bluuebunny/crossref_metadata_2025 huggingface dataset:
Named entity recognition on Indonesian legal documents: a dataset and study using transformer-based models
Authors: Yulianti Evi , Bhary Naradhipa , Abdurrohman Jafar , Dwitilas Fariz Wahyuzan , Nuranti Eka Qadri , Husin Husna Sarirah
Published: August 2024

The large volume of court decision documents in Indonesia poses a challenge for researchers to assist legal practitioners in extracting useful information from the documents. This information can also benefit the general public by improving legal transparency, law enforcement, and people's understanding of the law implementation in Indonesia. A natural language processing task that extracts important information from a document is called named entity recognition (NER). In this study, the NER task is applied to legal domains, which is then referred to as legal entity recognition (LER) task. In this task, some important legal entities, such as judges, prosecutors, and advocates, are extracted from the decision documents. A new Indonesian LER dataset is built, called IndoLER data, consisting of approximately 1K decision documents with 20 types of fine-grained legal entities. Then, the transformer-based models, such as multilingual bidirectional encoder representations from transformers (BERT) or M-BERT, Indonesian BERT or IndoBERT, Indonesian robustly optimized BERT pretraining approach (RoBERTa) or IndoRoBERTa, XLM (cross lingual language model)-RoBERTa or XLMR, are proposed to solve the Indonesian LER task using this dataset. Our experimental results show that the RoBERTa-based models, such as XLM-R and IndoRoBERTa, can outperform the state-of-the-art deep-learning baselines using BiLSTM (bidirectional long short-term memory) and BiLSTM-conditional random field (BiLSTM-CRF) approaches by 7.2% to 7.9% and 2.1% to 2.6%, respectively. XLM-RoBERTa is shown to be the best-performing model, achieving the F1-score of 0.9295.
Predicting television programs success using machine learning techniques
Authors: Fayq Khalid El , Tkatek Said , Idouglid Lahcen
Published: August 2024

In the ever-evolving media landscape, television (TV) remains a coveted platform, compelling industry players to innovate amid intense competition. This study focuses on leveraging machine learning regression models to precisely predict TV program reach. Our objective is to assess the models' efficacy, revealing a standout performer with a mean absolute percent error of just under 8%. Significantly, we identify features exerting a substantial impact on predictions and explore the potential for model enhancement through expanded datasets. This research extends beyond statistical insights, offering actionable implications for TV channel managers. Empowered by these findings, managers can make informed decisions in program planning and scheduling, optimizing viewer engagement. The temporal analysis of evolving trends over time adds a nuanced layer to our study, aligning it with the dynamic nature of the media landscape. As television retains its dynamic force, our insights contribute not only to academic discourse but also provide practical guidance, enhancing the competitive edge of television channels.
A framework for 3D radiotherapy dose prediction using the deep learning approach
Authors: Hien Lam Thanh , Toan Ha Manh , Toan Do Nang
Published: August 2024

Cancer is known as a dangerous disease to humans with a very high death rate. There are a lot of cancer treatment methods that have been studied and applied in the world. One of the main methods is using radiation beams to kill cancer cells. This method, also known as radiotherapy, requires experts having a high level of skill and experience. Our work focuses on the 3D dose prediction problem in radiotherapy by proposing a framework aiming to create a medical intelligent system for this problem. To do that, we created a convolutional neural network based on ResNet and U-Net to generate the predicted radiation dose. To improve the quality of the training phase, we also applied some data processing techniques based on the characteristics of the 3D computed tomography (CT) data. The experiment used the dataset from patients who were cancer-treated with radiotherapy in the OpenKBP competition. The results achieved good evaluating metrics, the first is by the Dose-score and the second is by the dose-volume histogram (DVH) score. From the training result, we built the medical system supporting 3D dose prediction and visualizing the result as slices in heatmap form.
Forecasting creditworthiness in credit scoring using machine learning methods
Authors: Mukhanova Ayagoz , Baitemirov Madiyar , Amirov Azamat , Tassuov Bolat , Makhatova Valentina , Kaipova Assemgul , Makhazhanova Ulzhan , Ospanova Tleugaisha
Published: August 2024

This article provides an overview of modern machine learning methods in the context of their active use in credit scoring, with particular attention to the following algorithms: light gradient boosting machine (LGBM) classifier, logistic regression (LR), linear discriminant analysis (LDA), decision tree (DT) classifier, gradient boosting classifier and extreme gradient boosting (XGB) classifier. Each of the methods mentioned is subject to careful analysis to evaluate their applicability and effectiveness in predicting credit risk. The article examines the advantages and limitations of each method, identifying their impact on the accuracy and reliability of borrower creditworthiness assessments. Current trends in machine learning and credit scoring are also covered, warning of challenges and discussing prospects. The analysis highlights the significant contributions of methods such as LGBM classifier, LR, LDA, DT classifier, gradient boosting classifier and XGB classifier to the development of modern credit scoring practices, highlighting their potential for improving the accuracy and reliability of borrower creditworthiness forecasts in the financial services industry. Additionally, the article discusses the importance of careful selection of machine learning models and the need to continually update methodology in light of the rapidly changing nature of the financial market.
Empowering E-learning through blockchain: an inclusive and affordable tutoring solution
Authors: Lgarch Saadia , Hnida Meriem , Retbi Asmaa
Published: August 2024

This study presents an innovative approach using the Ethereum blockchain to democratize access to tutoring services, advancing educational technology by bridging the affordability gap for learners with limited financial resources. This solution enables low-income learners to access tutoring services without significant expenses by eliminating intermediaries through smart contracts. Learners can directly book tutoring services based on fees and evaluations, ensuring a fair and accessible experience. The findings show that this approach reduces tutoring expenses and improves trust and accountability through transparent transactions and feedback mechanisms. The proposed system demonstrates how blockchain technology can foster a more equitable and efficient educational landscape, offering personalized
Forecasting stock market prices using deep learning methods
Authors: Ismailova Aisulu , Beldeubayeva Zhanar , Kadirkulov Kuanysh , Doumcharieva Zhanagul , Konyrkhanova Assem , Ussipbekova Dinara , Aripbayeva Ainura , Yesmukhanova Dariga
Published: August 2024

The article focuses on enhancing stock market price prediction through artificial neural networks and machine learning. It underscores the significance of improving forecast accuracy by incorporating historical stock prices, macroeconomic indicators, news events, and technical indicators. Exploring deep learning principles, it delves into convolutional neural networks (CNN), recurrent neural networks (RNN), including long short-term memory (LSTM), and gated recurrent unit (GRU) modifications. This financial time series processing study covers data preprocessing, creating training/test sets, and selecting evaluation metrics. Results suggest promising applications for the developed forecasting models in stock markets, stressing the importance of considering various factors for precise forecasts in dynamic financial environments. Historical reserve data serves as the model foundation. Integration of macroeconomic, news, and technical indicators offers a holistic approach, aiding trend and anomaly identification for enhanced forecasts. The article recommends suitable deep learning architectures, highlighting LSTM and GRU's effectiveness in adapting to intricate data dependencies. Experimental outcomes showcase these architectures' benefits in predicting stock market prices, offering valuable insights for finance and asset management professionals in financial analysis and machine learning realms.
Deep HybridNet with hybrid optimization for enhanced medicinal plant identification and classification
Authors: Renukaradhya Sapna , Narayanappa Sheshappa S.
Published: August 2024

Herbal leaves, known for their efficacy in treating a range of infectious diseases including cancer, asthma, and heart conditions, are still widely used by medical professionals. Traditionally, villagers have identified these plants visually, but given the similarity in appearance among various species, this method is prone to human error. Accurate identification of these plant species is critical for effective treatment. Hence, the development of an intelligent plant classification system is crucial to reduce the risk of misidentification and enhance treatment accuracy. This paper introduces the deep HybridNet with hybrid optimization module (DeepHybrid-OptNet) a novel deep learning framework for medicinal plant identification and classification. Merging convolutional and recurrent neural network architectures, deep HybridNet excels in extracting complex botanical features through channel-wise feature extraction modules in convolutional neural network (CNN) and feedback loop in recurrent neural network (RNN). The incorporation of a DeepHybrid-OptNet module enhances the model's learning efficiency and accuracy. Empirical results on the Mendley and folio dataset demonstrate the framework's superiority over existing methods in accuracy, precision, and recall making it a valuable asset for botany and herbal medicine research.
An enhanced Giza Pyramids construction for solving optimization problems
Authors: Omar Asmaa Hekal , Mostafa Naglaa M. , Desuky Abeer S. , Bakrawy Lamiaa M. El
Published: August 2024

Many real-world optimization problems can be solved by various algorithms that are not fast in convergence or gain enough accuracy. Meta-heuristic algorithms are used to solve optimization problems and have achieved their effectiveness in solving several real-world optimization problems. Meta-heuristic algorithms try to find the best solution out of all available solutions in the possible shortest time. A good meta-heuristic algorithm is characterized by its accuracy, convergence speed, and ability to solve high dimensions’ problems. Giza Pyramids construction (GPC) has recently been introduced as a physics-inspired optimization method. This paper suggests an enhanced Giza Pyramids construction (EGPC) by adding a new parameter based on the step length of each individual and iteratively revises the individual’ position. The EGPC algorithm is suggested for improving the GPC exploitation and exploration. Experiments were performed on 23 benchmark functions and four IEEE CEC 2019 benchmarks to test the performance of the proposed EGPC algorithm. The experimental results show the high competitiveness of the EGPC algorithm compared to the basic GPC algorithm and another four well known optimizers in terms of improved exploration, exploitation, convergence’ rate, and the avoidance of local optima.
Reinforcement learning-empowered resource allocation with multi-head attention mechanism in V2X networks
Authors: Khan Irshad , Haladappa Manjula Sunkadakatte
Published: August 2024

Intelligent transport systems (ITS) offer safe and autonomous service in vehicular applications. The vehicle to everything (V2X) network aids in performing communication between any vehicle to other entities such as networks, pedestrians or other objects. However, the allocation of power in the V2X network is still seen as a challenging task in recent resource allocation approaches. So, multi-head attention mechanism with reinforcement learning (MHAMRL) is utilized in resource allocation. This work considers real traffic scenes in highway traffic model and wireless transmission model. Specifically, in the mode 4 cellular V2X, every individual vehicle is considered as a resource which does not rely on the base station for resource allocation. Vehicle users are classified into V2I or V2V links based on the varied service requirements of V2X. The combination of multi-head attention mechanism sequences the signal with minimal noises which diminishes the energy consumption and improves channel gain. In the velocity range of 20-25 m/s, the proposed approach achieves a sum rate of 53 Mb/s, surpassing the 50 Mb/s achieved by the existing multi-agent deep reinforcement learning-based attention mechanism (AMARL) algorithm.
Supply and demand of ecosystem service provision in relation to dynamics land-cover changes: a remote sensing and geospatial analysis in Sukabumi Regency
Authors: Fitriani Ananda , Dimyati Muhammad , Zulkarnain Faris
Published: August 2024

The rate of population growth in Sukabumi Regency continues to grow, along with the increasing need for food. This population growth, combined with the constant changes in land cover can reduce the productivity of environment in providing natural capital for food availability. Therefore, this study aimed to examine the condition of ecosystem service provision for a decade in Sukabumi Regency due to changes in land cover. In general, the efficient use of remote sensing method and geographic information systems to monitor ecosystem services had received widespread recognition. Following this scenario, the current study used geospatial analysis with dasymetric method which was integrated with supply and demand formulas for ecosystem services provision, food availability, and remote sensing. Geographic information system was also used for land cover interpretation data. The results showed that three districts in Sukabumi Regency, namely Cicurug, Cibadak, and Cicantayan, had “exceeded” condition when the environmental condition already passed the threshold or were unable to support population's needs. Meanwhile, the other districts have “not exceeded” condition, when the environmental conditions were still able to fulfill the needs of population. Finally, the changes in agricultural land cover had a significant influence on the condition of ecosystem services.
An Efficient Approximate Method for Solving Bratu’s Boundary Value Problem
Authors: Al-Khaled Kamel , Ajeel Mahmood Shareef , Abu-Irwaq Issam , Al-Khalid Hala
Published: August 2024

We compute the numerical solution of Bratu’s boundary value problem (BVP). To achieve this, we apply a new and useful approach to solve Bratu’s boundary value problem by using Green’s function and a new integral operator, along with a modified version of the Adomian decomposition method. This process produces solutions that call for the boundary conditions to be applied explicitly. Statistical results demonstrating the robustness and efficiency of the proposed scheme are included. An exact and approximate solution comparison is made with known results. The quantitative outcomes showcase our novel approach’s high numerical precision and consistency across a range of parameter configurations.
Performance evaluation of machine learning algorithms for meat freshness assessment
Authors: Arsalane Assia , Klilou Abdessamad , Barbri Noureddine El
Published: August 2024

In meat industry, a non-destructive evaluation and prediction of meat quality attributes is highly required. Artificial vision technology is a powerful and widely used tool for meat quality evaluation because of reliability, reproducibility, non-invasiveness, and non-destructiveness. Machine learning methods are a fundamental and crucial part of artificial vision technology. Their choice is critical in determining successfully the quality of meat. The goal of this paper was to compare the performance of three artificial intelligence-based methods to evaluate the beef meat freshness. In this research, a dataset of beef meat samples images was used to extract the color and texture features. Different methods including the support vector machines (SVM), k-nearest neighbor (KNN), and naïve Bayes (NB) algorithms were applied to determine the freshness of samples. The accuracy rates of KNN, SVM and NB algorithms were obtained about 92.59%, 90.12% and 87.65%, respectively. The results show that the KNN provides the highest classification rates against SVM and NB algorithms.
Demographic information combined with collaborative filtering for an efficient recommendation system
Authors: Nabil Sana , Chkouri Mohamed Yassin , Bouhdidi Jaber El
Published: August 2024

The recommendation system is a filtering system. It filters a collection of things based on the historical behavior of a user, it also tries to make predictions based on user preferences and make recommendations that interest customers. While incredibly useful, they can face various challenges affecting their performance and utility. Some common problems are, for example, when the number of users and items grows, the computational complexity of generating recommendations increases, which can increase the accuracy and precision of recommendations. So, for this purpose and to improve recommendation system results, we propose a recommendation system combining the demographic approach with collaborative filtering, our approach is based on users’ demographic information such as gender, age, zip code, occupation, and historical ratings of the users. We cluster the users based on their demographic data using the k-means algorithm and then apply collaborative filtering to the specific user cluster for recommendations. The proposed approach improves the results of the collaborative filtering recommendation system in terms of precision and recommends diverse items to users.
Exploring the frontiers of trajectory outlier detection: an in-depth review and comparative analysis
Authors: Chakri Sana , Mouhni Naoual , Ennaama Faouzia
Published: August 2024

This paper provides a review and comparative analysis of trajectory outlier detection methods. It presents the definition of outliers in trajectory data and the existing types to further examine the advanced approaches. Basic steps for detecting an outlier, which include data preprocessing, feature extraction, modeling, and similar, have been presented. Moreover, advanced methods such as autoencoders and the use of deep learning for outlier detection have been explored. In the end, this paper evaluates the techniques and compares them using common metrics, mainly focusing on the techniques based on autoencoders or deep learning. It covers applications in real life and practice along with any limitations, challenges, and perspective ideas for the future. Ultimately, it can be a useful resource for expanding the understanding of domain researchers and practitioners.
Empowering crop cultivation: harnessing internet of things for smart agriculture monitoring
Authors: Alsayaydeh Jamil Abedalrahim Jamil , Yusof Mohd Faizal , Magenthiran Mithilanandini S. , Hamzah Rostam Affendi , Mustaffa Izadora , Herawan Safarudin Gazali
Published: August 2024

Agriculture, the foundation of human civilization, has relied on manual practices in the face of unpredictable weather for millennia. The contemporary era, however, witnesses the transformative potential of the Internet of things (IoT) in agriculture. This paper introduces an innovative IoT-driven smart agriculture system empowered by Arduino technology, making a significant contribution to the field. It integrates key components: a temperature sensor, a soil moisture sensor, a light-dependent resistor, a water pump, and a Wi-Fi module. The system vigilantly monitors vital environmental parameters: temperature, light intensity, and soil moisture levels. Upon surpassing 30°C, an automatic cooling fan alleviates heat stress, while sub-300CD light levels trigger light-emitting diode lighting for optimal growth. Real-time soil moisture data is relayed to the “Blynk” mobile app. Temperature thresholds align with specific crops, and users can manage the water pump via Blynk when manual intervention is required. This work advances agricultural practices, optimizing water management by crop type. Through precise coordination of soil moisture, temperature, and light intensity, the system enhances productivity while conserving water resources and maintaining fertilizer balance.
A "hora da autocrítica": diagnósticos sobre a historiografia e a formação do historiador no Brasil, anos 1960-1970
Authors: Gontijo Rebeca
Published: August 2024

O artigo analisa discursos proferidos durante três eventos acadêmicos: o I Simpósio de Professores de História do Ensino Superior (1961); o I Encontro Brasileiro sobre Introdução ao Estudo da História (1968); e o I Seminário de Estudos Brasileiros do IEB (1971). O objetivo é compreender os diagnósticos sobre a área de História, observando indicações sobre o que foi realizado e o que seria preciso realizar. A hipótese é que, em meio aos debates, a formação do professor e do historiador foi objeto de atenção e alguns referenciais para as práticas universitárias foram elaborados, construindo consensos em torno de problemas e possíveis soluções, estimulando uma espécie de consciência coletiva acerca do passado, do presente e das possibilidades de futuro dos estudos históricos no Brasil.
Trimethylamine
N
-Oxide and Related Gut Microbe-Derived Metabolites and Incident Heart Failure Development in Community-Based Populations
Authors: Tang W.H. Wilson , Lemaitre Rozenn N. , Jensen Paul N. , Wang Meng , Wang Zeneng , Li Xinmin S. , Nemet Ina , Lee Yujin , Lai Heidi T.M. , de Oliveira Otto Marcia C. , Fretts Amanda M. , Sotoodehnia Nona , DiDonato Joseph A. , Bäckhed Fredrik , Psaty Bruce M. , Siscovick David S. , Budoff Matthew J. , Mozaffarian Dariush , Hazen Stanley L.
Published: August 2024

BACKGROUND:

Growing evidence indicates that trimethylamine
N
-oxide, a gut microbial metabolite of dietary choline and carnitine, promotes both cardiovascular disease and chronic kidney disease risk. It remains unclear how circulating concentrations of trimethylamine
N
-oxide and its related dietary and gut microbe-derived metabolites (choline, betaine, carnitine, γ-butyrobetaine, and crotonobetaine) affect incident heart failure (HF).

METHODS:

We evaluated 11 768 participants from the Cardiovascular Health Study and the Multi-Ethnic Study of Atherosclerosis with serial measures of metabolites. Cox proportional hazard models were used to examine the associations between metabolites and incident HF, adjusted for cardiovascular disease risk factors.

RESULTS:

In all, 2102 cases of HF occurred over a median follow-up of 15.9 years. After adjusting for traditional risk factors, higher concentrations of trimethylamine
N
-oxide (hazard ratio, 1.15 [95% CI, 1.09–1.20];
P
<0.001), choline (hazard ratio, 1.44 [95% CI, 1.26–1.64];
P
<0.001), and crotonobetaine (hazard ratio, 1.24 [95% CI, 1.16–1.32];
P
<0.001) were associated with increased risk for incident HF. After further adjustment for renal function (potential confounder or mediator), these associations did not reach Bonferroni-corrected statistical significance (
P
=0.01, 0.049, and 0.006, respectively). Betaine and carnitine were nominally associated with a higher incidence of HF (
P
<0.05). In exploratory analyses, results were similar for subtypes of HF based on left ventricular ejection fraction, and associations appeared generally stronger among Black and Hispanic/Latino versus White adults, although there were no interactions for any metabolites with race.

CONCLUSIONS:

In this pooled analysis of 2 well-phenotyped, diverse, community-based cohorts, circulating concentrations of gut microbe-derived metabolites such as trimethylamine
N
-oxide, choline, and crotonobetaine were independently associated with a higher risk of developing HF.

REGISTRATION:

URL:
https://www.clinicaltrials.gov/
; Unique identifiers: NCT00005133 and NCT00005487.
Variability in personalities at sex, population, and taxon-levels in two European lacertid lizards
Authors: Sacchi Roberto , Cominetti Dario , Scali Stefano , Zuffi Marco A.L. , Mangiacotti Marco
Published: August 2024

The variability in behavioural syndromes at different scale levels (e.g., within population or among species) is relevant in ecological and evolutionary patterns. It provides the basis on which natural selection can act to promote evolutionary processes. Additionally, behavioural syndromes can be seen as a form of pre-adaptation, favouring a species’ ability to cope with novel selective pressures, establish in new habitats, enhancing population stability, and dealing with deseases and parasites. We assessed repeatability (two replicates for individual) for five behavioural traits (activity, boldness including components of exploration and escape, sociability, and foraging activity) and tested the occurrence of behavioural syndromes in both sexes of three taxa of lacertid lizards: two clades of the common wall lizard (Podarcis muralis maculiventris and P. m. nigriventris), and the Italian wall lizard (Podarcis siculus). All behavioural traits except sociability were repeatable in all the three taxa. Our findings clearly reveal the occurrence of a behavioural syndrome related to the propensity to move through and explore new enviroments, escape from predation risk, and successfully capture prey. We also provide empirical evidence for higher repeatability of behaviours in males compared to females, as well as differences in behavioural syndromes at the taxon-level. These results support the occurrence of personality in both common and Italian wall lizards, highlighting marked variability in personality across different scales. This variability could ultimately reflect the interaction between the specific needs inherent in both sexes within each species, and the environmental/competitive context in which these needs are fulfilled.
Thermoregulation and activity patterns of three species of snakes with different lifestyles in central Mexico
Authors: Figueroa-Huitrón Ricardo , Díaz-Martínez Elia Fernanda , Méndez de la Cruz Fausto Roberto , Pérez-Mendoza Hibraim Adán
Published: August 2024

Temperature is closely linked to the various lifestyles of reptiles, considerably influencing many aspects of their behaviour. Species with contrasting foraging modes or associated with different microhabitats are expected to thermoregulate in distinct ways. We sought to describe the thermal ecology of three model snake species, highlighting how their distinct lifestyles account for variation in thermoregulatory strategies. We chose the fossorial Conopsis lineata, the terrestrial Crotalus polystictus, and the semi-aquatic Thamnophis melanogaster. Following the protocol proposed by Hertz et al. (1993), we gathered data on body and operative temperatures in the field and preferred temperatures () in the laboratory. The three species exhibited a wide range of , with differences between lower and upper limits ranging from 6.4°C to 8°C. The E index of effectiveness of thermoregulation was 0.685 for C. polystictus, 0.644 for C. lineata, and 0.486 for T. melanogaster. These results indicate that C. lineata and C. polystictus are precise and active thermoregulators. C. lineata has cryptic and fossorial habits, and we propose that its efficient thermoregulation likely results from the strategic selection of thermally favourable retreat sites. Contrary to what we predicted, T. melanogaster turned out to be the least accurate thermoregulator. Its semiaquatic lifestyle can help it to maintain beneficial body temperatures with less effort since aquatic environments are more thermally homogeneous. Overall, our study shows that lifestyle affects thermoregulation to an extent in these species, but that the thermal environments they inhabit also plays a key role in delimiting their thermoregulatory strategies.
Acoustics of parental and hybridogenetic water frogs (Pelophylax: Amphibia: Ranidae): a tool for monitoring an invasive species
Authors: Gomes de Almeida Filipe , Ciavatti François , Pulido Angélica , Gendre Thomas , Geniez Philippe , Eble Aimeric , Leblanc Elise , Mansier Yoann , Martinossi-Allibert Ivain , Raymond Blaise , Pineau Adrien , Demay Jérémie , Crochet Pierre-André
Published: August 2024

The Pelophylax genus comprises two main hybridogenetic systems resulting from hybridization between Pelophylax ridibundus and other species: the P. lessonae-esculentus (LE) system and the P. perezi-grafi (PG) system, whose distribution ranges lie partly (LE) or entirely (PG) outside the natural distribution of P. ridibundus. The introductions and subsequent expansions of P. ridibundus in many areas naturally inhabited by these hybridogenetic systems have led to their decrease or local extinction. One of the major impediments to efficient population surveys and conservation measures for these systems is the difficulties of reliable species identification due to their similar morphology and poorly documented vocalizations, especially for the PG system. Our aim in this work was to design a reliable method of acoustic identification to distinguish the taxa composing the hybridogenetic systems from P. ridibundus and to assess the possibility of separating the PG and LE systems by their vocalizations. Our acoustic identification methods proved highly accurate to separate the PG and LE systems from P. ridibundus but only a fraction of the PG and LE calls can be identified.
Native African Bishops: Emerging Voices on the Eve of Vatican II (1959–1960)
Authors: Nnabugwu Chidiebere Obiora
Published: August 2024

Some scholars have advanced timidity and conformism as typifying the contributions of young missionary churches, such as the Catholic Church in Africa at the antepreparatory phase of the Second Vatican Council. It has further been suggested that these features are more characteristic of the native prelates than their foreign missionary counterparts. This article, focusing on the vota of the indigenous African bishops, contests this stance to say that boldness, broad-mindedness, keen desire for adaptation, aggiornamento [updating], and reform were not lacking in the responses of these natives. Through a close study of the vota of these African-born prelates, the present essay demonstrates that these nascent native clergy found their voice right from the antepreparatory phase of Vatican II, and manifested their acute awareness of the reforms and adaptations they expected from the Council. Perceptible signs of reticence and timidity should thus be seen more as a general feature that could be identified with some individual bishops and prelates in all continents.
Towards the Empowerment of the Vanquished
Authors: Zou Tao , Asaah Augustine H.
Published: August 2024

Against the background of the 19th century British domination of Ghana, Ayi Kwei Armah’s The Healers (1978) depicts the restorative activities of a group of traditional health practitioners. The paper seeks to place African Indigenous Knowledge Systems at the heart of the resistance to colonialism and imperialism. The paper resorts to a close reading of the narrative guided by perspectives on recuperation provided by Armah, Amilcar Cabral, and Christel N. Temple. Traditional medicinal expertise, ecological awareness, cultural memory, and anthroponymy, as bearers of African Indigenous Knowledge Systems, coalesce to discredit the metropolitan miscomception of Africa as a tabula rasa, thereby infusing confidence into subalternized Blacks towards the dream of a reunified continent. The paper concludes that resistance to the empire entails politics and ethics of healing, empowerment, continuous mental decolonization, and commitment to African reunification.
A new and unusually ornate southern African dung beetle species in d’Orbigny’s Onthophagus Group 18 (Coleoptera: Scarabaeidae: Onthophagini)
Authors: DESCHODT CHRISTIAN M. , SOLE CATHERINE L.
Published: August 2024

Onthophagus pragtig Deschodt, new species is described and illustrated from South Africa. Its known collection localities are shown. We also briefly describe and illustrate three distinct subgroups in the18th Onthophagus group of d’Orbigny (1913) and further provide a checklist of the known species in this group while dividing them into these subgroups where known.
Can Expert Prediction Markets Forecast Climate-Related Risks?
Authors: Roulston Mark , Kaivanto Kim
Published: August 2024

Prediction markets use a betting-like mechanism to aggregate information from disparate sources to produce probability forecasts that represent the collective view of participants. They have similarities with financial markets but are designed specifically to discover information rather than transfer assets or risks. Contracts that pay out a fixed amount if a well-defined event occurs are traded in these markets and, under certain conditions, the price at which these event-contracts trade can be interpreted as a probability estimate that the event will occur. This article examines the performance of 24 prediction markets for climate-related variables that have been run over the past 5 years. The predicted variables included the monthly temperature and rainfall for the United Kingdom, an index of El Niño–Southern Oscillation, Atlantic hurricane activity, and U.K. wheat yield. The markets had horizons of 2–12 months. Invitations to participate in the markets were extended to individuals and teams with relevant expertise. Participants did not have to pay to take part but did receive cash rewards based on their performance. Trades were made through an automated market maker overcoming the problems of low activity that have affected previous prediction markets for specialized topics. The predictions of the markets were consistent with good reliability, given the resolving power afforded by the sample size. Whether this level of reliability would persist for longer, multiyear, horizons relevant to climate change cannot be known without running markets on such time scales, something that we strongly advocate.
Parameterization of Vertical Turbulent Transport in the Inner Core of Tropical Cyclones and Its Impact on Storm Intensification. Part I: Sensitivity to Turbulent Mixing Length
Authors: Katz Jeremy , Zhu Ping
Published: August 2024

In the inner core of a tropical cyclone, turbulence not only exists in the boundary layer (BL) but can also be generated above the BL by eyewall and rainband clouds. Thus, the treatment of vertical turbulent mixing must go beyond the conventional scope of the BL. The turbulence schemes formulated based on the turbulent kinetic energy (TKE) are attractive as they are applicable to both deep and shallow convection regimes in the tropical cyclone (TC) inner core provided that the TKE production and dissipation can be appropriately determined. However, TKE schemes are not self-closed. They must be closed by an empirically prescribed vertical profile of mixing length. This motivates this study to investigate the sensitivity of the simulated TC intensification to the sloping curvature and asymptotic length scale of mixing length, the two parameters that determine the vertical distribution of a prescribed mixing length. To tackle the problem, both idealized and real-case TC simulations are performed. The results show that the simulated TC intensification is sensitive to the sloping curvature of mixing length but only exhibits marginal sensitivity to the asymptotic length scale. The underlying reasons for such sensitivities are explored analytically based on the Mellor and Yamada level-2 turbulence model and the analyses of azimuthal-mean tangential wind budget. The results highlight the uncertainty and importance of mixing length in the numerical prediction of TCs and suggest that future research should focus on searching for physical constraints on mixing length, particularly in the low- to midtroposphere, using observations and large-eddy simulations.

Significance Statement

The parametric representation of subgrid-scale turbulent mixing is one of the major sources of uncertainty in numerical predictions of tropical cyclones (TCs). This study investigates how the numerical prediction of TC intensification is affected by the turbulent mixing length, a length scale that is required to close a turbulence scheme formulated based on the turbulent kinetic energy (TKE). The research highlights the uncertainty and importance of mixing length in numerical prediction of TCs and suggests that future research should focus on searching for physical constraints on the mixing length, particularly in the low- to midtroposphere, using observations and large-eddy simulations.
Revisiting the Role of Atmospheric Initial Signals in Predicting ENSO
Authors: Wu Jiye , Luo Jing-Jia , Doi Takeshi , Yamagata Toshio , Behera Swadhin K.
Published: August 2024

El Niño–Southern Oscillation (ENSO) provides an important source of global seasonal–interannual predictability, while its prediction encounters bottlenecks. Besides the slow-varying air–sea feedbacks, high-frequency atmospheric signals (HFASs) act on ENSO evolution. This study revisits the role of atmospheric initial signals in ENSO prediction using an atmosphere–ocean coupled model. Two sets of sensitivity hindcasts are conducted. One utilizes a pure sea surface temperature (SST) nudging for initialization so that no observed atmospheric internal signal is assimilated. The other applies a combination of the SST nudging and spectral nudging of JRA-55 reanalysis, which not only assimilates the observed atmospheric states and hence realizes skillful predictions of HFAS at the initial stage but also improves the oceanic initial conditions (ICs), especially around the thermocline. Unexpectedly, the better atmosphere–ocean ICs neither improve ENSO prediction nor overcome the spring prediction barrier. Further analysis of Bjerknes stability index suggests that underestimated negative feedbacks and insufficient responses of ocean currents and thermocline slope to atmospheric internal winds may account for the failure. Nevertheless, assimilating the atmospheric information partly improves the prediction of El Niño onset, especially for two recent extreme cases (i.e., 1997/98 and 2015/16). This improvement is associated with better representations of initial westerly wind bursts (WWBs) that excite subsequent downwelling equatorial Kelvin waves. However, due to unpredictable WWBs and underestimated wind response to the SST warming owing to the cold tongue biases in boreal summer, the zonal advective feedbacks are underestimated and thus further development of El Niño cannot be well predicted. This study suggests the importance of initial atmospheric signals despite the limitation, prompting further efforts to improve model physics.
It’s Social Media, Stupid! Opportunities and Constraints in the Representation of Corruption in and Through Social Media
Authors: Marchetti Rita , Stanziano Anna , Mincigrucci Roberto , Del Sarto Simone , Pagiotti Susanna
Published: August 2024

Recent years have seen a growing interest in the role of the media in relation to corruption. This interest, however, has particularly concerned legacy media, leaving the role played by social media in relation to corruption largely unexplored. This study attempts to understand how social media contributes to the public representation of corruption through an analysis of the actors who discuss it and the topics they introduce into the debate. Despite social media’s ability to diversify both the actors able to intervene in the public debate and the sub-topics being discussed, some aspects of it, such as the affordances of the platforms that fuel polarization, favor political instrumentalization of corruptive phenomena. By investigating how social media deals with corruption based on a content analysis of the posts and an analysis of the actors who intervene in the discussions, this study fills a gap in the literature.
Prevalence of suicidal and self-harm ideation in fathers during the perinatal and early parenting period: A systematic review and meta-analysis
Authors: Fogarty Alison , McMahon Grace , Findley Helen , Hosking Casey , Schulz Madison , Seymour Monique , Leach Liana , Borschmann Rohan , Garfield Craig F , Giallo Rebecca
Published: August 2024

Objective:

Suicide is a leading cause of death in males aged 25–44 years, an age which often coincides with becoming a father. This review aims to synthesise the evidence of the prevalence of suicidal and self-harm ideation in fathers during the perinatal, postnatal and early parenting period.

Methods:

Five databases were searched (PsycINFO, Medline, Web of Science, PubMed and the Cochrane Database of Systematic Reviews) to identify papers published between 1 January 2000 and 9 March 2023. A meta-analysis was conducted to estimate the prevalence of suicidality and self-harm ideation across the included studies. Subgroup and sensitivity analyses were conducted to explore potential sources of heterogeneity.

Results:

A total of 4215 articles were identified, with 14 studies included in the review. The combined pooled prevalence of suicidal and self-harm ideation was 4.2% (95% CI [2.6%, 6.2%]). Prevalence estimates were higher for self-harm ideation at 5.1% (95% CI [2.6%, 6.2%]) than for suicidality at 3% (95% CI [0.9%, 6.1%]).

Conclusions:

This review found that a considerable proportion of fathers experience suicidal and self-harm ideation during the early years of parenting. However, the paucity of rigorous prevalence studies indicates that further research in this area is needed urgently.
Enhancing Occupational Therapists’ Intervention Capabilities With Adolescents With Neurodevelopmental Disorders
Authors: Tal-Saban Miri , Zaguri-Vittenberg Shahar , Ivzori Yonat
Published: August 2024

Background. Adolescents with neurodevelopmental disorders (NDDs) have an increased risk of participation limitations. Occupational therapy faces knowledge-to-practice gaps among this population. Purpose. To examine the effectiveness of a continuing-education program for occupational therapists working with adolescents with NDDs. Methods. The study used a mixed-method design, including pre–post comparisons of competence and sense of self-efficacy of occupational therapists to work with adolescents with NDDs. Interviews regarding the program's perceived contribution to practice were conducted. Results. Post-program results showed significantly ( p < .001) higher professional competence and self-efficacy for interventions and evaluation skills with NDD's adolescents and transition programs as well as higher knowledge about adolescence and NDDs characteristics. Contrary, there was no change in competence and self-efficacy regarding client-centred interventions with adolescents. Program participants increased their professional confidence and incorporated evidence-based methods into routine practice. Conclusions and Implications. The continuing-education program that was examined expanded the occupational therapy practitioner's knowledge and professional skills, resulting in a change in their capacity to practice with adolescents narrowing the knowledge-to-practice gap. Future studies involving the client perspective, regarding their participation may validate and support continuing-education program for occupational therapists.
Evolution of bamboo derivative fiber-mollusk shell based calcite particulate hybrid reinforced epoxy bio-composites for sustainable applications
Authors: Oladele Oluwole I , Akinwekomi Akeem D , Akinseye Joseph G , Falana Samuel O , Oke Samuel R
Published: August 2024

This study focused on the development of environmentally friendly epoxy-based biocomposites by incorporating bamboo fiber (BF) and calcite particles (CP) as reinforcements. A total of 125 specimens were fabricated with different weight percentages of BF and CP ranging from 0% to 15% to the epoxy. The biobased reinforcements were processed before incorporation, and the hand layup technique was used to fabricate the composites, which were then cured at ambient temperature. Evaluations of selected properties were carried out to ascertain the most probable areas of application, while the fracture surfaces were examined using SEM. The results indicated that the inclusion of these hybrid bio-reinforcements significantly enhanced the properties of the biocomposites compared to unreinforced samples. The optimal composition was identified as 12 wt% BF/CP, which demonstrated the highest ultimate tensile strength (32.84 MPa) and tensile modulus (1.9 GPa), attributed to the strong interfacial bonding between the epoxy matrix and the BF/CP reinforcements. Additionally, the flexural strength (55.38 MPa) and modulus (2.72 GPa) were improved due to the effective load transfer and stiffening effect of the calcite particles. The hardness (67 HS) and wear index (0.015 mg) were enhanced by the toughening mechanism provided by the bamboo fibers, while the density (1.195 g/cm³) remained within a desirable range for lightweight applications. Composites with 9 wt% BF/CP exhibited the highest impact strength (22.66 J/m2), likely due to the optimal balance of fiber-matrix interaction and energy absorption capacity. These findings indicate that the developed hybrid-reinforced biocomposite compositions hold great promise with the improved physical and mechanical properties as seen and can be utilized for various applications, including automotive, aerospace, and other engineering applications.
Dual Narratives of the Israeli–Palestinian Conflict in Court: Shaping the Perception of International Terrorism
Authors: Porat Dan
Published: August 2024

This article focuses on trials stemming from attacks on El Al aircraft in Athens (1968) and Zurich (1969) and their role in shaping the narrative surrounding the Israeli–Palestinian conflict. The defense framed the attacks as part of a Palestinian military campaign against Israel, thus rationalizing the behavior as a legitimate response to its actions in occupied territories. In doing so, the Palestinians sought to portray themselves as political resistance fighters. Conversely, the Israelis depicted the attacks as deplorable acts of terrorism that targeted innocent civilians. Their objective was to counter the political motivations put forward by the Palestinians and emphasize the potential repercussions of such attacks on international air travel. These trials marked the first instances of legal proceedings specifically addressing international terrorism, framing these incidents as acts of terrorism rather than legitimate acts of liberation. The trials, along with their outcomes and the subsequent verdicts issued by the courts, played a significant role in defining these actions as terrorism. Subsequent international conventions on hijacking further reinforced this perspective, solidifying them as illegitimate acts of terror.