bluuebunny/crossref_metadata_2025 huggingface dataset:
scLong: A Billion-Parameter Foundation Model for Capturing Long-Range Gene Context in Single-Cell Transcriptomics
Authors: Xie Pengtao , Bai Ding , Mo Shentong , Zhang Ruiyi , Luo Yingtao , Gao Jiahao , Yang Jeremy , Wu Qiuyang , Singh Digvijay , Rahmani Hamidreza , Amariuta Tiffany , Grotjahn Danielle , Zhong Sheng , Lewis Nathan , Wang Wei , Ideker Trey , Xing Eric
Published: December 2024

Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of cellular heterogeneity by providing gene expression data at single-cell resolution, uncovering insights into rare cell populations, cell-cell interactions, and gene regulation. Foundation models pretrained on large-scale scRNA-seq datasets have shown great promise in analyzing such data, but existing approaches are often limited to modeling a small subset of highly expressed genes and lack the integration of external gene-specific knowledge. To address these limitations, we present scLong, a billion-parameter foundation model pretrained on 48 million cells. scLong performs self-attention across the entire set of 28,000 genes in the human genome. This enables the model to capture long-range dependencies between all genes, including lowly expressed ones, which often play critical roles in cellular processes but are typically excluded by existing foundation models. Additionally, scLong integrates gene knowledge from the Gene Ontology using a graph convolutional network, enriching its contextual understanding of gene functions and relationships. In extensive evaluations, scLong surpasses both state-of-the-art scRNA-seq foundation models and task-specific models across diverse tasks, including predicting transcriptional responses to genetic and chemical perturbations, forecasting cancer drug responses, and inferring gene regulatory networks.
Surrogate model and machine learning approaches for thermal field reconstruction from weld pool contour: application to GTA welding
Authors: Boutaleb Zaid , Bendaoud Issam , Rouquette Sébastien , Soulié Fabien
Published: December 2024

Thermal cycles in arc welding are crucial as they determine the metallurgy, residual stresses and distortions of welded parts. Experimentally measuring the temperature everywhere in the welded parts is not possible. This can be achieved with a thermal simulation but finite element analysis requires long computational times especially for large parts. This study aimed to predict the thermal field using a data-driven approach using numerical and experimental data. First, thermal modeling is defined and arc heating is described with an equivalent heat source. The numerical design of experiments was conducted by varying the heat source parameters. Weld pool contour is extracted from each simulations for building a numerical dataset. The numerical dataset is used for training a surrogate model. The surrogate model is used for estimating the heat source parameters from the weld pool contour using an optimization technique. Then a K-Nearest Neighbors algorithm is used to predict the thermal field from the estimated heat source parameters. A significant reduction in computational time is obtained for predicting the thermal field from experimental weld pool contour. Numerical analysis showed that the predicted thermal field is fairly good in the solid than in the weld pool.
Landslide Susceptibility Zonation Based on Slope Units and Grid Units Using the Weight of Evidence and Frequency Ratio Methods in the Kulon Progo Mountains, Indonesia
Authors: Erzagian Egy , Fathani Teuku Faisal , Wilopo Wahyu
Published: December 2024

The Kulon Progo Mountains in the south of Java Island have complex geological conditions and are highly susceptible to landslides, resulting in significant material losses and threats to human safety. In recent years, the frequency of landslides in Kulon Progo has increased markedly, from 208 events in 2020 to 622 in 2022, with estimated material losses reaching 450 million USD in 2022. To reduce these risks, it is essential to identify landslide susceptibility zones. This study uses grid and slope units to perform landslide susceptibility zonation. The weight of evidence (WoE) and frequency ratio (FR) methods were applied to compare model accuracy. Nine landslide-controlling factors, including slope, aspect, elevation, slope material, lineament density, Normalized Difference Vegetation Index (NDVI), distance to rivers, distance to roads, and rainfall, were analyzed using spatial methods and landslide event data. The results show that slopes steeper than 40° and high lineament density (> 1.61 km/km²) are the most significant factors influencing landslide susceptibility in both units. Validation using the Area Under Curve (AUC) indicates that the slope unit-based WoE method provides better accuracy, with AUC values of 0.812 for the success rate and 0.827 for the prediction rate. The study produced four landslide susceptibility zonation maps, classifying areas from very low to high susceptibility. High susceptibility zones are concentrated in the northern part of the study area, especially on steep slopes, with some areas in the southern part.
The Heparinized Wet-Suction Technique Enhances Aspiration Quality and Quantity of EUS-FNA for Solid Pancreatic Lesions: A Retrospective Study
Authors: Shang Rumin , Han Xiangming , Zhu Meng , Xu Bo , Rong Fang , Lv Fei , Wu Changwei , Ding Xiangwu
Published: December 2024

Background
The optimal suction method for performing endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) on solid pancreatic lesions (SPLs) remains unclear. This study aims to compare the heparinized wet-suction technique (HWST) with the conventional dry-suction technique(DST) for EUS-FNA in SPLs.
Methods
We conducted a retrospective study on EUS-FNA procedures at a single medical center in China. A total of 105 patients were enrolled who received 3 passes of EUS-FNA with 19-G needles. Sample quantity (including specimen adequacy, maximum intact specimen (MIS) length, and macroscopic visible core (MVC) length), sample quality (including cytology and histology blood contamination), diagnostic accuracy, and adverse events were compared between DST and HWST.
Results
From January 2021 to April 2024, a total of 105 patients (DST group: n = 50; HWST group: n = 55) were analyzed in this study. The specimen adequacy score was significantly higher with HWST compared to DST in the first pass (P = 0.006), the second pass (P = 0.032), and the combined total of three passes (P = 0.010). HWST produced longer MIS and MVC lengths than DST in the first pass (MIS: P = 0.001; MVC: P < 0.001), the second pass (MIS: P = 0.001; MVC: P = 0.022), and the combined total of three passes (MIS: P = 0.001; MVC: P < 0.001). Compared with DST, HWST yielded a lower blood contamination score in the first pass (cytology: P = 0.002; histology: P = 0.005), the second pass (histology: P = 0.035), and the combined total of three passes (cytology: P = 0.035; histology: P = 0.036). In terms of diagnostic yield, HWST demonstrated a significantly superior histological accuracy in the first pass compared with DST (P = 0.036).
Conclusions
EUS-FNA with HWST led to significantly higher specimen quantity and quality, improved first-pass histological diagnostic accuracy, and comparable safety to DST. HWST may have a wide range of applications in clinical practice.
Coordination of flux-related leaf and root traits impacts forest community assembly
Authors: Ma Zeqing , Duan Mengcheng , Sack Lawren , Ma Chengen , Li Le , McCormack M , Lu Mingzhen , Guo Dali
Published: December 2024

Understanding the functions and coordination of plant traits is critical for predicting how diverse species respond to climate change. According to hydraulic and economic theories, leaves and roots—key organs for resource acquisition—are expected to function in coordination, such that species with faster resource utilization would possess leaf and root traits that facilitate rapid carbon, nutrient, and water uptake and fluxes. However, there has been limited evidence supporting leaf-root trait coordination and a role for that coordination on community structure. Here, we measured 13 leaf and root functional traits for 101 woody species from six tropical and subtropical forests, and assessed coordination and its association with community dominance. Hydraulic traits, such as leaf vein density and root vessel density, were coordinated between organs and showed compensation trade-offs between traits within organs, such as, leaf vein density and diameter. Economic traits relating to composition, such as nitrogen concentration, were coordinated between organs, whereas economic structural traits were decoupled, such as leaf mass per area and specific root length. Overall, hydraulic traits and economics traits were partially independent. The coordination of flux-related leaf and root traits was associated with ectomycorrhizal symbiosis and with dominance within the community. These findings indicate how trait organization within and across organs contributes to optimal whole plant function, with implications for performance in natural communities.
Anatomical and Functional Factors Influencing Recovery in Idiopathic Epiretinal Membrane after Surgery
Authors: Shih En-Jie , Peng Kai-Ling , Kung Ya-Hsin , Wu Tsung-Tien
Published: December 2024

Purpose
We aimed to explore the anatomical and functional factors influencing recovery outcomes in patients with idiopathic epiretinal membranes (iERM) after surgical intervention.
Methods
Participants diagnosed with iERM underwent pars plana vitrectomy with ERM and internal limiting membrane (ILM) peeling. ERM staging, preoperative and postoperative findings of retinal microstructures, and thickness changes using optic coherence tomography, and inner and outer retinal layer thickness changes preoperatively and postoperatively were analyzed.
Results
The study analyzed 85 eyes from 65 patients (mean age, 65.64 ± 6.19 years) with idiopathic ERM who underwent PPV and both ERM and ILM peeling without recurrence within one year. Male accounted for 44.71% (38/85). The mean preoperative vision was 0.47 [Snellen equivalent (SE), 68/200] ± 0.29 logMAR, and the mean final vision was 0.32 (SE, 96/200) ± 0.30 logMAR. Visual improvements were significant (p < 0.001, paired t-test). Preoperative lens status, preoperative vision, preoperative inner and outer segment junction (IS/OS) distortion, final lens status, and follow-up duration significantly correlated with final vision. Central foveal thickness, inner and outer retinal layer thickness decreased continuously until 12 months postoperatively in the pseudophakia group, whereas for those in the phakia group, the outer retinal layer thickness decreased only in the first 6 months.
Conclusions
Phakia, poor initial vision, IS/OS distortion, and short follow-up do worsen the final visual outcomes. The postoperative central foveal thickness, inner and outer retinal layers thickness showed the longest statistical improvement in pseudophakic eyes over 12 months.
Versatile, tunable method for NaNO2/HX (Cl or Br)–mediated radical dihalogenation or nitration of olefins
Authors: Wang Yaxin
Published: December 2024

We have developed a versatile, tunable method for NaNO2/HX (Cl or Br)–mediated radical nitration or vicinal dihalogenation of olefins. The method allowed us to synthesize high-added-value vicinal dihalides by using inexpensive, low-molecular-weight HCl or HBr as a chloride or bromide source and commercially available NaNO2 under irradiation with blue LEDs without the need for a photocatalyst or metal catalyst. In the absence of light, reactions of the olefins with NaNO2 and HCl gave nitration products in a controllable fashion. This regio- and chemoselective method was compatible with a diverse array of olefin substrates, showed good functional group tolerance, and was successfully used for late-stage dihalogenation and nitration of dozens of derivatives of natural products and active pharmaceutical ingredients. Mechanistic studies showed that in the dihalogenation reactions, a nitrosyl halide (X–N=O) generated in situ by reaction of NaNO2 and aqueous HX not only delivered a halogen radical upon photoirradiation to initiate olefin dihalogenation but also underwent halogen-atom transfer (XAT) process with a carbon radical intermediate to form the vicinal dihalogenation product. In the nitration reactions, a nitric oxide (NO or NO2) that was formed in situ from NaNO2 and the same equivalent of HCl selectively triggered olefin nitration.
Predicting the Neonatal Mortality Using Ensemble Machine Learning Algorithms in Case of Ethiopian Rural Areas
Authors: Mengstie Melaku , Telake Misganaw
Published: December 2024

Each year, approximately 2.5 million newborns die globally, with developing countries behavior the impact of this crisis. Sub-Saharan Africa experiences the highest neonatal mortality rate at 27 deaths per 1,000 live births. In Ethiopia, neonatal mortality remains alarmingly high at 29 deaths per 1,000 live births, with early neonatal mortality reaching 41.8 deaths per 1,000 live births. Rural areas face even more severe disparities, with a prevalence of 45.6 deaths per 1,000 live births compared to 25.5 in urban settings, basically due to inadequate healthcare access, poor maternal and neonatal services, and socioeconomic challenges.
This study aimed to develop a robust predictive model for neonatal mortality in rural Ethiopia, using secondary data from the Ethiopian Demographic and Health Surveys (2000–2019). The dataset, consisting of 29,048 instances and 22 relevant features, was preprocessed to handle missing values and balance the class distribution using SMOTE. Several advanced ensemble machine learning algorithms were applied to build the predictive model, including Random Forest, Gradient Boosting, Extreme Gradient Boosting, Light Gradient Boosting, and CatBoost. The performance of these models was evaluated based on key metrics, including accuracy, precision, recall, F1 score, and ROC-AUC.
Among the ensemble algorithms tested, CatBoost demonstrated the highest performance, achieving 97.5% accuracy, 97.52% precision, 97.5% recall, 97.5% F1 score, and an outstanding ROC-AUC value of 99.57%. The key risk factors for neonatal mortality identified in the study included BCG vaccination status, the number of under-five children in the household, recent episodes of diarrhea, and iron tablet intake during pregnancy. These factors were found to significantly contribute to predicting neonatal mortality, underscoring the importance of targeted healthcare interventions for high-risk neonates.
This study developed a predictive model for neonatal mortality in rural Ethiopia using ensemble machine learning, identifying key risk factors like BCG vaccination and maternal health. It offers actionable insights for targeted interventions, supports healthcare prioritization, and highlights the need for improved access and policy reforms. Mobile health apps and policymaker collaboration can further reduce neonatal mortality.
Developing the Predictive Model for the Level of Food Insecurity Status of Households Using Ensemble Machine Learning Techniques with XAI
Authors: Mengstie Melaku
Published: December 2024

Among the world’s undernourished people, more than 282 million people in Africa and 418 million people in Asia were found. Moderate or severe food insecurity at the global level has risen from 22.6% in 2014 to 26.6% in 2019, with Sub-Saharan Africa experiencing high levels, particularly in Ethiopia. In 2018, 239 million undernourished people were registered in Sub-Saharan Africa particularly high in Kenya, Somalia, Ethiopia, and South Sudan. This study, hence, aimed to develop a predictive model using ensemble machine learning algorithms to assess and classify the levels of food insecurity based on socioeconomic, environmental, and demographic factors in North Western Ethiopia. The dataset, collected from the Dabat Health and Demographic Surveillance from 2014 to 2020, was preprocessed using data cleaning, transformation, and class balancing with SMOTE-ENN. The dataset is split into training, validation, and testing sets with a 90/10 ratio. Relevant features were selected via recursive feature elimination, and class decomposition methods were evaluated. Experiments with ensemble models, including Random Forest, Gradient Boosting, XGBoost, CatBoost, and LightGBM, identified XGBoost as the best-performing algorithm, achieving 91.53% accuracy, 89.39% recall, and a 94.00% micro-average ROC score. SHAP was employed for explainable AI, offering insights into critical factors influencing food insecurity. Key factors that enhance food security include receiving assistance in the form of food or money, the size of cultivated land, altitude, monthly income, and household assets. In contrast, factors that increase the likelihood of food insecurity include the education level of the household head, age, marital status, limited access to irrigation, and reductions in portion sizes and meal frequency for the household head. This study demonstrates the utility of machine learning in identifying key drivers of food insecurity and offers a robust framework for targeted interventions. By bridging predictive analytics with explainable AI, actionable insights are provided to policymakers and stakeholders to alleviate food insecurity in Ethiopia and beyond.
Predictive Modeling and Optimization of TBM Operations: Advanced Techniques Applied to the Jakarta MRT Project
Authors: SalamM Chairul , Kural Orhan
Published: December 2024

The effectiveness of Earth Pressure Balance (EPB) Tunnel Boring Machines (TBMs) in urban underground construction relies on understanding and optimizing their performance under variable geotechnical conditions. This study investigates the key parameters impacting TBM efficiency during the construction of the Jakarta Mass Rapid Transit (MRT) Underground Section CP106. Data from TBM operation were analyzed using statistical and machine learning techniques, including Mutual Information (MI), Partial Dependence Plots (PDP), and Analysis of Variance (ANOVA), to identify influential parameters such as Tensile Strength, Uniaxial Strength, Spacing, and Penetration. Predictive models, including Gradient Boosting Regressor, Random Forest Regressor, and Linear Regression, were evaluated based on error metrics and R-squared values, with Gradient Boosting Regressor showing the highest predictive accuracy. Clustering analyses using K-Means and Principal Component Analysis (PCA) further classified operational states, identifying conditions that optimize energy efficiency and reduce mechanical wear. The findings suggest that TBM configurations with lower Specific Energy, Normal Force, and Rolling Force contribute to more efficient, less force-intensive tunneling. These insights provide a basis for refining TBM operations and predictive modeling in urban tunneling projects.
BBBW on the spindle
Authors: Amariti Antonio , Mancani Salvo , Morgante Davide , Petri Nicolò , Segati Alessia
Published: December 2024

We study the spindle compactification of families of AdS_55 consistent truncations corresponding to M5 branes wrapped on complex curves in Calabi-Yau three-folds. From the AdS/CFT correspondence these models are dual to \mathcal{N}=1𝒩=1 SCFTs obtained by gluing of T_NTN blocks. The truncations considered here have both vector and hyper multiplets and the analysis of the BPS equations on the spindle allows to extract the central charges. Such analysis gives also consistency conditions for the existence of the solutions. The solutions are then found both analytically and numerically for opportune choices of the charges for some sub-families of truncations. We then compare our results with the one expected from the field theory side, by integrating the anomaly polynomial.
On analysing time series of accidents and incidents at hazardous production facilities
Authors: Bochkov A. V. , Kirkin M. A.
Published: December 2024

Safe operation of any complex distributed systems is largely defined by the quality of the event analysis and prediction tools. Aside from the external hazards, the so-called hazardous production facilities (HPFs) pose significant threats. Accidents at such facilities are a subject of constant analysis and concern of operating organisations. At the same time, the accident statistics accumulated over the period of operation of such facilities are often heterogeneous. Accidents and incidents at HPFs occur at different times, against different forecast backgrounds, which complicates the construction and verification of digital models of such facilities. This paper proposes an algorithm for preprocessing time series of observations to extract data that can be subsequently used to build and train predictive models with the required accuracy. The proposed approach can be implemented by means of the R language, that in many respects has become a standard for statistical calculations.
História, memória e patrimônio italianos: Hospital Matarazzo de São Paulo
Authors: Maria Izilda Santos de Matos  , Thaís Teixeira Dias Brambilla
Published: December 2024

O Hospital Matarazzo (SP), importante remanescente da presença italiana em São Paulo, teve seus edifícios tombados pelo Conselho de Defesa do Patrimônio Histórico, Arqueológico, Artístico e Turístico do Estado de São Paulo (CONDEPHAAT) em 1986, num momento em que enfrentava grave crise que levou ao encerramento de suas atividades (1993). O conjunto construtivo foi vendido (1996) e ficou abandonado por décadas, até ser comprado pelo grupo Allard (2011), que, após articulações, conseguiu a revisão do tombamento e construiu um complexo de alto luxo denominado Cidade Matarazzo, composto de hotel, escritórios, shopping center, centro cultural e gastronômico. Essas reflexões geram questionamentos sobre por que nesse momento celebrativo dos 150 anos da imigração italiana, numa cidade como São Paulo, na qual a comunidade italiana foi/é tão expressiva, ocorre tal descaso, descuido e destruição para com o patrimônio histórico, que sucumbe ante os interesses do capital imobiliário. Assim, estes escritos buscam discutir a presença italiana na cidade, as suas práticas associativistas, a constituição das associações beneficentes da comunidade italiana em São Paulo, a criação do Hospital Matarazzo, sua crise e fechamento e a destruição do patrimônio.
A New-fangled Classification Algorithm for Medical Heart Diseases Analysis using Wavelet Transforms
Authors: Nayak Soumya Ranjan , Selvarasu Sivakumar , Sripathy B , Singh Prabhishek , Diwakar Manoj , Gupta Indrajeet , Ravi Vinayakumar , Al Mazroa Alanoud
Published: December 2024

Background

In this article, the Mixed Mode Database Miner (MMDBM) algorithm is introduced for the classification of data. This algorithm depends on the decision tree classifier, which handles the numerical and categorical attributes. For the experimental analysis in a well-explored heart disease data set collected from the UCI Repository.

Aims

Understanding the fundamentals of every classification method and how to apply them to CUDA is the aim of this study. After reviewing the literature, the approach that is most suited is selected for implementing the suggested algorithm in the MMDM classifier along with the discrete wavelet decomposition. From this experimental analysis, we observed that the use of the wavelet technique in the MMDBM algorithm provides better and more accurate results for data classification.

Objective

The main objective of the manuscript is to identify the early stage of heart attack that was caused either by smoking, smoking with tobacco, or non-smoking. Additionally, this study aims to check the validity of the MMDM classifier along with the discrete wavelet decomposition. From this experimental analysis, we observed that the use of the wavelet technique in the MMDBM algorithm provides better and more accurate results for data classification.

Methods

In the modern digital world today, data has a major impact on everyone’s life. Every database contains a lot of information hidden either in the form of numerical data, characteristic data, or a mixture of both. Moreover, to accurately decode every dataset, a fast and efficient classifier is essential.. Moreover, by using this hybrid technique based on both MMDBM and Wavelet processes, it compresses the data with minimum storage capacity.

Results

The experimental results are compared with the classified data to wavelet data output. These results prove that our presented technique is more prominent and robust in an analysis of heart diseases.

Conclusion

This algorithm is based on a decision tree classifier and tested on a heart disease database. MMDBM is applied to classify a large data set of numerical and categorical attributes. The data are compressed with the help of a one-dimensional wavelet transform. This is one of the new approaches applied to classified data in real-time applications.
The Association of Self-efficacy with Self-care Behaviors, Anxiety, and Academic Motivation in Iranian Medical Sciences Students
Authors: Amiri Mohammad , Sadeghi Elham , Parvane Ayda , Khosravi Ahmad
Published: December 2024

Background and Objective

Medical students are most susceptible to contracting stressful clinical phenomena and experiencing subsequent psychological consequences. The study aimed to investigate the association between self-efficacy, self-care, and anxiety with academic motivation among students enrolled at Shahroud University of Medical Sciences.

Materials and Methods

This cross-sectional study, conducted in 2022 immediately after the university opening during the COVID-19 pandemic,to investigate anxiety and self-care behaviors related to COVID-19, academic self-efficacy, and motivation among 339 students from diverse medical science disciplines. A multi-stage random sampling technique was employed to choose the participants. The data underwent analysis using one-way ANOVA, Chi-square test, and linear regression model. The significance level for all tests was 0.05.

Results

The mean score of students' self-care behavior was 56.5±13.7, the mean academic self-efficacy was 106.5±17.6, and the mean academic motivation score was 98.5±13.0. The mean score of students' anxiety about COVID-19 was 6.9±6.9, suggesting a low level of anxiety. A total of 70 participants (20.6%) demonstrated good self-care behaviors against COVID-19, 316 (93.2%) reported reduced anxiety related to COVID-19, and 154 (45.4%) reported high levels of academic motivation. Only the self-care behaviors and academic motivation variables in the faculty and COVID-19 setting were shown to be substantially correlated with the sense of academic self-efficacy in the final model.

Conclusion

Medical students have been found to have significantly lower motivation and academic self-efficacy than students in other fields of study, so the need for identification of causes and implementation of intervention measures has been deemed necessary.
Analysis of Oral and Gut Microbiota Composition in Children with Dental Caries by NGS Approaches
Authors: Cianetti Stefano , Marchianò Silvia , Wijeratne Dhanushka Frank , Carino Adriana , Biagioli Michele , Bordoni Martina , Roselli Rosalinda , Di Giorgio Cristina , Bellini Rachele , Valenti Chiara , Coniglio Maddalena , Lomurno Giuseppe , Lomurno Anna Palma , Pagano Stefano
Published: December 2024

Objectives:

This study aimed to characterize oral and gut microbiota of children with
high dmft index and caries-free children at phylum, family and species levels as well as to evaluate
the effect of Streptococcus salivarius M18 DSM 14685 (Carioblis) administration on microbiota
composition of caries active children.

Materials and Methods:

Ten children with active caries and nine caries-free children have been recruited.
Four samples from different oral niches and stools were collected from each patient for
the NGS sequencing of 16s Microbiota rDNA by S5 Ion Torrent.

Results:

Our results revealed modifications in the microbiota composition of teeth, saliva and
vestibular regions of the oral cavity and faecal samples in the presence of dental caries. These
changes were evident at the family and species levels, with no significant differences found at the
phylum composition level. In particular, Streptococcaceae were positively correlated to the high
degree of caries in all niches, and the analysis at the species level led to the identification of 39
bacterial species significantly modulated in the analyzed groups. The use of probiotic seemed to
exert beneficial effects on oral but not on faeces dysbiosis. The intestinal tract was confirmed to
have a different microbiota composition compared to the oral cavity.

Conclusions:

Dental caries mainly lead to modifications in the oral microbiota composition. Streptococcus
salivarius M18 DSM 14685 administration determines a shift in the oral microbiota composition
towards a healthier state. Concerning the gastrointestinal tract, our study found for the
first time that caries cause the increase of two bacterial species, related to other disorders: Bifidobacterium
adolescentis and Ruminococcus torques.
Desafios Sociais e Éticos da Inteligência Artificial no Século XXI
Authors: Machado Helena Cristina , Silva Susana
Published: December 2024

Este livro nasce da necessidade de disponibilizar, numa linguagem acessível, uma análise científica e pedagógica sobre Inteligência Artificial (IA) que fomente a capacidade crítica e reflexiva. Com um olhar atento aos principais desafios sociais e éticos da IA no século XXI — sobretudo na educação, saúde e justiça —, esta obra explora questões de poder e elementos políticos e culturais que subjazem às narrativas dominantes sobre a IA. Ao desvendar estas dimensões e projetar cenários futuros, queremos abrir espaço para as visões, necessidades e expectativas de diferentes grupos sociais, promovendo uma IA que beneficie toda a sociedade. Este livro convida-nos a refletir sobre como a IA pode gerar vantagens para alguns, mas também impactos negativos para os mais vulneráveis, exigindo uma reflexão coletiva e antecipatória. Procuramos alargar o diálogo público sobre a IA, questionando: Quem define o que é “bom” para a sociedade? Que valores sociais devem prevalecer? Como projetar e aplicar a IA de forma a garantir o bem-estar comum? Um convite para imaginar uma IA mais inclusiva e responsável.
Desain Sistem Kadaster Multiguna Basisdata Pendaftaran Tanah Sistematik Lengkap
Authors: Arnanto Ardhi , Suharno Suharno , Aminah Siti Nur , Puspitasari Soraya Rizky
Published: December 2024

Salah satu program prioritas yang diamanahkan Presiden Jokowi adalah menuntaskan permasalahan pertanahan agar dapat memberikan keadilan dan meningkatkan perekonomian masyarakat. Program Pendaftaran Tanah Sistematis Lengkap merupakan tindaklanjut dari program prioritas Presiden Jokowi yang bertujuan untuk mempercepat pendaftaran tanah di Indonesia. Hasil dari program PTSL ini diharapkan menjadi basisdata bidang tanah yang valid dan dapat dimanfaatkan untuk berbagai kepentingan. Penelitian ini mengkaji pemanfaatan basisdata hasil dari program PTSL untuk desain sistem kadaster multiguna. Metode penelitian yang digunakan adalah research dan development (RD), yaitu metode yang menghasilkan suatu produk dan menguji keefektifannya. Tujuan dari penelitian ini adalah untuk mengetahui struktur basis data PTSL yang dapat digunakan untuk mengembangkan sistem informasi kadaster multiguna dan melakukan desain sistem informasi kadaster multiguna yang memenuhi kebutuhan stake holder terkait. Hasil penelitian ini dapat menyimpulkan bahwa masih diperlukan penyesuaian basisdata karena perbedaan basis pemetaan dan penggunaan identitas tunggal NIB yang digunakan sebagai primary key untuk merelasikan antar basisdata dalam sistem kadaster multiguna.
Pemodelan kesesuaian habitat macan dahan kalimantan (Neofelis diardi borneensis) di Taman Nasional Kayan Mentarang
Authors: Gumilar Jefry Frihardian , Husainar Husien Dwi , Nababan Josua
Published: December 2024

Macan dahan kalimantan (Neofelis diardi borneensis Wilting, Christiansen, Kitchener, Kemp, Ambu and Fickel, 2007) merupakan satwa prioritas di Taman Nasional Kayan Mentarang (TNKM) dan predator puncak di Kalimantan. Data dan informasi tentang habitat macan dahan kalimantan di alam masih sulit untuk dipahami. Penelitian ini bertujuan untuk membuat pemodelan spasial kesesuaian habitat macan dahan kalimantan di TNKM. Penelitian ini dilakukan dengan metode pemodelan spasial dengan Maximum Entropy (MaxEnt). Variabel yang digunakan diantaranya adalah variabel ketinggian, kelerengan, jarak dari lahan pertanian, jarak dari lahan terbuka, jarak dari savana, dan jarak dari sungai. Hasil pemodelan menunjukkan  wilayah TNKM yang sesuai untuk habitat macan dahan kalimantan adalah seluas 514.327,15 ha. Hasil uji jackknife menunjukkan variabel dengan kontribusi tertinggi adalah variabel ketinggian dan variabel yang memiliki pengaruh tertinggi adalah variabel jarak dari lahan pertanian, ketinggian dan jarak dari savana. Evaluasi kinerja model menunjukkan nilai AUC sebesar 0,872 dan standar deviasi sebesar 0,038 dengan kategori baik.
Challenges of the first six months COVID-19 among students : an Experience at Yogyakarta State University
Authors: Prasastiawati Diana , Andhika Muhammad , Sumunar Dyah Respati Suryo
Published: December 2024

Students and residents who live in residential areas around the campus are one of the groups vulnerable to the spread of COVID-19. This study aims to analyze the level of anticipation of vulnerability and capacity in  Yogyakarta in facing the threat of COVID-19 This research is categorized as quantitative research, with a research sample of 395 students of Yogyakarta State University. The survey method through questionnaires was used to collect primary data in the form of community vulnerability and student capacity. The research sampling technique in this research is using purposive sampling technique. The validity used is content validity and construct validity. The results of the reliability analysis of this study are 0.95 for Likert scale items (Cronbach Alpha), 0.708 for Guttman scale items (KR-20), and 0.575 for multiple choice items (Split-Half Coefficient). The results showed that in general the score of anticipating the vulnerability of Universitas Negeri Yogyakarta to the threat of COVID-19 reached 69.98% (good category). The level of capacity of students in dealing with the threat of COVID-19 is also classified as good (90.76%).
Exploring the legend and the myth in ocean and maritime life: A systematic literature review
Authors: Olivia Raisya Arzety , Ashari Arif , Ervin Muhamad , Hadi Bambang Syaeful , Brotowiranti Rayung Wulan , Reswari Az-Zahra Ardhana , Yuangwidi Kinanthi Purbaning , Salma Greace Nanda Putri , Fitriana Kamlia Nur , Abdullah Nurul Hakimah Che , Zekeri Fatihin Nabiha Mohd , Razali Muhammad Syafiq
Published: December 2024

Oceans cover about two-thirds of the Earth's surface and hold no less than 97% of the Earth's water. With this significant position, many mysteries still have yet to be revealed by various explorations and studies on the oceans. Here, we explore the oceans' and maritime world's legends and myths worldwide. This paper is a systematic literature review (SLR) organized using the PRISMA approach. Search keywords were determined using the PCC method. The documents reviewed were obtained from the Scopus database. A total of 436 documents were obtained, and 70 met the criteria based on selection in the extraction process and continued in the review stage. The findings of this study show that ocean and maritime myths are not always related to metaphysical myths. Some myths are philosophies of community life and semiotics. Myths related to metaphysics usually form local wisdom in the community. In summary, this study offers new insights into myths in the oceans, a topic that is still relatively rare in previous literature.
Energy Self-Sufficiency in Rural Areas; Case Study: North Euboea, Greece
Authors: Sargentis G.-Fivos , Ioannidis Romanos , Dimitriadis Panayiotis , Malamos Nikolaos , Lyra Olga , Kitsou Olga , Kougkia Matina , Mamassis Nikos , Koutsoyiannis Demetris
Published: December 2024

The modern globalized civilization is sustained by interactions, trade, the transportation of goods, and energy. Energy self-sufficiency is crucial in rural and disaster-prone areas like North Euboea because dependence on external energy supplies can leave regions vulnerable to supply chain disruptions, price volatility, and geopolitical risks. In such isolated regions, energy independence ensures resilience in natural disasters and economic instability. The alternative to self-sufficiency often involves reliance on centralized energy systems, fossil fuels, or external imports, which may not always be reliable or sustainable. Therefore, in this study, we explore the possibility of an area having energy self-sufficiency. As a case study, we chose North Euboea in Greece, explicitly focusing on the Municipality of Mantoudi-Limni-Agia Anna. The analysis combines local land use patterns, energy needs for inhabitants, agriculture and water requirements. It is followed by exploring various renewable energy sources, including hydropower, biomass, solar, and wind. We considered the stochastic nature of renewable energy production and the challenges associated with energy storage. The findings suggest that while wind turbines and solar panels could be installed in the area and contribute significantly to energy needs, achieving complete self-sufficiency requires careful planning, particularly regarding energy storage and the social acceptance of these installations. The results highlight the need for a holistic approach that integrates environmental, landscape, societal, and technical considerations in designing and implementing renewable energy systems. Additionally, it is highlighted that the available renewable energy from forest biomass (before the 2021 megafire event) could reliably and adequately meet the area's energy needs without requiring investments in photovoltaic parks and wind turbines and without competing with the use of agricultural land.
Knowledge of elderly cadres on dementia in Yogyakarta, Indonesia
Authors: Mulyani Sri , Cahyani Kadek Dewi , Listyaningrum Desy , Ulayya Aisyah Iffah
Published: December 2024

Introduction: Dementia can cause disability, in addition to physical, mental, and financial stress for caregivers, and affect the nation’s healthcare system and economy. Efforts are needed to improve the quality of life of people with dementia involving various sectors, especially elderly cadres. Elderly cadres are volunteers who provide screening and health education for elderly persons at the community level. Accordingly, it is crucial to identify the knowledge levels about dementia in elderly cadres to improve public awareness and interventions concerning this topic. This study aimed to determine the level of knowledge of elderly cadres about dementia. Methods: This quantitative study was conducted with a cross-sectional design. There were 103 elderly cadres recruited using convenience sampling from November to December 2022. A demographic questionnaire and Dementia Knowledge Assessment Scale (DKAS) were used to collect the data. The data were analyzed using Spearman’s rho and Mann Whitney with significance set as p < 0.05. Results: The median score of the elderly cadres’ knowledge score was 22 (minimum-maximum = 0-44). Elderly cadres’ knowledge was related significantly to training about dementia (p = .002). There was no significant correlation between elderly cadres' knowledge and age (p = .271), gender (p = .073), and education level (p = .106). Conclusions: In general, the knowledge of elderly cadres about dementia is relatively low. However, there was a significant change in knowledge scores after dementia training, so a program to increase understanding about dementia through training is recommended for elderly cadres.
The critical role of informed consent for doctor and patient in the community
Authors: Yuliana Yuliana
Published: December 2024

Malpractice and disputes in medical cases are commonly found nowadays in the community. It was caused by misperception between patients and doctors on a medical case. Therefore, the informed consent role is essential for patients and doctors. This paper aims to describe the role of informed consent for patients and doctors. This is a narrative literature review. Literature was taken from PubMed, Google Scholar, and Science Direct. The selected journals were published within 10 years. Informed consent is a sign of approval for any medical procedure that might cause disputes. On the doctors’ side, informed consent is a legal protection. On the other side, informed consent is an explanation for the patient in helping decide on approval or rejection of any medical procedure. An informed consent paper has legal protection for the attending physician. Doctors have the obligation of giving true and complete information about the procedure before it is done. Patients have the right to have an explanation, give consent, or rejection regarding any procedure. When a doctor does a medical procedure before giving any informed consent, it is considered malpractice. Informed consent is related to the human rights fulfilment. Patients have the right to accept or reject any medical procedure after the doctors explain thoroughly about the procedure. In conclusion, the role of informed consent for doctors is as legal protection and obligation, meanwhile, the role of informed consent for the patients is as a fulfilment of human rights.
The Effect of College Student’ Employment Anxiety on Academic Involution: The Chain Mediating Role of Academic Stress and Future-oriented Coping
Authors: Jia Xinyu
Published: December 2024

Under the background of China’s social and economic transformation and development, the imbalance between supply and demand in the job market and the prominent phenomenon of employment anxiety among college students, coupled with the rapid development of higher education, more and more students are devoting their energy to academics in order to gain more advantages in the job market, which leads to the serious phenomenon of academic involution. Using random sampling, 413 college students were selected for the questionnaire survey. By constructing structural equation modeling, Bootstrap method was used to test the chain mediating role of academic stress and future orientation coping between employment anxiety and academic involution of college students. The results showed that employment anxiety and academic stress differed significantly across genders, with females scoring significantly higher than males; employment anxiety positively predicted academic involution, academic stress and future-oriented coping mediated the chain between college students’ employment anxiety and academic involution, with a mediation effect accounting for 52.8% of the total. The results of the study revealed that it is necessary to implement the employment support policy for college students, solve the problem based on the root cause, help college students establish the awareness of future-oriented coping, avoid the employment risk in advance, adjust the level of their academic pressure, and develop together in a benign competition.
СИСТЕМЫ КОМПЬЮТЕРНОГО ЗРЕНИЯ НА ПРОМЫШЛЕННЫХ ПРЕДПРИЯТИЯХ
Authors: Шупов А.
Published: December 2024

Системы компьютерного зрения сегодня применяются повсеместно:
в промышленности, сельском хозяйстве, оборонной отрасли, авиации,
на транспорте и в других областях. На промышленном предприятии
системы компьютерного зрения помогают исключить человеческий
фактор при работе с продукцией, сократить издержки, повысить
гибкость производства, обеспечить безопасность на объекте, следить
за соблюдением техники безопасности. Система компьютерного зрения
«Сокол» построена на основе алгоритмов машинного обучения
и глубокого анализа данных с возможностью автоматического дообучения
под конкретную задачу в процессе работы. Это позволяет эффективно
анализировать видеоинформацию на различных производственных
участках, отслеживать производственные процессы, перемещение
сотрудников и их взаимодействие с различными объектами.
СОВРЕМЕННЫЕ ТЕНДЕНЦИИ КОРПУСИРОВАНИЯ БЕЗВЫВОДНЫХ МЕТАЛЛОПОЛИМЕРНЫХ КОРПУСОВ
Authors: Абашин Е. , Алехин С. , Гаврилин А. , Данцев О.
Published: December 2024

Современным направлением в развитии микроэлектроники является
значительное увеличение производства интегральных микросхем
и дискретных полупроводниковых приборов в малогабаритных
металлополимерных корпусах. Корпусирование в металлополимерные
корпуса является дополнительным видом конструктивного исполнения
наряду с изделиями, закорпусированными в металлокерамические
и металлостеклянные корпуса.
ВЫСОКОСКОРОСТНЫЕ ДИФФЕРЕНЦИАЛЬНЫЕ ПАРЫ НА ПЕЧАТНОЙ ПЛАТЕ ДЛЯ МОДУЛЬНОГО СОЕДИНИТЕЛЯ
Authors: Шаломанов В. , Баканин Д.
Published: December 2024

Компания «Радиант-ЭК» в рамках импортозамещения осваивает
номенклатуру модульного высокоскоростного соединителя,
соответствующего международному стандарту VPX, с монтажом
на плату методом запрессовки по технологии Press Fit. Рассмотрено
моделирование печатной платы с дифференциальными парами
для оценки скорости передачи данных.
ПРИМЕНЕНИЕ РОССИЙСКОЙ ЭКБ И МЕРЫ ПО КАДРОВОМУ ОБЕСПЕЧЕНИЮ ОТРАСЛИ. НЕКОТОРЫЕ МЕРОПРИЯТИЯ ДЕЛОВОЙ ПРОГРАММЫ ВЫСТАВКИ EXPOELECTRONICA 2024
Authors: Ковалевский Ю.
Published: December 2024

С 16 по 18 апреля в Москве прошла 26-я Международная выставка
электроники ExpoElectronica. В этом году общая площадь выставки
превысила 30 000 м2, число ее экспонентов составило 760 компаний,
среди которых более 400 из России, Беларуси и Кыргызстана и 346 –
из Китая и ОАЭ, а посетили выставку 22 473 специалиста из 22 стран
и 77 регионов России.
В рамках деловой программы было проведено более 40 мероприятий,
основные из которых проходили на трех площадках: Электроника
Арена, Цифровая Арена и Робо Арена. Также деловая программа
включала технические семинары и мероприятия на стендах
участников. В данной статье рассказывается о двух мероприятиях
деловой программы выставки, на которых обсуждались такие
актуальные для отрасли вопросы, как выстраивание отношений
между отечественными производителями и потребителями
электронной компонентной базы (ЭКБ) и обеспечение
электронной промышленности кадрами.
ЗАРУБЕЖНЫЕ И ОТЕЧЕСТВЕННЫЕ МНОГОСЛОЙНЫЕ КЕРАМИЧЕСКИЕ КОНДЕНСАТОРЫ: ТЕХНОЛОГИИ, КАТЕГОРИИ КАЧЕСТВА
Authors: Савицкий В. , Раскин А.
Published: December 2024

Многослойные керамические конденсаторы (МКК) применяются
практически в любой современной радиоэлектронной аппаратуре (РЭА)
и являются самым массовым изделием из числа электронных
компонентов. МКК в мире выпускают несколько сотен компаний,
среди которых крупнейшими являются Murata, Samsung, TDK,
Taiyo Yuden, Yageo, Walsin. Из общего количества МКК, выпускаемых
в мире, 40–50 % производится на территории Китая. Чтобы сократить
отставание от зарубежных компаний, российским производителям МКК
нужно решить целый ряд технологических и организационных задач.
В статье рассмотрены конструкция, технологии производства и категории
качества зарубежных и отечественных МКК. Обсуждаются вопросы
совершенствования характеристик МКК, а также задачи, стоящие
перед российскими производителями конденсаторов.
ВЛИЯНИЕ СТРУКТУРЫ И СВОЙСТВ ТОНКИХ ПЛЕНОК ОСМИЯ НА ЭМИССИОННУЮ СПОСОБНОСТЬ И ДОЛГОВЕЧНОСТЬ КАТОДА
Authors: Соляник В.А. , Мирошниченко А.Ю.
Published: December 2024

Металлопористые катоды нашли широкое применение в электровакуумных приборах О-типа. В статье рассмотрен обзор работ, посвященных изучению влияния основных элементов металлопористых катодов на их эмиссионную способность и долговечность. Особое внимание уделено современным методам исследования структуры и свойств пленки осмия, которую наносят на поверхность катода. Описаны необходимые направления дальнейших разработок.