bluuebunny/crossref_metadata_2025 huggingface dataset:
The Development of High Availability Database Infrastructure for OSS Projects with Monitoring Systems in Cloud Computing Environments
Authors: Ikhsan Akmal , Kusumo Dana
Published: September 2024

This study addressed a critical problem in the Kominfo's Operation Support System (OSS) project, which significantly impacted business operations. The outage of the MongoDB database, a vital component of OSS, resulted in server crashes and data damage. To overcome this issue, the OSS team initiated a project to migrate to PostgreSQL for high availability and improved database performance. The implementation involved the use of HA PostgreSQL technology, with multiple connected servers sharing data in real-time. Through functional and performance testing, the study has demonstrated that the HA PostgreSQL system increased database availability, managed server failures, and facilitated effective cluster administration. The findings of this research can guide the development of the OSS project's IT infrastructure and serve as a reference for similar projects utilizing HA PostgreSQL technology.
Convolutional Neural Network Implementation with AlexNet Architecture for Face Recognition
Authors: Denny Hardiyanto  , Dyah Anggun Sartika  , Imam Junaedi  , Sukamto
Published: September 2024

In today's digital era, the process of facial recognition has a very big role. Face recognition has benefits for authentication and identification processes. The development of facial recognition research continues to be carried out with the aim of being able to get the right algorithm, more accurate, faster processing, to be able to recognize faces from various angles. In this study, a performance test was performed on the Convolutional Neural Network (CNN) algorithm with the AlexNet architecture, which is one of the deep learning algorithm developments for facial recognition. AlexNet has 8 convolution layers so that it will not leave even the slightest feature of the object. The process of training and testing the system uses the MATLAB programming language. The number of datasets used is 400 image data which is divided into 360 training image data and 40 test image data. The 400 data come from 4 classes of facial images that have been labeled with names and each classes have 100 images. The training process produces an accuracy of 100% and the testing process produces an accuracy of 95%.
Machine Learning Sentiment Analysis in Cyber Threat Intelligence Recommendation System
Authors: Aji bawono Marastika wicaksono aji bawono , Kasman Sachlany , Dwi Utomo Stevani
Published: September 2024

The use of the digital world is increasing every day. Attacks and data theft occur on various websites, both government-owned and commercial and banking sites. Therefore, this research aims to identify the threats of frequently occurring viruses in a country. There is a considerable amount of news explaining cybercrime incidents. The problem of this research is that unstructured data such as articles and technical reports are difficult to analyze and identify the types of cybercrime attacks. Previous research attempted to semantically extract unstructured cyber threats, but there were shortcomings in previous research. The novelty of this research is the development of a Cyber Threat Intelligence (CTI) machine learning model to identify the types of virus attacks or cybercrimes that frequently occur in e-commerce transactions, so that they can take rescue actions for incident handling in the digital world using tactics, techniques, and procedures (TTP). The method involves using machine learning, taking Cyber Threat Intelligence (CTI) documents as input regarding cybersecurity threat handling steps, and then processing the data using AI TF-IDF and Bags of Words  for the identification of steps, tactics, techniques, and procedures required for each frequently occurring security incident.
Socio-user Context Aware-Based Recommender System: Context Suggestions for A Better Tourism Recommendation
Authors: Kusuma Adi Achmad  , Lukito Edi Nugroho  , Achmad Djunaedi  , Widyawan
Published: September 2024

The existing tourism recommender system model is mostly predictive analytics for destination recommendations (item recommendation). Limited research has been conducted in the discussion of a recommender system model, particularly context suggestion. Thus, it is necessary to develop a recommender system model not only to predict tourism destinations but also to suggest contexts appropriate for tourist preferences (context suggestions). A deep learning method was used to create a model of the socio-user context aware-based recommender system for context suggestions. The attribute used as a label to suggest context was uHijos, uCuisine, uAmbience, and uTransport. The accuracy of the socio-user context aware-based recommender system in suggesting the context of uHijos, uAmbience, and uTransport was 100% with an error rate of 0%. It was found that only the level of recognition of the model in suggesting uCuisine was less accurate (below 30%) with a classification error for more than 70%. Performance evaluation of the socio-user model context-based recommender system was considered efficient, particularly for the evaluation of the level of accuracy, completeness (recall/sensitivity), precision, and a harmonic average of precision and recall (F-score), mainly for label/context of uHijos, uAmbience, and uTransport.
XGBoost for Predicting Airline Customer Satisfaction Based on Computational Efficient Questionnaire
Authors: Nur Ghaniaviyanto Ramadhan  , Aji Gautama Putrada
Published: September 2024

Customer satisfaction can be created through a well-crafted service quality strategy, which forms the cornerstone of a successful business-customer relationship. Establishing and nurturing these relationships with customers is vital for long-term success. Within the airline industry, a persistent challenge lies in enhancing the passenger experience during flights, necessitating a comprehensive understanding of customer demands. Addressing this challenge is crucial for airlines aspiring to thrive in a competitive landscape, thus underlining the significance of providing top-notch services. This study addresses this issue by leveraging predictive airline customer satisfaction data analysis. We forecast customer satisfaction levels using a powerful Extreme Gradient Boosting (XGBoost) ensemble-based model. An integral aspect of our methodology involves handling missing values in the dataset, for which we utilize mean-value imputation. Furthermore, we introduce a novel logistic Pearson Gini (Log-PG) score to identify the factors that significantly influence airline customer satisfaction. In our predictive model, we achieved notable results, showing an accuracy and precision of 0.96. To ascertain the efficiency of our model, we conducted a comparative analysis with other boosting-type ensemble prediction models, such as gradient boosting and adaptive boosting (AdaBoost). The comparative assessment established the superiority of the XGBoost model in predicting airline customer satisfaction.
Predicting Forest Fire Hotspots with Carbon Emission Insights Using Random Forest and Gradient Boosting Regression
Authors: Palupi Irma , Wahyudi Bambang ari , AL Mamuda Naila , Shabrina Ayu
Published: September 2024

This research paper focuses on predicting the dispersion of carbon emissions, a crucial indicator for identifying potential forest fire hotspots in the wooded regions of Sumatra Island, Indonesia. Forest fires, often triggered by extended periods of dry weather, result in significant environmental degradation, impacting both the ecosystem and the economy. Furthermore, health concerns arise from smoke inhalation, leading to respiratory problems. To achieve this predictive capability, we harnessed valuable datasets, including GFED4.1s for carbon emissions and ERA5 for historical climate indicators, spanning from 1998 to 2022. Employing supervised learning ensemble methods, specifically Random Forest Regression (RFR) and Gradient Boosting Regression (GBR), we sought to forecast carbon emissions. It is noteworthy that our predictions encompassed carbon emission values from 1998 to 2023, providing insights into recent trends. Our analysis showed that GBR did better than RFR in terms of evaluation metrics, with a root mean square error (RMSE) of 10.87 and a mean absolute error (MAE) of 2.91. This was done by carefully tuning the hyperparameters. Additionally, our study highlighted that precipitation, temperature, and humidity were the primary climate factors influencing carbon emission values.
Kim Jong United: How a Future North Korean ASAT Threat Makes Strange International Bedfellows and Novel Opportunity
Authors: Scheiber Max A.
Published: September 2024

While North Korea does not have a formal direct ascent antisatellite (DA-ASAT) weapon, its missile technology provides some baseline technology to make one, and a DA-ASAT program furthers the ruling Kim Jong Un regime’s strategic goals. Thus, the United States should prepare for this threat now. This article argues that North Korean DA-ASAT weapon is a unique political-military challenge, in that China and Russia—traditional North Korean allies but major space-faring nations—are also threatened by this weapon because of the indiscriminate space debris it creates. This creates aligned interests between the United States, China, and Russia to stop a North Korean DA-ASAT program, and as this article asserts, the best way to do that is to cooperate in slowly advancing the North Korean space program with nonthreatening technology in return for the country abandoning DA-ASAT research.
Breaking the Newtonian Fetish: Conceptualizing War Differently for a Changing World
Authors: Zweibelson Ben
Published: September 2024

Explaining what a military’s social paradigm concerning conflict and war is requires a theoretical approach to both frame the core constructs and offer feasible alternatives. This article introduces social paradigm theory for military application and how most modern, technologically advanced militaries sustain a Newtonian-styled worldview concerning warfare and what constitutes war. The Newtonian-styled war paradigm gained prominence during the last five centuries, yet is now becoming increasingly insufficient and possibly irrelevant. The integration of ever-increasingly sophisticated artificial intelligence into nearly all aspects of warfare will require new ways of thinking and how teams of humans and AI systems collaborate in complex security contexts immediately. The new combination of the space domain, cyberspace, those military forces associated with these new domains, and special operations activities are of increased focus for how and why conflict may change, particularly within an overarching traditional nuclear deterrence between state competitors. This requires a military paradigmatic shift, moving away from Newtonian constructs.
Individual and Universal Self from the Point of View of Buddhist Philosophy Regarding the Interpretations of Leo Tolstoy’s Reasons for Leaving Home
Authors:   Soka University, Eguchi Mitsuru
Published: September 2024

The development of Buddhist thought is characterized by the existence of many schools and a variety of approaches while maintaining ideological integrity. The doctrine of no-self (Skt. anātman) is considered one of the key tenets of the Buddhist worldview. However, this idea needs significant clarification. Due to the fact that initially the Buddha’s sermons did not contain dogmatic principles, but were addressed to different people in relation to different circum­stances, the doctrine of anātman received many interpretations – from the com­plete denial of the Self to the consideration of the individual Self as a “small self”, which must be harmonized with the “universal self”. The article examines the differences in understanding of the Self, in particular conceptions about the individual and “universal self” using the example of reflections on the rea­sons for Leo Tolstoy’s escape from his home, put forward by thinkers who expressed the points of view of various branches of Buddhism – Hinayana and the Far Eastern schools of Mahayana. The author focuses on the fact that Buddhism carries enormous potential for the development of philosophical thought, since its very basis contains a polemical impulse and the possibility of discussing problems at different levels with the involvement of heterogeneous cultural and historical experience
Argumentation Theory in Russia: Reconstructing Tradition
Authors:   Faculty of Philosophy, Lomonosov Moscow State University, Alekseev Aleksandr
Published: September 2024

The author considers the study of the history of developing argumentation prob­lems in Russia as an urgent task and contribution to the development of the the­ory of argumentation corresponding the present challenges. The article presents a general view of the theory of argumentation as one of the programs of “axial time” in the history of humankind – realizing, first of all, the ideas of Aris­totelian “Rhetoric”. The author interprets Russian intellectual culture as one of the heirs of ancient Greek and explores the prerequisites and circumstances of the formation of the theory of argumentation as a research area in the USSR. Special attention is paid to the role that logic played in this case. The author in­sists that it was the maintenance of traditional logic positions in Soviet education in the middle of the XX century that led to the interpretation by domestic scien­tists of the theory of argumentation as having logic as a basis. This was a signi­ficant difference between the Soviet (and Russian) approach from the Western European one. The last was formed in conditions when the mathematization of logic led to the displacement of traditional logic from the field of research and education. This factor explains the opposition of argumentation to proof that was stressed by Ch. Perelman and his followers. Perelman proceeded from the inter­pretation of logical proof as a sequence of formulas constructed according to cer­tain rules. Considering the discussion by Russian philosophers and philologists of the question of the relationship between the theory of argumentation and rhetoric, the author of the article evaluates the idea of “argumentorics” put for­ward by V.N. Bryushinkin at the beginning of this century as very promising. The author shows that the rhetorical teaching of M.V. Lomonosov, set out in the treatise “A brief guide to eloquence. The first book, which contains rhetoric…” has every reason to be understood as an “argumentoric” theory. The article was prepared for the 70th anniversary of the Department of Philoso­phy for Humanities at Lomonosov Moscow State University
Building A Foundation Model for Drug Synergy Analysis Powered by Large Language Models
Authors: Zhao Hongyu , Liu Tianyu , Chu Tinyi , Luo Xiao
Published: September 2024

Drug synergy prediction is a challenging and important task in the treatment of complex diseases including cancer. In this manuscript, we present a novel Foundation Model, known as BAITSAO, for tasks related to drug synergy prediction with a unified pipeline to handle different datasets. We construct the training datasets for BAITSAO based on the context-enriched embeddings from Large Language Models for the initial representation of drugs and cell lines. After demonstrating the relevance of these embeddings, we pre-train BAITSAO with a large-scale drug synergy database under a multi-task learning framework with rigorous selections of tasks. We demonstrate the superiority of the model architecture and the pre-trained strategies of BAITSAO over other methods through comprehensive benchmark analysis. Moreover, we investigate the sensitivity of BAITSAO and illustrate its unique functions including new drug discoveries, drug combinations-gene interaction, and multi-drug synergy predictions.
Feasibility of CO2 Pipeline Construction to Enable Gigaton-Scale Carbon Dioxide Removals: Evidence from historical precedent
Authors: Roberts Cameron , Nemet Gregory F
Published: September 2024

We assess the feasibility of a rapid CO2 pipeline buildout using historical evidence from oil and gas pipelines. We answer four questions: 1) What length of pipeline network will be required to achieve the benchmarks of 1 GT or 100 Mt of CO2 in 2050? 2) What have been the fastest national oil and gas pipeline buildouts achieved in a 25-year period ? 3) Are the pipeline requirements for gigaton-scale CO2 removals feasible given these historical precedents, and 4) Under what political, economic, and social circumstances have rapid pipeline buildouts occurred? Modelling studies projecting 100 Mt of CO2 transportation and sequestration capacity by 2050 suggest rates of pipeline construction that are precedented in 18 national 25 year build-outs during the twentieth and twenty-fist centuries. For 1 Gt, only two 25-year national pipeline build-outs (both in the USA) achieve the rate of pipeline construction that the modelling studies suggest would be required, only three 25-year periods of global pipeline construction meet the benchmark. Rapid construction of fossil fuel pipelines has benefited from strong economic and institutional drivers, which may not apply to CO2 pipelines in the same way. Our findings are reason for caution about the likelihood of CO2 pipeline buildouts keeping pace with CO2 removal targets.
Biological invasions and their potential economic costs in Morocco
Authors: Jamaai Jazila El , Taheri Ahmed , Ballesteros-Mejia Liliana , Ahmed Danish , Bang Alok , Diagne Christophe , Courchamp Franck , Angulo Elena
Published: September 2024

Biological invasions pose substantial economic threats globally, yet detailed cost assessments for many Global South nations, especially in Africa, remain scarce. This study presents the first comprehensive breakdown of the potential costs of biological invasions in Morocco. We identified 551 invasive alien species, comprising approximately 1.76% of Morocco's biodiversity. Using the InvaCost database, we gathered cost data for the 12 most frequent invasive species with available data. Calculating the mean annual cost for each species and extrapolating based on their prevalence in Morocco, we estimated the potential annual economic impact to be US$ 1.61 billion. Urban species management and damage, especially pigeons, accounted for a substantial portion of this impact. Invasive plant species also emerged as prevalent and costly. Annual management costs exceeded damage costs (US$ 1.50 billion vs. 856.80 million), driven mainly by pigeon management. Costs by authorities and stakeholders outweighed agricultural costs (US$ 1.49 billion vs. 859.10 million). Despite challenges in extrapolating cost data from other regions, this study underscores the urgent need for targeted management and policy interventions to minimize the spread of invasive species and reduce their economic toll. Morocco can implement proactive management measures and foster international collaborations to tackle this socio-ecological crisis, ensuring long-term sustainability and prosperity.
Non-convex fractional-order TV model for image inpainting
Authors: Lian Wenhui , Liu Xinwu
Published: September 2024

This paper aims to address the challenge of effectively processing missing or corrupted image areas using the known image information. In this study, we consider a novel non-convex and non-smooth variational model tailored for image inpainting. Our scheme introduces the non-convex potential function into the fractional order total variation regularization. This innovative technique effectively mitigates blurred contours and alleviates the undesirable staircase effect, significantly improving restoration quality. Numerically, to tackle the generated optimization problem, we design a practical primal-dual algorithm that integrates with the iteratively reweighted 1 algorithm. Extensive simulation experiments, comparing our novel model with other approaches, conclusively demonstrate its capability to achieve the visually realistic inpainting while yielding the highest quantitative results.
Elucidating the enzyme network driving Amaryllidaceae Alkaloids Biosynthesis
Authors: Desgagne-Penix Isabel , Lamichhane Basanta , Gélinas Sarah-Eve , Merindol Natacha , Koirala Manoj , Santos Karen Cristine Gonçalves dos , Germain Hugo
Published: September 2024

Amaryllidaceae alkaloids (AAs) are diverse bioactive metabolites with significant pharmaceutical potential, derived from 4´-O-methylnorbelladine (4´OM). The biosynthesis of these compounds involves the condensation of tyramine and 3,4-dihydroxybenzaldehyde by norbelladine synthase (NBS) and/or noroxomaritidine/norcraugsodine reductase (NR), followed by O-methylation. Cytochrome P450 enzymes, particularly the CYP96T family, introduce further structural diversity through C-C couplings, resulting in lycorine, galanthamine, and crinine cores. Despite their importance, the exact biosynthetic pathways remain poorly defined. In this study, we characterized key enzymes from Leucojum aestivum (La), providing crucial insight into AA biosynthesis. Transient expression in Nicotiana benthamiana demonstrated that LaNBS and LaNRII catalyze the conversion of tyramine and 3,4-dihydroxybenzaldehyde to norbelladine, which is subsequently O-methylated by a norbelladine-4´-O-methyltransferase (LaN4´OMT). Co-agroinfiltration of LaNBS, LaNRII, LaN4´OMT, and LaCYP96T1 resulted in the production of various phenol-coupled products, with lycorine as the predominant compound, alongside with haemanthamine, crinine/vittatine, and norgalanthamine. This study identifies LaCYP96T1 and LaCYP96T2 as the first monocot enzymes capable of catalyzing all three regioselective C-C phenol couplings, highlighting the substrate promiscuity of LaNRII. The findings not only elucidate crital steps in AA biosynthesis but also open new avenues for biotechnological application in producing valuable alkaloids, offering potential for novel drug development.
Pre-Therapeutic Prognostic Factors Influencing Outcomes of Brain Hypothermia Therapy for Hypoxic-Ischemic Encephalopathy
Authors: Kurimoto Tomonori , Tokuhisa Takuya , Hayasaka Itaru , Yamamoto Tsuyoshi , Ibara Satoshi
Published: September 2024

Perinatal hypoxic-ischemic brain injury can cause long-term neurodevelopmental impairment (NDI), including motor, cognitive, and behavioral deficits. Hypoxic-ischemic encephalopathy (HIE) affects approximately 1.3–1.7/1,000 live births. Although brain hypothermia therapy (BHT) can significantly reduce mortality and NDI in HIE, half of infants with moderate to severe HIE still experience death or NDI. Herein, we enrolled 144 infants with HIE treated with BHT at Kagoshima City Hospital, Japan between January 2000 and October 2022, of whom 100 were evaluated. Clinical data, amplitude-integrated electroencephalograms (aEEG), Thompson scores, and resuscitation data were analyzed to identify predictive factors for developmental outcomes at 18 months. Significant predictors of death cerebral palsy (CP); a developmental quotient (DQ) < 70 including low Apgar scores at 1/5/10 min; low umbilical artery pH; flat trace or continuous low voltage on aEEG; a high Thompson score; chest compressions during resuscitation; and adrenaline during resuscitation. Flat trace or continuous low voltage on aEEG, Thompson score ≥ 12, and chest compressions remained significant predictors on multivariate regression analysis. When combined, these criteria had a high sensitivity (0.704) and specificity (0.0096), with an AUC of 0.86. These factors can guide early prognostic predictions before the initiation of BHT, thereby providing critical prognostic information for treatment.
Analysis of Spatial Distribution of Precipitation Using Hydrological Modeling of Watersheds
Authors: Gorjizade Ali , Shahbazi Ali
Published: September 2024

One of the significant challenges in hydrological modeling is the spatial variability of rainfall; even a slight change in rainfall patterns can lead to fundamental differences in runoff responses, affecting the accuracy of predictions. Moreover, the complexity of rainfall patterns, influenced by topography, increasingly leads to mispredictions. Therefore, this study investigates and analyzes the spatial distribution of rainfall and the role of the dispersion of raindrop data and its impact on rainfall-runoff modeling using raindrop data from two gridded precipitation products, PERSIANN-CDR and ERA5, at daily and monthly scales with the semi-distributed SWAT model. For this purpose, raindrop data from four stations—Talezang, Tang-e Panj Bakhtiari, Sepid Dasht Sezar, and the Dez Dam Rain gauge stations and Tele-Zang hydrometry station —from 2008 to 2019 were utilized.
The results of the simulations compared with the observed data indicate that due to the lack of a suitable raingage network and spatial distribution of rainfall, the SWAT rainfall-runoff simulation model was unable to establish a correspondence between the rainfall amounts and their corresponding discharge values. Consequently, an appropriate modeling with the observed data could not be achieved, as evidenced by the NSE coefficient values of 0.52 and -0.01 for daily calibration and validation periods, respectively, and 0.76 and 0.82 for monthly periods.
Additionally, it was observed that in the daily hydrological evaluation, the performance of the PERSIANN-CDR dataset was better, with NSE values of 0.29 and 0.59 for the calibration and validation periods, respectively. In contrast, the ERA5 dataset displayed superior capabilities in monthly hydrological evaluation for rainfall-runoff modeling, with calibration and validation coefficients of 0.77 and 0.82, respectively.
Pollen-derived fatty acids and amino acids mediate variance in pollinator visitation
Authors: Murray Anne F. , Leonhardt Sara D. , Stout Jane C. , Ruedenauer Fabian A. , Vanderplanck Maryse , Russo Laura
Published: September 2024

Pollinators help maintain functional landscapes and are sensitive to floral nutritional quality. Both proteins and lipids influence pollinator foraging, but the role of individual biochemical components in pollen remains unclear. We conducted an experiment comprising common garden plots of six plant species (Asteraceae, Rosaceae, Onagraceae, Boraginaceae, and Plantaginaceae). These plots were treated with low concentrations of agrochemicals, including fertilizer, herbicide, and a combination of both to induce intra-specific variation in floral chemistry. We recorded insect visitation to inflorescences over two years and eight sites in Dublin, Ireland. We analyzed the pollen amino acid and fatty acid content, quantifying the concentrations of 51 fatty acids and 17 amino acids of the six focal plant species across the four agrochemical treatments. We tested relationships between the pollen composition and the insect visitation matrix as well as an insect trait matrix including sociality, body size, nesting behavior, and whether the insect was a bee or hoverfly. We found: i) the agrochemical treatments did not affect the biochemical composition of the pollen; ii) there were many strong associations between fatty acids, amino acids, insect traits, and visitation; and iii) specific compounds with strong associations (montanic acid, cysteine, and proline) explained more of the variance in insect abundance (honeybees, bumble bees, and hoverflies) than the total amino acid or fatty acid concentrations in the pollen. Our results suggest it is important to evaluate the contribution of individual biochemical compounds in pollen to insect visitation, and also that different insect species respond to different pollen compounds.
Resettlement and the Agricultural Productivity of Origin Locations
Authors: Gebresilasse Mesay
Published: September 2024

How do large out-migration shocks affect agricultural productivity in origin locations? Planned resettlements, forced migration, and climate change have resulted in various relocation schemes across the globe. In this paper, I examine the effects of large-scale out-migration on origin locations considering a government-mandated resettlement in Ethiopia that relocated close to 600,000 people from densely-populated and drought-prone areas. Using a difference-indifference strategy that exploits variation in out-resettlement across districts with similar environmental stressors, I find that agricultural productivity increases relatively in districts with a greater share of out-resettled population. These effects persist for several years after the end of the resettlement program. The results suggest that improvements in land distribution facilitated by the resettlement helped to lessen land degradation and increase agricultural productivity.
JEL Codes: O12, O13, O15, O21, Q12, Q15, Q18, Q24, Q32
A Causal Intercomparison framework unravels precipitation drivers in Global Storm-Resolving Models
Authors: Ricard Lucile , Beucler Tom , Stephan Claudia , Nenes Athanasios
Published: September 2024

Correctly representing convective precipitation remains a long-standing problem in climate models, due to its highly parameterized nature and unclear role of drivers interacting over a wide range of spatial scales. We analyze and compare simulations of Global Storm-Resolving Models, using a methodology based on dimensionality reduction and causal inference, to unravel the contribution of large-scale variables and storm-scale dynamics on precipitation distribution. We derive regions of Column Relative Humidity (CRH), which exclude sharp humidity gradients and help define coherent thermodynamic environments, which are subsequently found to control precipitation throughout half of the tropics. The control of CRH on precipitation is notably amplified by considering explicitly the intermediate role of the convective area. Moreover, the effect values are consistent across models and quantiles, which could be further employed to constrain GCMs. Our results show that the most extreme intensities (99.9th percentile) cannot be adequately represented without high-resolution data on vertical velocity. However, their effect on precipitation varies considerably across models and precipitation quantiles, making it more difficult to develop a constraint on storm-scale control.
Real-Time Infrared Pedestrian Recognition Model on Edge Devices
Authors: Liu Li , Huang Kaiye , Li Yujian , Zhang Chuxia , Zhang Shuo , Hu Zhengyu
Published: September 2024

Aiming at the lightweight and real-time detection requirements of infrared pedestrians on edge devices, this paper proposes an improved infrared pedestrian recognition model based on YOLOv5n. The model effectively reduces the number of parameters by introducing the GhostConv and SlimNeck modules in the lightweight design. To enhance real-time performance, the model reduces the number of parameters and improves operation speed through the PAGCP pruning strategy. After experimental analysis, a balance between operation speed and accuracy is achieved. The results show that compared to the original model, the proposed model decreases the number of parameters by 1.52M, increases running speed by 21%, and decreases mAP.50 by only 0.4%. The running speed on Jetson Nano reaches 0.12s per image. The proposed model effectively ensures high detection accuracy under lightweight and real-time requirements, providing technical support for the deployment of edge devices in scenarios such as autonomous driving.
Microfibers degradation processes in water under simulated solar irradiation: a small- and wide-angle X-ray scattering study
Authors: Piccinini Astra , Lucia Giulia , Colarossi Daniele , Principi Paolo , Amenitsch Heinz , Pittura Lucia , Regoli Francesco , Gorbi Stefania , Spinozzi Francesco
Published: September 2024

Microfibers are pollutants of significant concern, reaching aquatic environments and posing threats to living organisms. In these environments, microfibers undergo various degradation processes, reducing their size and enabling interactions with biological systems. Photodegradation is a primary driver, leading to fiber fragmentation and shrinkage. This study investigates the impact of UV irradiation on five different microfibers (cotton, cellulose acetate, polyamide, polyester, linen) in seawater and freshwater, simulating one year of sunlight exposure using a solar simulator. Structural changes at the nanoscopic level are analyzed through synchrotron small- and wide-angle X-ray scattering experiments, employing a novel model that describes macrofibrils as bundles of microfibrils. This model enables the determination of macrofibril diameter, flexibility, and length, as well as microfibril radius, spacing, and packing order. Results show cotton and polyester in seawater experience significant macrofibril enlargement, likely due to increased microfibril spacing. Cellulose acetate remains stable in seawater but shows increased microfibril dimensions in freshwater. Polyamide exhibits minimal structural changes, indicating low sensitivity to UV irradiation. Linen, uniquely, shows a reduction in macrofibril size in both water types, suggesting a potential for fragmentation. These findings highlight the diverse and complex responses of microfibers to UV irradiation, emphasizing the importance of considering material-specific behaviors in assessing environmental persistence and potential ecological impact.
Effective cataract surgical and refractive error coverages in the State of Qatar
Authors: Ashwal Shadi M Al , Rabiu Muhammad Mansur , Mccormick Ian , AlQahtani Omar , Algadi Halla Salih , Kokku Suresh Babu , Al-Thani Mohamed Hamad
Published: September 2024

Purpose
To evaluate the progress in Qatar's eye care since 2009, focusing on effective cataract surgical and refractive error coverages, leading to enhance eye health strategies and action plans.
Methods
A modified Rapid Assessment of Avoidable Blindness (RAAB) survey was employed using multi-stage sampling in all persons 50 years and older in Qatar. The study focused on uncorrected refractive errors, cataract surgery coverage and effectiveness, and visual acuity assessment, with secure data gathering.
Results
There were 339 individuals out of 3,206 examined participants who underwent cataract surgery out of which 66.1% of the operated eyes obtained ‘Good’ post-operative outcomes (Vision ≥ 6/12). The Effective Cataract Surgical Coverage (eCSC) was 61.2%, which is on par with developed countries. WHO set a global target for countries to achieve a 30 percentage-point increase in eCSC by 2030, accordingly Qatar's target should be 91.2% by 2030. Additionally, the study found that the Refractive Error Coverage and the effective Refractive Coverage across were 77.8% and 74.3%, respectively. Notably, these rates were lowest among Qatari women.
Conclusion
Qatar's eye care services have shown an improvement since the 2009 RAAB, with disparities in outcomes based on gender and nationality. Continued efforts are needed to meet WHO standards and reduce quality gaps, particularly among Qataris.
Assessing Landslide Susceptibility in Chefchaouen, North-West Morocco Using Remote Sensing and GIS: A Weighted Overlay Analysis Approach
Authors: Dahmani Lahcen , Laaribya Said , NAIM Hafida , DINDAROGLU Turgay
Published: September 2024

This study maps landslide susceptibility in the Chefchaouen region of northwestern Morocco using remote sensing and Geographic Information Systems (GIS) with a weighted overlay analysis approach. Field data collected from five strategically selected sites were used to validate the model's results. Key environmental factors considered include slope, aspect, elevation, vegetation cover, and soil characteristics. The results show a Kappa coefficient of 0.72, indicating substantial agreement between observed and expected accuracies. The user's and producer's accuracy for low susceptibility areas is 75%, for medium susceptibility areas is 85%, and for high susceptibility areas is 80%. These findings indicate a good match between the model's predictions and actual field conditions. Validation of the Landslide Susceptibility Zoning (LSZ) map revealed that most high-risk areas are located on steep slopes in the northern and western regions of Chefchaouen. Nine major landslide-prone zones were identified, providing crucial information for stakeholders to formulate effective mitigation measures. Despite several limitations, such as the temporal scope of data and the resolution of satellite imagery, this research offers significant insights into landslide risk management in the Chefchaouen region. Future perspectives include integrating long-term climate change scenarios and improving methodology to better capture local geological complexity. This study emphasizes the importance of sustainable land and vegetation management practices to reduce landslide risks.
Real-Time Evaluation of the Improved Eagle Strategy Model in the Internet of Things
Authors: Rajendran Venushini , Ramasamy R Kanesaraj
Published: September 2024

With the rapid expansion of cloud computing and the pervasive growth of IoT across industries and educational sectors, the need for efficient remote data management and service orchestration has become paramount. Web services, facilitated by APIs, offer a modular approach to integrating and streamlining complex business processes. However, real-time monitoring and optimal service selection within large-scale, cloud-based repositories remain significant challenges. This study introduces the Improved Eagle Strategy (IES) hybrid model, designed to drastically reduce computation time while ensuring accurate service selection tailored to specific user requirements. Through comprehensive NetLogo simulations, the IES model demonstrates superior efficiency in service selection compared to existing methodologies. Additionally, the IES model's application through a web dashboard system highlights its capability to manage both functional and non-functional service attributes effectively. When deployed on real-time IoT devices, the IES model not only enhances computation speed but also ensures a more responsive and user-centric service environment. This research underscores the transformative potential of the IES model in optimizing cloud computing processes, particularly within the IoT ecosystem.
Comparing sparse Llama 3 and Llama 2 models for on-device AI assistants
Authors: Liu Bo
Published: September 2024

Sparse connectivity is ubiquitous in biological neural networks. In the machine learning field, it has a long history to train dense neural networks then prune them for sparse connectivity. Recently, this research direction was extended to pruning and sparsifying large language models (LLMs), for which many numerical methods have been invented. Previously, we adopted one of such methods to prune the Llama-2 7B model, in order to develop small-sized language models for on-device AI assistants. We figured out that its sparsity limit is 60%, at which the pruned Llama-2 7B model could generate fluent and helpful answers to daily queries despite occasional factual inaccuracies. In this article, we extended previous pruning and evaluation methods to the newly-released Llama-3 8B model. We found that this Llama 3 model has a sparsity limit of 53%, at which it produced fewer factual inaccuracies than Llama 2 at the sparsity limits but required more parameters. We hypothesized that the accuracy improvement is due to the longer context window and larger training dataset used in Llama 3, while the increased parameter requirement is due to its versatility in generating both text and code output. Based on these results and insights, we proposed future directions to boost the model performance after pruning, paving the way for lightweight LLMs as on-device AI assistants.
A new method for calculating the food self-sufficiency ratio: Supply-side food self-sufficiency ratio
Authors: Takahashi Goro
Published: September 2024

Background: The conventional formula for calculating food self-sufficiency cannot cover all the food we eat on a daily basis, and the food self-sufficiency ratios (FSSR) of each country cannot be calculated. The conventional food self-sufficiency ratio (CFSSR) can only calculate the FSSRs of each country for grains. To determine the actual state of food insecurity worldwide as accurately as possible, a method for calculating the FSSR of each country for all the foods we eat on a daily basis is needed. To address this situation, this study proposes the supply-side food self-sufficiency ratio (SSFSSR), which can systematically calculate the self-sufficiency ratio of all foods in all countries/regions.
Results: We compared the results of both calculations under the same conditions and used the same data to determine whether the CFSSR or the SSFSSR is a more suitable method for obtaining basic information and formulating measures of global food security. The results showed that the SSFSSR has advantages and practicality over the CFSSR. The SSFSSR can calculate self-sufficiency ratios for all foods in all countries/regions of the world, and the figures for various statistical tests are better. The food that is the subject of the calculation in the SSFSSR formula is the entire supply from production, distribution, storage, and consumption, excluding duplication in the calculation, and includes primary products required to produce secondary products, such as livestock products and edible oils. The study also highlighted the value of reducing the amount of primary products used to produce secondary products such as livestock and edible oils, thereby lowering the primary product conversion rate (PPCR).
Conclusion: This study used actual data to estimate the SSFSSR for each country/region to demonstrate the applicability of this method and that lowering the PPCR would lead to an increase in the food self-sufficiency ratio. To further refine this methodology, we find that the most important tasks for the future are to collect more reliable data on calories per weight for a large number of foods, expand the number of types covered by more reliable PPCRs, and analyze those data.
Exploring Personal Experience and Value Creation in Postdigital Education: Insights from a Large-Scale MOOC Survey.
Authors: Soleymani Ali , De Laat Maarten , Specht Marcus
Published: September 2024

This study investigates students’ online learning experiences based on their perception of the value this course creates for them through a comprehensive analysis of responses from 1227 participants in MOOCs offered by the Extension School of the Technical University of Delft. Utilizing the value creation framework by Wenger, Trayner, and De Laat (2011), the research explores the immediate, potential, applied, realized, and transformative value creation cycles. Our findings reveal significant insights into the multifaceted impacts of study behavior on learners’ perceptions. Participants reported benefits such as skill acquisition, professional development, and enhanced confidence while highlighting areas needing improvement, such as practical application opportunities and course relevance. This study highlights the importance of aligning MOOC content with learner needs and providing ongoing support to maximize the educational value online courses can offer them. These insights contribute to understanding educational value in the postdigital age, advocating for the development and support of MOOCs to foster continued personal and professional growth.
Transitioning from Wet Lab to Artificial Intelligence: A Systematic Review of AI predictors in CRISPR
Authors: Abbasi Ahtisham Fazeel
Published: September 2024

The revolutionary CRISPR-Cas9 system leverages a programmable guide RNA (gRNA) and Cas9 proteins to precisely cleave problematic regions within DNA sequences. This groundbreaking technology holds immense potential for the development of targeted therapies for a wide range of diseases, including cancers, genetic disorders, and hereditary diseases. CRISPR-Cas9 based genome editing is a multi-step process such as designing a precise gRNA, selecting the appropriate Cas protein, and thoroughly evaluating both on-target and off-target activity of the Cas9-gRNA complex. To ensure the accuracy and effectiveness of CRISPR-Cas9 system, after the targeted DNA cleavage, the process requires careful analysis of the resultant outcomes such as indels and deletions. Following the success of artificial intelligence (AI) in various fields, researchers are now leveraging AI algorithms to catalyze and optimize the multi-step process of CRISPR-Cas9 system. To achieve this goal AI driven applications are being integrated into each step, but existing AI predictors have limited performance and many steps still rely on expensive and time-consuming wet-lab experiments. The primary reason behind low performance of AI predictors is the gap between CRISPR and AI fields. Effective integration of AI into multi-step CRISPR-Cas9 system demands comprehensive knowledge of both domains. This paper bridges the knowledge gap between AI and CRISPR-Cas9 research. It offers a unique platform for AI researchers to grasp deep understanding of the biological foundations behind each step in the CRISPR-Cas9 multi-step process. Furthermore, it provides details of 80 available CRISPR-Cas9 system-related datasets that can be utilized to develop AI-driven applications. Within the landscape of AI predictors in CRISPR-Cas9 multi-step process, it provides insights of representation learning methods, machine and deep learning methods trends, and performance values of existing 50 predictive pipelines. In the context of representation learning methods and classifiers/regressors, a thorough analysis of existing predictive pipelines is utilized for recommendations to develop more robust and precise predictive pipelines.
Canonical correlations between morphoagronomic and production traits in traditional cowpea varieties based on genotypic values
Authors: de Souza Suzianne Maria Silva , Silva Suzy Anne de Araújo e , Santos Vanderley Borges dos , Nascimento Luan de Oliveira , Biase Adriele Giaretta
Published: September 2024

Obtaining information about the main morphoagronomic and yield components, as well as their interrelationships, is of utmost importance in cowpea breeding programs, aiming at the selection of superior genotypes. The analysis of canonical correlations allows us to predict and describe the relationship between groups of characters, allowing the indirect selection of superior genotypes. The objective of this work was to verify relationships between morphological characters and production components through the analysis of canonical correlations using genotypic values obtained via mixed REML/BLUP models in traditional cowpea varieties. The experiment was conducted in a randomized block design with three replications and 12 treatments. Traditional varieties were characterized and evaluated during the germination, seedling, flowering and harvest stages with data obtained from plants, pods and seeds. Using the genotypic correlation matrix, two sets of characteristics were established, Group I, formed by production components and Group II, formed by morphoagronomic characters. A second correlation analysis was performed to verify associations between morphoagronomic and seed traits (seed length, width and thickness). It was found that dependence between the groups evaluated and intergroup associations can be established, which allows the study of cause-effect and the practice of indirect selection between groups of variables studied. An increase in pod length, hundred-grain mass and seed size is achieved through the selection of early maturing plants, with greater width of the apical leaflet and length of the hypocotyl.