bluuebunny/crossref_metadata_2025 huggingface dataset:
A Comprehensive Systematic Review for Cardiovascular Disease using Machine Learning Techniques
Authors: D. S. Aabdalla Islam , Vasumathi Dr.D
Published: February 2024

The global upswing in cardiovascular disease (CVD) cases presents a critical challenge. While the ultimate goal remains elusive, improving CVD prediction accuracy is vital. Machine learning and deep learning are crucial for decoding complex health data, enhancing cardiac imaging, and predicting disease outcomes in clinical practice. This systematic literature review meticulously analyses CVD using machine learning techniques, with a particular emphasis on algorithms for classification and prediction. The metaanalysis covers 343 references from 2020 to November 2023, preceding a thorough examination of 65 selected references. Acknowledging current hurdles in CVD classification methods that impede practical use, this systematic literature review (SLR) is conducted. The study provides valuable insights for researchers and healthcare professionals, facilitating the integration of clinical applications in machine learning settings related to CVD. It also aids in promptly identifying potential threats and implementing precautionary measures. The study also recognizes prevalent classical machine learning methods, emphasizing their clinically relevant diagnostic outcomes. Deliberating on current trends, algorithms, and potential areas for future research offers a comprehensive insight into the present state of affairs.
Ensemble Learning Approach for Digital Communication Modulation’s Classification
Authors: Benremdane Yahya , Jamal Said , Taheri Oumaima , Lakziz Jawad , Ouaskit Said
Published: February 2024

This work uses artificial intelligence to create an automatic solution for the modulation's classification of various radio signals. This project is a component of a lengthy communications intelligence process that aims to find an automated method for demodulating, decoding, and deciphering communication signals. As a result, the work we did involved selecting the database required for supervised deep learning, assessing the performance of current methods on unprocessed communication signals, and suggesting a deep learning network-based method that would enable the classification of modulation types with the best possible ratio between computation time and accuracy. In order to use the current automatic classification models as a guide, we first conducted study on them. As a result, we suggested an ensemble learning strategy based on Transformer Neural Network and adjusted ResNet that takes into account the difficulty of forecasting in low Signal Noise Ratio (SNR) scenarios while also being effective at extracting multiscale characteristics from the raw I/Q sequence data. Ultimately, we produced an architecture for communication signals that is simple to work with and implement. With an accuracy of up to 95%, this solution's optimum and sturdy architecture decides the type of modulation on its own.
Foundations of ANNs: Tolstoy’s Genius Explored using Transformer Architecture
Authors: Guliyev Shahriyar
Published: February 2024

Artificial Narrow Intelligence is in the phase of moving towards the AGN, which will attempt to decide as a human being. We are getting closer to it by each day, but AI actually is indefinite to many, although it is no different than any other set of mathematically defined computer operations in its core. Generating new data from a pre-trained model introduces new challenges to science & technology. In this work, the design of such an architecture from scratch, solving problems, and introducing alternative approaches are what has been conducted. Using a deep thinker, Tolstoy, as an object of study is a source of motivation for the entire research.
Wavelet Scattering Transform for ECG Cardiovascular Disease Classification
Authors: D. S. Aabdalla Islam , Vasumathi D
Published: February 2024

Classifying the ECG dataset is the main technique for diagnosing heart disease. However, the focus of this field is increasingly on prediction, with a growing dependence on machine learning techniques. This study aimed to enhance the accuracy of cardiovascular disease classification using data from the PhysioNet database by employing machine learning (ML). The study proposed several multi-class classification models that accurately identify patterns within three classes: heart failure rhythm (HFR), normal heart rhythm (NHR), and arrhythmia (ARR). This was accomplished by utilizing a database containing 162 ECG signals. The study employed a variety of techniques, including frequency-time domain analysis, spectral features, and wavelet scattering, to extract features and capture unique characteristics from the ECG dataset. The SVM model produced a training accuracy of 97.1% and a testing accuracy of 92%. This work provides a reliable, effective, and human error-free diagnostic tool for identifying heart disease. Furthermore, it could prove to be a valuable resource for future medical research projects aimed at improving the diagnosis and treatment of cardiovascular diseases.
A Machine Learning Ensemble Model for the Detection of Cyberbullying
Authors: Alqahtani Abulkarim Faraj , Ilyas Mohammad
Published: February 2024

The pervasive use of social media platforms, such as Facebook, Instagram, and X, has significantly amplified our electronic interconnectedness. Moreover, these platforms are now easily accessible from any location at any given time. However, the increased popularity of social media has also led to cyberbullying.It is imperative to address the need for finding, monitoring, and mitigating cyberbullying posts on social media platforms. Motivated by this necessity, we present this paper to contribute to developing an automated system for detecting binary labels of aggressive tweets.Our study has demonstrated remarkable performance compared to previous experiments on the same dataset. We employed the stacking ensemble machine learning method, utilizing four various feature extraction techniques to optimize performance within the stacking ensemble learning framework. Combining five machine learning algorithms,Decision Trees, Random Forest, Linear Support Vector Classification, Logistic Regression, and K-Nearest Neighbors into an ensemble method, we achieved superior results compared to traditional machine learning classifier models. The stacking classifier achieved a high accuracy rate of 94.00%, outperforming traditional machine learning models and surpassing the results of prior experiments that utilized the same dataset. The outcomes of our experiments showcased an accuracy rate of 0.94% in detection tweets as aggressive or non-aggressive.
Effective Multi-Stage Training Model for Edge Computing Devices in Intrusion Detection
Authors: Huynh Trong Thua , Nguyen Hoang Thanh
Published: February 2024

Intrusion detection poses a significant challenge within expansive and persistently interconnected environments. As malicious code continues to advance and sophisticated attack methodologies proliferate, various advanced deep learning-based detection approaches have been proposed. Nevertheless, the complexity and accuracy of intrusion detection models still need further enhancement to render them more adaptable to diverse system categories, particularly within resource-constrained devices, such as those embedded in edge computing systems. This research introduces a three-stage training paradigm, augmented by an enhanced pruning methodology and model compression techniques. The objective is to elevate the system's effectiveness, concurrently maintaining a high level of accuracy for intrusion detection. Empirical assessments conducted on the UNSW-NB15 dataset evince that this solution notably reduces the model's dimensions, while upholding accuracy levels equivalent to similar proposals.
Feature Selection using the Concept of Peafowl Mating in IDS
Authors: Ghosh Partha , Sharma Joy , Pandey Nilesh
Published: February 2024

Cloud computing has high applicability as an Internet based service that relies on sharing computing resources. Cloud computing provides services that are Infrastructure based, Platform based and Software based. The popularity of this technology is due to its superb performance, high level of computing ability, low cost of services, scalability, availability and flexibility. The obtainability and openness of data in cloud environment make it vulnerable to the world of cyber-attacks. To detect the attacks Intrusion Detection System is used, that can identify the attacks and ensure information security. Such a coherent and proficient Intrusion Detection System is proposed in this paper to achieve higher certainty levels regarding safety in cloud environment. In this paper, the mating behavior of peafowl is incorporated into an optimization algorithm which in turn is used as a feature selection algorithm. The algorithm is used to reduce the huge size of cloud data so that the IDS can work efficiently on the cloud to detect intrusions. The proposed model has been experimented with NSL-KDD dataset as well as Kyoto dataset and have proved to be a better as well as an efficient IDS.
Comparative Study of Orchestration using gRPC API and REST API in Server Creation Time: An Openstack Case
Authors: S M Hari Krishna , Sharma Rinki
Published: February 2024

Cloud computing is the quick, simple, and economical distribution of computing services. To offer quality services in cloud infrastructure, the NFV Management and Orchestration (MANO) function is a critical task for managing both infrastructure resources and network functions. Since the demand for cloud services is rapidly increasing, cloud service providers are constantly trying to reduce operational costs. Hence the elements of MANO functions have gained critical importance in cloud infrastructure. To use MANO efficiently, there are hypervisors (OpenStack) that act as a middle layer between hardware and software that are used to provide Infrastructure-as-a-Service solutions through a set of interrelated services. These services are managed by an application programming interface (API). The existing orchestration method uses Representational state transfer (REST) API for communicating with core components of a hypervisor. These API have been integral part of creating and deploying a server. To improve the performance, this paper proposes a novel approach for orchestration based on the opensource remote procedure call framework, known as gRPC. To analyse the proposed solution, both REST and gRPC-based orchestration methods are implemented for creating and launching servers using OpenStack cloud computing platform. The server creation time is obtained for different scenarios and SFC use cases. The results show that by using gRPC, the performance in terms of server creation time of an orchestration is improved by up to 27% compared to the traditional REST-based orchestration.
Adaptive Multi-Criteria-Based Load Balancing Technique for Resource Allocation in Fog-Cloud Environments
Authors: A. A. Gad-Elrab Ahmed , S. Alsharkawy Almohammady , E. Embabi Mahmoud , Sobhi Ahmed , A. Emara Farouk
Published: February 2024

Recently, to deliver services directly to the network edge, fog computing, an emerging and developing technology, acts as a layer between the cloud and the IoT worlds. The cloud or fog computing nodes could be selected by IoTs applications to meet their resource needs. Due to the scarce resources of fog devices that are available, as well as the need to meet user demands for low latency and quick reaction times, resource allocation in the fog-cloud environment becomes a difficult problem. In this problem, the load balancing between several fog devices is the most important element in achieving resource efficiency and preventing overload on fog devices. In this paper, a new adaptive resource allocation technique for load balancing in a fog-cloud environment is proposed. The proposed technique ranks each fog device using hybrid multi-criteria decision- making approaches Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy Technique for Order Performance by Similarity to Ideal Solution (FTOPSIS), then selects the most effective fog device based on the resulting ranking set. The simulation results show that the proposed technique outperforms existing techniques in terms of load balancing, response time, resource utilization, and energy consumption. The proposed technique decreases the number of fog nodes by 11%, load balancing variance by 69% and increases resource utilization to 90% which is comparatively higher than the comparable methods.
Traffic Light Control Systems: Requirements Engineering
Authors: Audat Ahmad , Yahia Marah , Ghoul Said , Younes Maram Bani
Published: February 2024

Requirements Engineering (RE) is the most important activity and pivot phase in the software development life cycle. It consists mainly of requirements analysis, design, and specification. The application of RE in a business domain aims to generalize its different specific approaches by meta-models under which each specific approach is an instance or specialization. RE is being applied more and more in different business domains, and its application benefits are found to be valuable. Despite these active RE applications and their added values in different domains, the Traffic Light Control (TLC) domain has not yet been approached, regardless of its everyday interest. The work presented in this paper is part of a TLC enhancement project that explores RE's potential contributions to leveraging TLC quality. Thus, in this work, different recent specific approaches to TLC are analyzed, and a large part of this business domain's functional/ non-functional requirements are elicited and specified. Moreover, the traffic context-aware traffic light control systems (CTLC) are also considered. They are deeply analyzed and compared to the typical TLC systems. As a primary result, the tangling of different concerns was stated, and a separate concerns paradigm was applied. This leads to requirements specification through separated agents. This should lead to TLC and CTLC systems development and maintenance cost reduction. An important agent dealing with security aspects and their evolving technologies is introduced. The impact evaluations of the RE on some TLC and CTLC current approaches are also presented.
Optimizing Hyperparameters for Enhanced Email Classification and Forensic Analysis with Stacked Autoencoders
Authors: Thomas Merly , Meshram B. B
Published: February 2024

Electronic mail, commonly known as email, is a crucial technology that enables streamlined operations and communications in corporate environments. Empowering swift and dependable transactions, email is a driving force behind heightened productivity and organizational effectiveness. However, its versatility also renders it susceptible to misuse by cybercriminals engaging in activities such as hacking, spoofing, phishing, email bombing, whaling, and spamming. As a result, effective and efficient data analysis is important in avoiding and detecting cyber-attacks and crime on times. To overcome the above challenges, a novel approach named Aquila Optimization (AO) is used in this paper to find the best set of hyperparameters of the Stacked Auto Encoder (SAE) classifier. The purpose of increasing the hyperparameters of the SAE using the AO is to obtain a higher text classification accuracy. Then the optimized SAE classifies the selected features into different classes. The experimental results showed that the proposed AO-SAE model outperforms the existing models such as Logistic Regression (LR) and Long Short-Term Model based Gated Current Unit (LSTM based GRU) in terms of Accuracy.
On Approach to Increase Density of Fieldeffect Heterotransistors in the Framework of a Current Follower Transconductance Amplifier. Influence of Mismatchinduced Stress and Porosity of Materials on Technological Process
Authors: L. Pankratov Evgeny
Published: February 2024

In this paper we introduce an approach to increase density of field-effect heterotransistors in the framework of a current follower transconductance amplifier. In the framework of the approach we consider manufacturing the amplifier in heterostructure with specific configuration. Several required areas of the heterostructure should be doped by diffusion or ion implantation. After that dopant and radiation defects should by annealed framework optimized scheme. We also consider an approach to decrease value of mismatch-induced stress in the considered heterostructure. We introduce an analytical approach to analyze mass and heat transport in heterostructures during manufacturing of integrated circuits with account mismatch-induced stress.
An Intelligent Apitesting: Unleashing the Power of AI
Authors: Khankhoje Rohit
Published: February 2024

In the continually evolving domain of software development, guaranteeing the dependability and functionality of Application Programming Interfaces (APIs) is of utmost importance. Traditional approaches to API testing frequently encounter difficulties in keeping up with the dynamic nature of APIs, resulting in inefficiencies and overlooked defects. This research paper investigates the transformative potential of Artificial Intelligence (AI) in API testing, ushering in a new era of intelligent testing. Intelligent API testing harnesses the capabilities of AI to enhance the efficiency, precision, and adaptability of the testing process. API driven techniques enable the production of diverse and realistic test data, ensuring comprehensive test coverage. Furthermore, AI-powered algorithms can anticipate potential issues, identify anomalies, and optimize test case selection, all while adapting to evolving API schemas. This research paper delves into the various aspects of intelligent API testing, encompassing data generation, tools and technologies, benefits and impact, challenges, and real-world use cases. We illustrate how AI empowers testers to discover subtle defects, streamline testing endeavors, and enhance the overall quality of APIdriven applications. As we navigate the era of digital transformation, intelligent API testing emerges as an essential tool in the software development toolkit, enabling organizations to deliver robust and resilient APIs that fulfill the demands of contemporary applications. Embracing AI in API testing not only holds the promise of expediting the development lifecycle but also ensures that APIs remain agile and reliable in an ever-changing digital landscape.
Enhancing Web Accessibility - Navigating the Upgrade of Design Systems from WCAG 2.0 to WCAG 2.1
Authors: Shah Hardik
Published: February 2024

In this research, we explore the vital transition of Design Systems from Web Content Accessibility Guidelines (WCAG) 2.0 to WCAG 2.1, emphasizing its role in enhancing web accessibility and inclusivity in digital environments. The study outlines a comprehensive strategy for achieving WCAG 2.1 compliance, encompassing assessment, strategic planning, implementation, and testing, with a focus on collaboration and user involvement. It also addresses the challenges in using web accessibility tools, such as their complexity and the dynamic nature of accessibility standards. The paper looks forward to the integration of emerging technologies like AI, ML, NLP, VR, and AR in accessibility tools, advocating for universal design and user-centered approaches. This research acts as a crucial guide for organizations aiming to navigate the changing landscape of web accessibility, underscoring the importance of continuous learning and adaptation to maintain and enhance accessibility in digital platforms.
Psychosocial and digital intervention for patients with schizophrenia
Authors: Kim Sung-Wan , Baek Seon-Hwa , Cho Chul-Hyun
Published: February 2024

Background: Schizophrenia is a severe mental disorder that significantly impairs the daily functioning of patients that causes substantial societal and economic burden. While drug therapy is crucial for schizophrenia treatment, relying solely on antipsychotic drugs does not yield the most favorable therapeutic outcomes.Current Concepts: Integrating psychosocial interventions could result in maximized therapeutic effects for individuals living with schizophrenia. Evidence-based psychosocial interventions for schizophrenia management include cognitive behavioral therapy, family involvement, psychoeducation, supported employment programs, lifestyle adjustments for physical health, and coordinated and comprehensive early intervention services. Incorporating peer support, allowing both service users and care providers to share knowledge and their illnessrelated personal experiences, facilitates mutual assistance along the path to recovery. Moreover, growing interest follows in using digital interventions, such as wearable technology and smartphone applications, as effective therapeutic tools for illness management.Discussion and Conclusion: The above-mentioned psychosocial interventions empower individuals with schizophrenia, helping them improve their functioning, enhance their life quality, and adapt more effectively to society. Combining early medication with comprehensive psychosocial interventions, including digital technology, is crucial for the best outcomes. This approach should begin as soon as schizophrenia is diagnosed, aiming to promote recovery and overall well-being in affected individuals.
Improving Students’ Cognitive Abilities through Jarimatics Method at Al-Hidayah Negarasaka Village Negerikaton District Pesawaran District
Authors: Ariyanto Bambang , Misrodin Misrodin
Published: February 2024

This research is based on the researcher's observations and experience, that cognitive abilities through Jarimatika are not just a medium for counting, but by using the Jarimatika method, children feel happy and there is no pressure. Using the Jarimatika method can train children's counting, and increase children's numeracy skills. Based on the background of the problem, the research problem was formulated, namely: whether the application of learning through the Jarimatika method is able to improve the numeracy skills of Group B RA students. Al-Hidayah, Negarasaka Village, Negerikaton District, Pesawaran Regency. This research uses a collaborative Classroom Action Research (CAR) approach. This research was carried out in two cycles, using instruments in the form of a calculation work sheet using the Jarimatika method and a teacher observation sheet. The improvement can be seen from the children's learning completeness in developing gross motor skills which has increased, this can be seen from the children's learning completeness in the Pras Cycle with a total of 40% completeness. then in cycle I it increased by 46.67%, and in cycle II the percentage of children's learning completion increased to 86.67%.

Keywords: Jarimatics Method, Improving Students’ Cognitive, Numeracy Skills
Rendre opérationnel le Fonds pertes et préjudices : apprendre des bénéficiaires potentiels
Authors:   Stockholm Environment Institute, Bakhtaoui I. , Naushin N. ,   International Centre for Climate Change and Development and Stockholm Environment Institute, Shawoo Z. , Binte Mirza A. , Saify Iqbal S. M. , Hossain F.
Published: February 2024

Ce rapport s’appuie sur les observations des représentants et des collaborateurs des gouvernements et organisations des pays du Sud susceptibles de demander à bénéficier du Fonds pertes et préjudices.
Teaching food biotechnology in a university engineering program: blended and in-person approaches in Ecuador
Authors: Díaz-Monroy Byron , Baquero-Tapia María Fernanda , Díaz-Arrieta Ronald
Published: February 2024

Food biotechnology is a modern science that generates interesting goods and services for society, it is part of the curriculum of agribusiness engineering careers in Ecuadorian universities. The objective was to determine the influence of the teaching system (in-person and blended) on learning levels. The sample consisted of four hundred students of the fifth level of the aforementioned career, divided into 8 different academic semester periods, during 4 consecutive years (2018, 2019, 2020, and 2021), four in an in-person system and four in a blended system (in-person and online) of an Ecuadorian public university, through a quasi-experimental type of research. The evaluation was carried out using both the grade records obtained by the students, as well as the application of a theoretical and practical test at the end of the course. The data were analyzed using descriptive statistics and a Student's t-test (P≤0.05). It was found that the in-person education system allows 72 % of learning of Biotechnology knowledge while the blended system improves it by up to 86 % with statistical differences between them, so Biotechnology is better understood and applied through a blended system of education.
Development and characterization of a high-performance single-particle aerosol mass spectrometer (HP-SPAMS)
Authors: Du Xubing , Xie Qinhui , Huang Qing , Li Xuan , Yang Junlin , Hou Zhihui , Wang Jingjing , Li Xue , Zhou Zhen , Huang Zhengxu , Gao Wei , Li Lei
Published: February 2024

Abstract. This study describes a high-performance single-particle mass spectrometry (HP-SPAMS) design in detail. The comprehensive improvements in the injection system, optical sizing system, mass spectrometry, and data acquisition system have improved particle detection efficiency and chemical analysis. The combination of an aerodynamic particle concentrator (APC) system and a wide range of aerodynamic lenses (ADLs) enables the concentration of particles in the 100–5000 nm range. Using an APC increases the instrument inlet flow by a factor of 3–5. The ion delayed-exaction technology of bipolar time-of-flight mass spectrometry improves the mass resolution by 2–3 times, allowing the differentiation of isobaric ions of different substances. Moreover, the four-channel data acquisition technology greatly enhances the dynamic range of mass spectrometry. The improved HP-SPAMS enhances the overall capability of the instrument in terms of particle detection number and scattering efficiency. Moreover, it improves accuracy and sensitivity for component identification of individual particles. The experimental performance of HP-SPAMS shows that the scattering efficiency of polystyrene latex microspheres is almost 70 %–100 % in the range of 300–3000 nm. Compared to the previous SPAMS, HP-SPAMS has a larger inlet flow rate and scattering efficiency and a higher laser frequency, which makes HP-SPAMS increase the effective number of particles detected and improve the temporal resolution of detection. For the analysis of individual particles, HP-SPAMS achieves an average mass spectral resolution of 2500 at m/z 208, which helps distinguish between most organic fragment ions and metal ions and facilitates the analysis of complex aerosol particles. For the analysis of individual particles, the increased resolution of the HP-SPAMS contributes to the differentiation of most organic fragment ions and metal ions and facilitates the evaluation of complex aerosol particles, in the case of atmospheric lead-containing particles. The improved detection efficiency and chemical analysis capability of HP-SPAMS will be of great importance for low-concentration aerosol detection and complex aerosol component analysis.
BrGDGT-based seasonal paleotemperature reconstruction for the last 15 000 years from a shallow lake on the eastern Tibetan Plateau
Authors: Hou Xiaohuan , Wang Nannan , Sun Zhe , Yuan Kan , Cao Xianyong , Hou Juzhi
Published: February 2024

Abstract. Understanding Holocene temperature changes is vital for resolving discrepancies between proxy reconstructions and climate models. The intricate temperature variations across the Tibetan Plateau (TP) add complexity to studying continental climate change during this period. Discrepancies between model-based and proxy-based reconstructions might stem from seasonal biases and environmental uncertainties in the proxies. Employing multiple proxies from a single sediment core for quantitative temperature reconstructions offers an effective method for cross-validation in terrestrial environments. Here, we present an ice-free-season temperature record for the past 15 kyr from a shallow, freshwater lake on the eastern TP, based on brGDGTs (branched glycerol dialkyl glycerol tetraethers). This record shows that the Holocene Thermal Maximum lags the pollen-based July temperature recorded in the same sediment core. We conclude that the mismatch between the brGDGT-based and pollen-based temperatures is primarily the result of seasonal variations in solar irradiance. The overall pattern of temperature changes is supported by other summer temperature records, and the Younger Dryas cold event and the Bølling–Allerød warm period are also detected. A generally warm period occurred during 8–3.5 ka, followed by a cooling trend in the late Holocene. Our findings have implications for understanding the seasonal signal of brGDGTs in shallow lakes and provide critical data for confirming the occurrence of seasonal biases in different proxies from high-elevation lakes. To further investigate the significance of the brGDGTs and temperature patterns on the TP, we examined existing brGDGT-based Holocene temperature records, which interpret these compounds as indicators of mean annual or growing season temperatures. The existing and available temperature records show complicated patterns of variation, some with general warming trends throughout the Holocene, some with cooling trends, and some with a warm middle Holocene. We analyzed the possible reasons for the diverse brGDGTs records on the TP and emphasize the importance of considering lake conditions and modern investigations of brGDGTs in lacustrine systems when using brGDGTs to reconstruct paleoenvironmental conditions.
Tide-Surge Interaction near Singapore and Malaysia using a Semi-empirical Model
Authors: Koh Zhi Yang , Grandey Benjamin S. , Samanta Dhrubajyoti , Switzer Adam D. , Horton Benjamin P. , Dauwels Justin , Chew Lock Yue
Published: February 2024

Abstract. Tide-surge interaction (TSI) plays a substantial role in determining the characteristics of surges over shallow regions. A variety of approaches have been used to study TSI globally, and TSI have been found to occur in Singapore and the east coast of Peninsular Malaysia. However, the characteristics of TSI in Singapore and the east coast of Peninsular Malaysia have yet to be studied in detail. We study the TSI at seven tide gauges along the east coast of Peninsular Malaysia and Singapore. Here, we propose a modified statistical framework that accounts for irregularity in the tidal cycle. We find evidence of TSI at all seven locations, with characteristics varying smoothly along the coastline: the highest non-tidal residuals are found to occur most frequently before tidal high water at the southern region of this coastline and in Singapore, both before and after tidal high water in the middle of the coastline, and after tidal high water at the northern region. We also propose a semi-empirical model to investigate the effects of tidal phase alteration, which is one the mechanisms of TSI. Results of our semi-empirical model reveal that tidal phase alteration caused by wind-driven surges is substantial enough to generate significant change in the timing of the largest surges.
Global modeling of aerosol nucleation with an explicit chemical mechanism for highly oxygenated organic molecules (HOMs)
Authors: Shao Xinyue , Wang Minghuai , Dong Xinyi , Liu Yaman , Shen Wenxiang , Arnold Stephen , Regayre Leighton , Andreae Meinrat , Pöhlker Mira , Jo Duseong , Yue Man , Carslaw Ken
Published: February 2024

Abstract. New particle formation (NPF) involving organic compounds has been identified as an important process affecting aerosol particle number concentrations in the global atmosphere. Laboratory studies have shown that highly oxygenated organic molecules (HOMs) can make a substantial contribution to NPF, but there is a lack of global model studies of NPF with detailed HOMs chemistry. Here, we add a state-of-art biogenic HOMs chemistry scheme with 96 chemical reactions to a global chemistry-climate model and quantify the contribution to global aerosols through HOMs-driven NPF. The updated model captures the frequency of NPF events observed at continental surface sites (normalized mean bias changes from -96 % to -15 %) and shows reasonable agreement with measured rates of NPF and sub-20 nm particle growth. Sensitivity simulations show that the effect of HOMs on particle growth is more important for particle number than the effect on particle formation. Globally, organics contribute around 45 % of the annual mean vertically-integrated nucleation rate (at 1 nm) and 25 % of the vertically-averaged growth rate. The inclusion of HOMs-related processes leads to a 39 % increase in the global annual mean aerosol number burden and a 33 % increase in cloud condensation nuclei (CCN) burden at 0.5 % supersaturation compared to a simulation with only inorganic nucleation. Our work predicts a greater contribution of organic nucleation to NPF than previous studies due to the explicit HOMs mechanism and an updated inorganic NPF scheme. The large contribution of biogenic HOMs to NPF on a global scale could make global aerosol sensitive to changes in biogenic emissions.
CAMELS-Chem: augmenting CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) with atmospheric and stream water chemistry data
Authors: Sterle Gary , Perdrial Julia , Kincaid Dustin W. , Underwood Kristen L. , Rizzo Donna M. , Haq Ijaz Ul , Li Li , Lee Byung Suk , Adler Thomas , Wen Hang , Middleton Helena , Harpold Adrian A.
Published: February 2024

Abstract. Large sample datasets are transforming the catchment sciences, but there are few off-the-shelf stream water chemistry datasets with complementary atmospheric deposition, streamflow, meteorology, and catchment physiographic attributes. The existing CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) dataset includes data on topography, climate, streamflow, land cover, soil, and geology across the continental US. With CAMELS-Chem, we pair these existing attribute data for 516 catchments with atmospheric deposition data from the National Atmospheric Deposition Program and water chemistry and instantaneous discharge data from the US Geological Survey over the period from 1980 through 2018 in a relational database and corresponding dataset. The data include 18 common stream water chemistry constituents: Al, Ca, Cl, dissolved organic carbon, total organic carbon, HCO3, K, Mg, Na, total dissolved N, total organic N, NO3, dissolved oxygen, pH (field and lab), Si, SO4, and water temperature. Annual deposition loads and concentrations include hydrogen, NH4, NO3, total inorganic N, Cl, SO4, Ca, K, Mg, and Na. We demonstrate that CAMELS-Chem water chemistry data are sampled effectively across climates, seasons, and discharges for trend analysis and highlight the coincident sampling of stream constituents for process-based understanding. To motivate their use by the larger scientific community across a variety of disciplines, we show examples of how these publicly available datasets can be applied to trend detection and attribution, biogeochemical process understanding, and new hypothesis generation via data-driven techniques.
Numerical-model-derived intensity–duration thresholds for early warning of rainfall-induced debris flows in a Himalayan catchment
Authors: Dixit Sudhanshu , Siva Subramanian Srikrishnan , Srivastava Piyush , Yunus Ali P. , Martha Tapas Ranjan , Sen Sumit
Published: February 2024

Abstract. Debris flows triggered by rainfall are catastrophic geohazards that occur compounded during extreme events. Few early warning systems for shallow landslides and debris flows at the territorial scale use thresholds of rainfall intensity–duration (ID). ID thresholds are mostly defined using hourly rainfall. Due to instrumental and operational challenges, current early warning systems have difficulty forecasting sub-daily time series of weather for landslides in the Himalayas. Here, we present a framework that employs a spatio-temporal numerical model preceded by the Weather Research And Forecast (WRF) Model for analysing debris flows induced by rainfall. The WRF model runs at 1.8 km × 1.8 km resolution to produce hourly rainfall. The hourly rainfall is then used as an input boundary condition in the spatio-temporal numerical model for debris flows. The debris flow model is an updated version of Van Asch et al. (2014) in which sensitivity to volumetric water content, moisture-content-dependent hydraulic conductivity, and seepage routines are introduced within the governing equations. The spatio-temporal numerical model of debris flows is first calibrated for the mass movements in the Kedarnath catchment that occurred during the 2013 North India floods. Various precipitation intensities based on the glossary of the India Meteorological Department (IMD) are set, and parametric numerical simulations are run identifying ID thresholds of debris flows. Our findings suggest that the WRF model combined with the debris flow numerical model shall be used to establish ID thresholds in territorial landslide early warning systems (Te-LEWSs).
An experimental study on the aerodynamic loads of a floating offshore wind turbine under imposed motions
Authors: Taruffi Federico , Novais Felipe , Viré Axelle
Published: February 2024

Abstract. The rotor of a floating wind turbine is subject to complex aerodynamics due to changes in relative wind speeds at the blades and potential local interactions between blade sections and the rotor near wake. These complex interactions are not yet fully understood. Lab-scale experiments are highly relevant for capturing these phenomena and provide means for the validation of numerical design tools. This paper presents a new wind tunnel experimental setup able to study the aerodynamic response of a wind turbine rotor when subjected to prescribed motions. The present study uses a 1:148 scale model of the DTU 10 MW reference wind turbine mounted on top of a 6 degrees of freedom parallel kinematic robotic platform. Firstly, the thrust variation of the turbine is investigated when single degree of freedom harmonic motions are imposed by the platform, with surge, pitch and yaw being considered in this study. For reduced frequencies greater than 1.2, it is found that the thrust variation is amplified by up to 150 % compared to the quasi-steady value when the turbine is subject to pitch and surge motions, regardless of the amplitude of motion. A similar behaviour is also noticed under yaw motions. Secondly, realistic 6 degrees of freedom motions are imposed by the platform. The motions are derived from FAST simulations performed on the full-scale turbine coupled with the TripleSpar floater, and the tests aim at exploring the thrust force dynamics for different sea states and wind conditions, obtaining reasonable agreement with the simulations. Finally, the work shows the capabilities of an off-the-shelf hexapod to conduct hybrid testing of floating offshore wind turbines in wind tunnels, as well as its limitations in performing such tests.
Eğitim Hakkı Kavramının Ulusal ve Uluslararası Normlarda Tartışılması
Authors: Çelik Ömer
Published: February 2024

The right to education, whose development began with the existence of modern society, has become a right included in the constitutions, which has become widespread in all societies with the new nation-state constitutions based on the phenomenon of equality of people. However, the right to education continues to be a concept that is currently being discussed in the context of fundamental rights and freedoms and human rights, due to the fact that its use cannot be realized only through acceptance by states in laws. It is necessary to determine the concepts of rights and freedoms before examining the place of the concept of the right to education among fundamental rights and freedoms. The element of authority is at the core of the concept of right. This is the authority to do something, to behave in a certain way from others, or to ask for something to be done. The concept of right is defined as the interests of a person recognized and guaranteed by the legal system in accordance with the theory of interest. According to this definition, in order for interests to be considered rights, they must belong to a person and be protected by the legal order. There are also approaches that suggest that rights are different from interests and needs. According to these approaches, interests and interests may be arbitrary, but rights are principled and officially accepted powers and standards. From the point of view of the theory of will, right is an authority and voluntary superiority recognized by the legal system and guaranteed at the same time. The position of the voluntary superiority is provided by the rules of law. Therefore, the right is the sovereignty of the will, which is protected by the legal order. According to the mixed theory, which combines the theory of will and interest, the concept of right has been put forward as a protected interest by recognizing the willpower of a person. In this context, the right to education as a concept is much newer than the concept of educational assignment. Because in the beginning in modern societies, people could not use education as a right, they took it on as an obligation. It has been possible for education to be a human right only with the developments in the field of human rights. The classical bourgeois revolutions brought a certain degree of freedom of self-development to a person, but not all social segments were able to achieve this through their own means. 19. economic and social rights, which have gained importance with the understanding of the social state that has developed since the second half of the century, have also included the right to education. Key Words: The Right to Education, The Concept of the Right to Education, National Norms, International Norms
Preface
Authors: Stiefel Léa , Currie Morgan , Musiani Francesca , Sandoz Alain , Silvast Antti , Williams Robin
Published: February 2024

While infrastructures are governed by institutions and rules, they also govern us, as social actors, by their very design. This special issue is dedicated to the study of the governance embedded in infrastructures, as opposed to the governance of the effects they might produce. From machine learning to artificial intelligence, from gig economy platforms to the infrastructures used by governments for advertising or social security, the articles in this special issue highlight the digital governance we are subject to, while exploring alternatives we might consider.
Governing work through personal data: The case of Uber drivers in Geneva
Authors: Pidoux Jessica , Dehaye Paul-Olivier , Gursky Jacob
Published: February 2024

This article presents an ethnographic account of the advocacy initiative, conducted by NGO PersonalData.IO and the company Hestia.ai, that seeks to empower gig workers by helping them regain access to their personal data through data access rights, using the European Union General Data Protection Regulation. It is based on a case study of Uber drivers in Geneva that has a worldwide relevance for the gig economy. Previously self-employed, drivers are now classified as employees and their working time and earnings must be calculated according to local labour laws. We contribute to debates on algorithmic management in ride-hailing platforms by focusing on participatory methods of accountability through personal data, from an infrastructural perspective. First, we focus on the nexus between personal data protection and algorithmic management to understand the domination of ride-hailing platforms over the workers’ means of production, i.e., their personal data. We provide empirical transparency on the data structures of Uber for the sake of algorithmic accountability. These structures are utilised for their surge pricing algorithms and ultimately govern the workforce. Second, within a collective process of governance, we built participatory tools and methods for empowering gig workers and data scientists. These are means for calculating earnings and working that made explicit a new social meaning of work, i.e., “lost time between rides”.
Influence government, platform power and the patchwork profile: Exploring the appropriation of targeted advertising infrastructures for government behaviour change campaigns
Authors: Collier Ben , Stewart James , Horgan Shane , Thomas Daniel R. , Wilson Lydia
Published: February 2024

The targeted digital advertising infrastructures on which the business models of the social media platform economy rest have been the subject of significant academic and political interest. In this paper, we explore and theorise the appropriation of these infrastructures — designed for commercial and political advertising — by the state. In the U.K., public sector bodies have begun to repurpose the surveillance and messaging capacities of these social media platforms, along with the influencer economy, to deliver targeted behaviour change campaigns to achieve public policy goals. We explore how frameworks of behavioural government have aligned with Internet platforms’ extensive infrastructures and the commercial ecologies of professionalised strategic marketing. We map the current extent of these practices in the U.K. through case studies and empirical research in Meta’s Ad Library dataset. Although the networks of power and discourse within the ad infrastructure are indeed acting to shape the capacities of the state to engage in online influence, public bodies are mobilising their own substantial material networks of power and data to re-appropriate them to their own ends. Partly as a result of attempts by Meta to restrict the targeting of protected characteristics, we observe state communications campaigns building up what we term patchwork profiles of minute behavioural, demographic, and location-based categories in order to construct and reach particular groups of subjects. However, rather than a clear vision of a ‘cybernetic society’ of reactive information control, we instead find a heterogeneous and piecemeal landscape of different modes of power.
Automating universal credit: A case of temporal governance
Authors: Podoletz Lena , Currie Morgan
Published: February 2024

In this paper we study the dimension of time in social security, namely, the requirement of claimants to adopt a particular temporality in return for entitlements. We focus on the temporal rules and mandates of Universal Credit (UC), a unified benefit in the United Kingdom that delivers payment to claimants through a dynamic, automated means-testing system. UC imposes temporality through a monthly assessment period, a unit of time that UC has made infrastructural through an automated payment system. From our empirical study, we offer two sets of examples of how UC’s particular temporality shapes claimant experience of the benefit. In these cases, the monthly assessment period conflicts with other temporalities that claimants must contend with — those set by employers through their employer payment cycle, in one, and the timeframes dictated by childcare providers and the practical needs of people with young children in the other. In both cases, a temporal mismatch leads to a loss of entitlement, a phenomenon we call temporal punitiveness.