian , Niravath Polly , Chen Shu-Hsia , Godin Biana , Frieboes Hermann B.
Published: October 2024

S12. AUROC achieved by ML models using primary tumor IMC clusters across a variable number of features to predict BCLM IMC cluster densities (as stated in gray box of each panel)
A Colombian Boy with a Novel de novo PURA Variant: A Case Report
Authors: Sinisterra-Díaz Stiven Ernesto , Vasquez-Forero Diana Marcela , Ordoñez Laura , Prieto Luis Eduardo , Pachajoa Harry
Published: October 2024

Introduction: PURA syndrome is a neurodevelopmental disease caused by de novo pathogenic variants in PURA encoding the purine-binding element alpha protein. It is characterized by autosomal dominant inheritance and a heterogeneous phenotype. Case Presentation: We describe a 7-year-old patient with history of congenital pneumonia, accompanied by hypotonia, convulsive episodes, poor sucking ability, neurodevelopmental delay. Physical examination revealed some dysmorphic features. Molecular analysis identified a de novo, heterozygous variant in PURA (NM_005859.5): c.692T>C; p.Phe231Ser, which was classified as pathogenic. Conclusion: In this report, we present a Colombian case of PURA syndrome. This case highlights the challenges associated with the early diagnosis of a newly described syndrome, in a limited-resources health system.
Design and testing of a nutrient solution control system for soilless culture using mathematical models
Authors: Chaila Sirinya , Soemphol Chaiyong
Published: October 2024

The optimization of nutrient management is crucial for successful soilless plant cultivation, where precise control of fertilizer application significantly impacts plant growth. This research addresses the challenge of developing an effective nutrient control system tailored for soilless cultivation by focusing on regulating electrical conductivity (EC) levels in nutrient solutions. The proposed system utilizes mathematical models and linear regression techniques to manage the nutrient solution mixing ratio. To ensure accuracy, sensors were calibrated, achieving a 99.59% accuracy rate for pH measurement and 95.25% for EC measurement. Experimental validation of the system demonstrated that, with a target EC range of 1.5-2.3 mS/cm, a 10 L solution volume yielded a maximum error rate of 1.75% and an average error of 0.95%. In contrast, a 50 L solution volume showed a slight increase in maximum error rate to 2.89% and an average error of 2.08%. These results highlight the system’s capability to precisely adjust EC levels using a defined linear regression model for AB liquid fertilizer ratios. In conclusion, the developed system effectively controls nutrient levels, demonstrating its potential for enhancing nutrient management in hydroponic farming applications.
Comparative analysis of whale and Harris Hawks optimization for feature selection in intrusion detection
Authors: Abualhaj Mosleh M. , Hiari Mohammad O. , Alsaaidah Adeeb , Al-Zyoud Mahran M.
Published: October 2024

This research paper explores the efficacy of two nature-inspired optimization algorithms, the whale optimization algorithm (WOA) and Harris Hawks optimization (HHO), for feature selection in the context of intrusion detection and prevention systems (IDPS). Leveraging the NSL-KDD dataset as a benchmark, our study employs Python for implementation and uses decision tree (DT) as the classification model. The objective is to assess the impact of the HHO and WOA optimization techniques on the performance of IDPS through feature selection. The WOA and HHO techniques were able to lessen the features from 40 to 16 and 13, respectively. Results indicate that DT integrated with HHO achieves an impressive accuracy of 97.59%, outperforming the WOA-enhanced model, which attains an accuracy of 97.5%. This study contributes valuable insights into the comparative effectiveness of WOA and HHO optimization algorithms in enhancing the accuracy of IDPSs, shedding light on their potential applications in the realm of cybersecurity.
A comparative study of pre-trained models for image feature extraction in weather image classification using orange data mining
Authors: Doungpaisan Pafan , Khunarsa Peerapol
Published: October 2024

This paper presents a detailed comparative analysis of pre-trained models for feature extraction in the domain of weather image classification. Utilizing the orange data mining toolkit, we investigated the effectiveness of six prominent pre-trained models-InceptionV3, SqueezeNet, VGG-16, VGG-19, painter, and DeepLoc-in accurately classifying weather phenomena images. Among these models, InceptionV3, in conjunction with neural networks, emerged as the most effective, achieving a classification accuracy (CA) of 96.1%. Painter and SqueezeNet also showed strong performance, with accuracies of 95.1% and 86.7%, respectively, although they were surpassed by InceptionV3. VGG-16 and VGG-19 provided moderate accuracy, while DeepLoc underperformed significantly with a maximum accuracy of 56%. Neural networks consistently outperformed other classifiers across all models. This study highlights the critical importance of selecting appropriate pre-trained models to enhance the accuracy and reliability of weather image classification systems.
Enhancing fake profile detection through supervised and hybrid machine learning: a comparative analysis
Authors: Bensassi Ismail , Ndama Oussama , Kouissi Mohamed , En-Naimi El Mokhtar
Published: October 2024

In modern times, social networks have become ubiquitous platforms facilitating widespread information dissemination, resulting in significant daily data generation. This increase in data production encompasses a wide range of user-generated content, which in turn promotes the proliferation of fraudulent users creating fake profiles and engaging in deceptive activities. This article aims to address this challenge by employing machine learning algorithms to accurately identify fake profiles. The research involves a thorough analysis of various user behaviors, engagement metrics, and content attributes within social platforms. The primary goal is to develop robust models capable of effectively detecting deceptive profiles by meticulously examining user activities and content characteristics. The study explores the application of robust methodologies such as K-means and K-medoids clustering, alongside supervised machine learning classifiers including K-nearest neighbors (KNN), support vector machine (SVM), Bernoulli Naïve Bayes (NB), logistic regression, and linear support vector classification (SVC), specifically tailored for the detection of fake profiles.
LMD-based fault detection scheme for TCSC compensated wind integrated transmission lines
Authors: Market Saritha , Swaminathan Seenivasan , Gurram Ravindranath
Published: October 2024

In this paper, a fast fault detection scheme is presented to detect the faults in thyristor-controlled series capacitor (TCSC) compensated transmission line connected with the large wind farms to export the electrical power to grid. The proposed logic utilizes the current information at the relay location and processes through the local mean decomposition technique to extract the magnitude features of the current. Cumulative sum of these features are computed for each phase currents to detect the faults in the transmission lines and further to classify the faulty phase in the system. The residual component of the current is used to detect the ground involvement in the faulty phase. The proposed method is tested during variety of faults by changing the nature of the fault using the fault parameters. Furthermore, the impact of the TCSC is also investigated along with the dynamic changes of the WF and their influence on the protection scheme. All the simulations are performed in MALTAB-Simulink software.
Apache Spark based distributed clustering for big data analytic with application to 3D road network
Authors: Sethy Rotsnarani , Mahanta Soumya Ranjan , Panda Mrutyunjaya
Published: October 2024

The vast amount of data stored nowadays has turned big data analytics into a very promising research field. Clustering is an essential step in data analysis, widely used for classification, collecting statistics, and acquiring insights in specific domains of knowledge. However, the most of existing algorithms based on Lloyd-Forgy’s method, have an enormously huge average-case complexity while clustering data sets with a large number of features, which may be superpolynomial time (NP-hard) and are severely constrained in terms of speed, productivity, and adaptability. Aiming to improve Lloyd-Forgy’s clustering performance, K-means++ algorithms, a variety of algorithm-level optimizations which is not been well-studied, is discussed along with very promising gaussian mixture model (GMM) and soft clustering based Fuzzy C-means (FCM). Further, for fast and distributed data processing and to leverage the benefits of big data platforms, such as Apache Spark, Spark-based clustering methods are applied on three-dimensional (3D) road network data set which is collected from UCI repository. However, Spark-based clustering research is still in infancy. The distributed computation tests are conducted by allocating two core processors and one databricks unit (DBU) with 15 GB memory and measuring execution times, as well as root mean square error (RMSE), mean absolute error (MAE), clustering accuracy, and silhouette values. The results are promising and provide new research directions in the field of spark-based clustering on big data.
Enhancing hypertension prediction: a hybrid machine learning optimization approach
Authors: Aouragh Abd Allah , Bahaj Mohamed , Toufik Fouad
Published: October 2024

Early identification of hypertension is crucial to prevent its serious complications, which can lead to devastating health effects by threatening lifestyle quality and significantly increasing premature mortality. This study aims to evaluate the effectiveness of machine learning techniques in predicting the presence of hypertension from an unbalanced dataset consisting of 4,363 records and 35 features. To balance the dataset, we employed the synthetic minority over-sampling technique (SMOTE) algorithm. In addition, to select the most relevant features, we used ant colony optimization. Next, we applied various algorithms, including logistic regression (LR), K-nearest neighbors (KNNs), support vector machine (SVM), extra trees (ETs), and AdaBoost (AB). We also evaluated the optimization of hyperparameters using two methods: Bayesian optimization (BO) and particle swarm optimization (PSO). The results reveal that the combination of AB with BO demonstrated superior performance, with an accuracy of 97.60%, a recall of 98.93%, and a precision of 98.59%. This research emphasizes the potential of machine learning techniques for anticipating hypertension and highlights the importance of optimization techniques in improving predictive models’ performance.
Information system success model: continuous intention on users’ perception of e-learning satisfaction
Authors: Fiati Rina , Widowati Widowati , Kusumo Nugraheni Dinar Mutiara
Published: October 2024

The information systems success strategy contributes to understanding of digitalization. This research aims to evaluate user satisfaction with the e-learning system continuously. The research method is hybrid, combining constructs of a unified theory of acceptance and use of technology, the technology acceptance model, and service quality (SVQ). Data collection was conducted through the distribution of questionnaires targeting instructors. Data analysis utilized structural equation modeling with partial least squares. This method was used to test the measurement model with factor loadings and average variance extracted (AVE) above 0.5. Meanwhile, validity testing on cross-loading had indicator values for each variable higher than other variables, with composite reliability above 0.7. These results were supported by hypothesis testing, which indicated that website quality positively influences user satisfaction, leading to sustained intention. The original sample obtained a value of 0.633; mean of 0.624; standard deviation of 0.105, and a p-value below 0.01. Additionally, user subjective norms have a strong relationship between sustained intention and system appropriateness, of 0.763 in using e-learning.
Seasonal meat stock demand used comparison of performance smoothing-average forecasting
Authors: Tundo Tundo , Saifullah Shoffan , Dharmawan Tio , Junaidi Junaidi , Devia Elmi
Published: October 2024

Seasonal patterns significantly influence the demand for beef stock, especially in rural areas that rely on natural feed. Accurate forecasting is essential for managing this demand due to beef's status as a government-regulated nutritional commodity. Food production, consumption, and income levels affect the demand for beef stocks. This research aims to identify the most precise forecasting method for predicting future beef stock needs. We evaluated multiple techniques, including single exponential smoothing (SES), double exponential smoothing (DES), single moving average (SMA), and double moving average (DMA), using the mean absolute percentage error (MAPE) metric, focusing specifically on beef supplies in Pemalang. The results indicated that the DMA method achieved the highest accuracy with a MAPE value of 5.993% at the 4th -order parameter. Additionally, increasing the data volume improved forecasting accuracy, demonstrating the effectiveness of the DMA method for beef stock prediction.
A predictive model for postpartum depression: ensemble learning strategies in machine learning
Authors: Fazraningtyas Winda Ayu , Rahmatullah Bahbibi , Naparin Husni , Basit Mohammad , Razak Nor Asiah
Published: October 2024

Postpartum depression (PPD) presents a significant mental health challenge for mothers following childbirth. While the precise cause of this condition remains unknown, preventive measures and treatments are available. This study aims to employ ensemble learning techniques, utilizing C4.5 decision tree (DT), gradient boosting tree (GBT), and extreme gradient boosting (XGBoost), to predict the occurrences of PPD in the Banjarmasin, South Kalimantan, Indonesia. The predictive model developed encompasses a dataset comprising 317 records gathered from postpartum mothers in hospitals, community health services, and midwifery clinics (referred to as Model 1). Furthermore, resampling techniques (Model 2) were employed to address class imbalance. Additionally, feature selection including forward selection and backward elimination (Model 3) were implemented to enhance model performance. The findings reveal that XGBoost, combined with resampling methods, achieved the highest accuracy rate at 87.57%. Feature selection identified five crucial factors associated with PPD incidence: marital status, number of living children, history of depression, fear of delivery, and family relationships. The utilization of ensemble learning strategies for PPD prediction yields reliable outcomes that can be applied within clinical settings. Exploring alternative ensemble learning strategies such as random forest and adaptive boosting could further optimize model performance and warrant consideration in future research endeavours.
Facial emotion recognition based on upper features and transfer learning
Authors: Zohra Ennaji Fatima , Hamada El Kabtane
Published: October 2024

Facial expression recognition (FER) in the upper face focuses on the analysis and recognition of emotions based on features extracted from the upper region of the face. This region typically affects the eyes, eyebrows, forehead, and sometimes the upper cheeks. Since these areas are often less affected by face masks or other facial coverings, FER algorithms can concentrate on capturing and interpreting the relevant facial cues, such as eye movements, eyebrow positions, and forehead wrinkles, to accurately recognize and classify different emotions. By focusing on the upper face, FER systems can mitigate the impact of occlusions caused by masks and still provide meaningful insights into the emotional states of individuals. In this work, a FER approach focusing on the upper region is proposed. Several experiments have been made using the CK+ dataset in addition to a comparison between the emotion recognition scores using the upper and the entire face in order to determine whether this area can reflect the real expressed emotion. The results of our approach are promising compared to previous studies with an accuracy up to 96%.
Classification of weather conditions based on automatic weather station data using a multi-layer perceptron neural network
Authors: Indrajaya Muhammad Aristo , Sollu Tan Suryani , Subito Mery , Rahman Yuli Asmi , Saputra Erwin Ardias
Published: October 2024

Weather is one of the important elements that greatly determines human activities, especially those related to economic factors. Therefore, understanding weather conditions using weather parameters as a reference is important for human life, so a method is needed to classify weather according to its category so that the information produced can be used for various needs. Determining weather conditions in an area will not run well without a reliable method that can analyze existing weather parameters. Therefore, in this study, the weather condition classification process was carried out using the multilayer perceptron algorithm, a type of neural network (NN) algorithm. All data analyzed were weather parameter data collected by mini weather stations placed on land. The weather parameters used were temperature, humidity, air pressure, wind speed, dew point, wind chill, daily rainfall, solar radiation, and UV index. This study was conducted in Palu city, Central Sulawesi Province, Indonesia. The classification process carried out by the multilayer perceptron algorithm was carried out on the Altair AI Studio application and produced an accuracy value of 93.87%, recall of 92.33%, and precision of 91.29%.
Convolutional neural network-based strategies for efficient content-based image retrieval
Authors: Kamatchi Chinnathambi , Rajendran Rathiya , Nagarajan Kopperundevi , Palanisamy Brinda , Jeyabalan Deepika , Paperananthamurugesan Rama Subramanian
Published: October 2024

Recent years have seen a meteoric rise in the usage of enormous image databases due to advancements in multimedia technologies. One of the most critical technologies for image processing nowadays is image retrieval. This study uses convolutional neural networks (CNNs) for content-based image retrieval (CBIR). With the ever-growing number of digital photos, practical methods for retrieving these images are crucial. CNNs are incredibly efficient in many computer vision applications. Improving the efficacy and precision of image retrieval systems is the primary goal of our research into using deep learning. The paper starts with a thorough analysis of the current state of CBIR methods and the difficulties they face. Afterwards, it explores CNN’s design and operation, focusing on CNN’s capacity to learn hierarchical features from images autonomously. This paper also looks at how the model performs when it alters its hyperparameters, transfer learning techniques, and CNN topologies. The insights obtained from these experiments enhance the comprehension of the elements impacting CNN effectiveness in CBIR. Finally, our study shows that CNNs can change the game for image search by transforming CBIR systems. This research adds to the expanding body of information about using cutting-edge deep learning algorithms to make image retrieval more efficient and accurate.
Spread of harmful substances in the atmosphere of industrial cities of Kazakhstan: modeling and data refinement
Authors: Temirbekov Nurlan , Tamabay Dinara , Tanashova Moldir
Published: October 2024

In Kazakhstan, air pollution in industrial cities poses a significant challenge that requires urgent attention. This study investigates the dispersion of harmful pollutants in the air across nine prominent industrial cities in Kazakhstan. The research involves modeling the emissions from major pollution sources for each city, which provides a comprehensive view of how these substances spread through the atmosphere. The study also examines the distribution patterns of these pollutants to gauge their concentration levels in each urban area. Additionally, it addresses the inverse problem of data assimilation from automated monitoring stations (AMS), aiming to refine the information on pollution sources. By utilizing the conjugate equations method, the study successfully converged to an accurate solution. Detailed visualizations for Almaty, Ust-Kamenogorsk, and Pavlodar illustrate the pollution dynamics and pinpoint the most affected regions. These findings are crucial for formulating strategies to mitigate the adverse effects of industrial emissions on both the environment and public health.
Tecendo sentidos: investigações em estudos linguísticos, textuais e discursivos
Authors:   Universidade de São Paulo. Faculdade de Filosofia, Letras e Ciências Humanas
Published: October 2024

O livro congrega um conjunto de 21 de capítulos aprovados em avaliação duplo-cega por pares e produzidos por docentes e por pesquisadores de variados níveis acadêmicos (Mestrado, Doutorado e Pós-Doutorado), vinculados à USP e a outras universidades parceiras. Os trabalhos se debruçam sobre diferentes práticas sócio-historicamente ancoradas de produção, distribuição, consumo e interpretação de textos a partir de perspectivas teórico-metodológicas distintas, o que ilustra a força dos estudos do texto e do discurso desenvolvidos em nosso país. As principais perspectivas que embasam os estudos reunidos nessa coletânea consistem na Análise do Discurso de linha francesa, na Análise do Discurso Digital, na Análise Crítica do Discurso, na Análise Dialógica do Discurso, na Semiótica, na Linguística Funcional, na Linguística Cognitiva, na Linguística Aplicada, na Linguística Queer, na Crítica Textual e nos Estudos da Argumentação.
Meaning, civilizations, and ecology: Response to Peter Wagner on Cornelius Castoriadis
Authors: Smith Jeremy CA , Adams Suzi , Blokker Paul , Arnason Johann P
Published: October 2024

This engaging response from the Editorial Collective of Social Imaginaries to the Conversation with Peter Wagner takes up themes and problems of civilizations, meaning and ecologies raised in the exchange between Wagner and members of the Collective.
Checklist and distribution of Carabidae Latreille, 1802 and Cicindelidae Latreille, 1802 (Insecta: Coleoptera) of El Salvador
Authors: PABLO-CEA JOSÉ D. , CERÓN-GÓMEZ RAFAEL , ALFARO ELIZABETH , WIESNER JÜRGEN
Published: October 2024

A paucity of knowledge regarding species distribution is one of the main shortfalls in large-scale biodiversity studies. This shortfall results in distribution maps of species that largely reflect sampling effort. Ground beetles (Carabidae) and tiger beetles (Cicindelidae) are among the most diverse and conspicuous families within the Order Coleoptera. However, no previous studies of these families have been published for El Salvador. Based on a literature revision, field work, and several entomological collections, the present article aims to describe the current state of knowledge of Carabidae and Cicindelidae occurrence in El Salvador. Eight subfamilies, 17 tribes, 35 genera, and 66 species are reported from the country; including a new country record of Eurycoleus macularius. We present a local distribution map for each species with available records. Our final aim is to stimulate interest in developing further studies that explore the taxonomic and ecological aspects of Carabidae and Cicindelidae in El Salvador.
Four new species of the Euconnus taiwanus group from China (Coleoptera, Staphylinidae, Scydmaeninae)
Authors: JAŁOSZYŃSKI PAWEŁ
Published: October 2024

Six Eastern Palaearctic species of the Euconnus taiwanus group are known to occur in Taiwan, China and Japan. Four more species are described based on specimens collected in China: E. (s. str.) baoshanus sp. nov. (Yunnan), E. (s. str.) pseudobaoshanus sp. nov. (Yunnan), E. (s. str.) tianmushanus sp. nov. (Zhejiang), and E. (s. str.) pingbianensis sp. nov. (Yunnan).
Active Tectonic Features of Muong Te Zone from Geomophological Indicators
Authors: Tuyen Nguyen , Hang Phung
Published: October 2024

The Muong Te fault zone is known as the active tectonic area in Northwest Vietnam, proved by earthquakes with the most recent one of Ms 4.9 on June 16, 2020 and hundreds of aftershocks accompanying with landslides. The neotectonic activities of the study area is asssessed by analysing 61 sub-basins in term of geomorphological indices: Ratio of valley floor width to valley height (Vf), Stream-Length gradient index (SL), Hypsometric integral index (HI), Hypsometric curve (HC) and Index of Relative Active Tectonics (IRAT). As a results, this are has: relative lowVf corresponding to the terrain of V-shape valleys; SL in large range from 10 to 7000 (gradient - m); straight – convex H ccurve and HI in the range of 0,422 – 0,51 corresponding to the developing phase of river basins; More than 50% of asymmetric basins (AF) due to tectonic impacts. IRAT ranging from 1.25 to 2.5 divided into 3 classes according to the tectonic levels: (1) – strong uplift with - IRAT < 1.5, (2) moderate uplift with IRAT from 1.5 to 2.0 (strong) and (3) weak uplift with IRAT more than 2. The calculated values of the indices are seemed very consistent with the tectonic-geological features of the Muong Te area.
The Impact of Exclusion Reasons and Trait Empathy on Third-Party Compensatory Behavior in Vicarious Exclusion
Authors: Ci Fengjie , Wang Yan
Published: October 2024

Social exclusion is a widespread phenomenon that not only negatively impacts the individuals directly excluded but also deeply affects bystanders who witness such exclusion. This study aims to investigate how bystanders' levels of trait empathy and their perception of the reasons for exclusion shape their third-party compensatory behaviors. Specifically, we examined whether bystanders with higher empathy levels are more likely to engage in compensatory actions when they perceive the exclusion as unjust. Using a 2x2 between-subjects experimental design, participants were divided into groups based on trait empathy (high vs. low) and the perceived appropriateness of the exclusion (appropriate vs. inappropriate). An online experiment was conducted to simulate social exclusion scenarios, and participants' responses were analyzed to understand how these factors influenced their behavior. The results demonstrated that bystanders with higher trait empathy were significantly more likely to offer compensation when the exclusion was perceived as inappropriate, highlighting the role of empathy in driving supportive actions. Conversely, bystanders with lower empathy showed less variation in their responses, regardless of the exclusion reason. These findings provide new insights into the interaction between personal traits and contextual factors in shaping bystander behavior during social exclusion. This study also offers practical implications for designing interventions aimed at reducing the negative effects of exclusion, promoting empathy, and creating a more inclusive and supportive social environment.
Trace Metal Elements (TMEs) in Groundwater Around the Former Industrial Gold Mine of Poura in Burkina Faso – West Africa
Authors: Yameogo Adama , Kagambega Nicolas , Bado Isso , Barry Abdoul-Azize
Published: October 2024

The objective of this study is to assess the quality of groundwater around the first industrial mine in Burkina Faso, whose activities have been closed since 1999. The diagnosis of pollution by trace metal elements of groundwater around the old Poura gold mine was carried out through the determination of the contents of Trace Metal Elements (TME) at MP-AES and the measurements of the physicochemical parameters in situ on 38 samples of borehole water. These data made it possible to calculate the pollution risk index, to establish correlations between the different physicochemical parameters on the one hand and with the trace metal elements on the other hand, from the Pearson matrix and to carry out a multivariate statistical analysis, in particular that in principal components (PCA), with a view to determining the origin of the polluting elements. The results obtained made it possible to identify four samples of groundwater with an acidic pH (GWF18 (6.44), GWF19 (6.39), GWF23 (6.1) and GWP01 (5.99)) and 34 samples of groundwater whose Cd, Al, Fe, As, Pb, or Hg contents are higher than the standards in force in Burkina Faso. The Pollution risk Index (PI) indicates that 35 groundwater samples are slightly polluted and 3 groundwater samples are very heavily polluted (GWP01 (PI = 456%), GWF06 (PI = 132.5%), GWF03 (PI = 105.1%)). The Pearson correlation matrix and the principal component analysis (PCA) show that the origin and mobilization of the trace metal elements involved (Al, As, Cd, Cr, Fe, Hg and Pb) are linked to natural mineralization and anthropogenic activities such as mining and agricultural activities, favored by the infiltration of water into the subsoil through geological structures such as fractures and faults.
The Invisible Social Class: Relational Equality and Extreme Social Exclusion
Authors: Floris Giacomo
Published: October 2024

In this article, I develop a novel relational egalitarian theory of social exclusion that explains how society fails to treat socially excluded individuals – such as people experiencing homelessness, individuals with substance use disorders and mental illness and sex workers – as equals. I argue that society places and keeps excluded individuals at the very bottom of the social status hierarchy by treating them as socially invisible, or by rendering them physically invisible, or both. The upshot, then, is that part of what is wrong with social exclusion is that it creates the invisible social class.
Are urban labour markets more dynamic? Vacancies and urban scaling
Authors: Rouwendal Harm-Jan , Rouwendal Jan
Published: October 2024

This paper shows that there is superlinear scaling of vacancies with employment size. That is, there are disproportionally more vacancies relative to employment in urban areas, not just for overall employment, but also for occupational and educational classes. Hence vacancies are more strongly concentrated than the jobs to which they refer. Moreover, we find that, compared to all employment, the concentration of labour demand increases with required skill levels. We show that the stronger growth of jobs in cities is unable to explain this finding and propose an alternative explanation based on vacancy chains in spatially related labour markets. The results suggest that on-the-job searchers have better possibilities in cities to improve their position. This helps explain the higher mobility of especially younger workers in cities and the superior quality of job-worker matches in large labour markets.
Psilocybin for major depressive disorder: An updated systematic review and meta-analysis of randomized clinical trials
Authors: Aghajanian Sepehr , Shafiee Arman , Parvizi Omran Samira , Rezaei Nejad Aida , Jafarabady Kyana , Kohandel Gargari Omid , Rajai Shahryar , Mohammadi Ida , Bahrami Babaheidari Touran , Bakhtiyari Mahmood
Published: October 2024

Background:

Due to the unsatisfactory therapeutic effects of current antidepressants, research has been launched into alternative treatment approaches, such as the administration of psychedelics. Psilocybin, a classic hallucinogen, has been shown to exert considerable positive influence on depression symptoms through its serotonergic and glutamatergic effects. This systematic review and meta-analysis aimed to evaluate the effectiveness of psilocybin in treating depression.

Methods:

A comprehensive search of Medline (via PubMed) and the Cochrane Library databases was conducted to identify relevant studies. Inclusion criteria were applied to select studies that investigated the therapeutic impact of psilocybin on depression. A mixed-effects multi-level model was used to estimate the overall effect size. Effectiveness over time was also investigated as a secondary analysis.

Results:

The results of the primary analysis revealed a large and clinically observable reduction (SMC: −1.24, 95%CI: −1.83 to −0.65, I2level2 = 11.39%, I2level3 = 77.67%) of depressive symptomatology in patients receiving psilocybin in addition to supportive therapy compared to baseline measurements. The decrease was also marked when compared to placebo ( p-value = 0.032). The results remained significant even when a secondary analysis assessed the effect in various time intervals since the administration of psilocybin.

Conclusion:

This systematic review and meta-analysis substantiate the claim that psilocybin is superior in treating depression compared to established psychotherapy alone used for treating depression. This finding warrants further studies with larger sample sizes and across a longer timeframe.
Time zones
Authors: Slobodian Quinn
Published: October 2024

In this response to the forum, I argue that if we think of zones not so much as spatial zones but as time zones, a new way of understanding the pixelated decentralization of politics comes into view. By explicit intention, the zone is designed to punch people out of a shared horizon or a space of capture in the quintessentially modern metrics of per capita growth or standard of living. While offering peculiar usable pasts often related to creative not to say anachronistic understandings of indigenous politics, the zone is also a better iconic scale for the end of history in the grand sense than the nation or even the world in its sloughing off the injunctions of mass politics and popular sovereignty.
Composing Time in a Secondary U.S. Classroom: (Not) Challenging Ideological Polarization through Straight and Queer Temporal Movements
Authors: Schey Ryan
Published: October 2024

Drawing on a larger year-long ethnography at a public, urban, comprehensive high school in the Midwestern United States, this article describes the texts students composed in a co-taught sophomore (grade 10) humanities course combining social studies and English language arts. Bringing together sociocultural perspectives on literacy and composition with queer theorizations of time, I argue for the utility of attending not only to time’s multi dimensionality but also its multi directionality. Doing so in writing instruction can help thaw binary polarization and foster more humanizing temporal and in turn ideological movements. To illustrate, I present an ethnographic case of students writing about the history of gendered clothing in 20th-century U.S. society. I examine how different temporal ideologies had consequences for students (not) reproducing antagonistic, polarized binaries with respect to oppressive values, in particular anti-LGBTQIA+ values as they intersect with class, race, and politics. Although my emphasis is how gender and sexuality intertwine with economics, race, and politics, this article suggests that attending to the multidimensionality and multidirectionality of time is a productive site for scholars and educators committed to praxes of justice in writing instruction.
Women’s Entrepreneurship for Poverty Alleviation: Impact of Mission Shakti in Odisha, India
Authors: Dash Kailash Chandra , Das Tushar Kanti
Published: October 2024

The study analyzes the impact of women’s entrepreneurial efforts on rural poverty (RP) reduction across two resource-scarce regions of Odisha, India. Its main objectives are creating creative home-based ventures (HBVs) and long-term RP solutions. The analysis assesses how home-based entrepreneurs’ sociocultural, economic and educational backgrounds affect the development of HBVs and the mitigation of RP. A women-led project in Odisha called Mission Shakti uses self-help groups and business ventures to empower women. The original data was gathered from 363 respondents in Odisha who were members of the Mission Shakti group. Structural equation modelling (SEM) was applied to investigate the correlations between the constructs, and AMOS (Analysis of Moment Structures 21.0) and SPSS (Statistical Package for the Social Sciences 20.0) were used for the analysis. The findings indicate that RP alleviation through Mission Shakti in Odisha and entrepreneurship have a statistically significant and favourable relationship. The additional findings of the study demonstrate that respondents’ sociocultural capabilities, rather than their educational and economic backgrounds, have a considerable and favourable influence on the expansion of their home-based enterprises and that the improvement of RP alleviation is more positively impacted by the qualitative growth of entrepreneurship than by the numeric growth. The study’s concentration on woman entrepreneurs in Mission Shakti’s rural Khordha and Angul areas is one of its limitations. It is possible to carry out comparable research in other Indian areas where the rural populace is significantly deprived. Policymakers in rural Odisha should consider the findings to address targeted RP. Thus, this report recommends that home-based businesses in rural Odisha should actively explore entrepreneurship to reduce poverty. Additionally, the study offers a helpful lesson to different parties involved in programs to reduce poverty in other developing nations. The integration of capacity theory and SEM in the setting of a rising economy such as Odisha, India, contributes to the body of knowledge in this research regarding the relationship linking entrepreneurship and the alleviation of RP.