bluuebunny/crossref_metadata_2025 huggingface dataset:
Blockchain and machine learning in education: a literature review
Authors: Alhabeeb Sarah , Alrusayni Norah , Almutiri Reem , Alhumud Sarah , Al-Hagery Mohammed Abdullah
Published: December 2023

There is a growing influence around the use of technology in education, with many solutions already being implemented and many others being explored. Using various forms of technology to assist the educational process has increased dramatically in the previous decade in education systems in many respects. Both machine learning and the blockchain have had a significant impact on education. The purpose of this study is to conduct a literature review on the application of machine learning and blockchain technology in educational institutions. Additionally, this study examines the potential applications, benefits, and challenges those educational institutions may face as a result of using machine learning and blockchain technologies. Using machine learning and blockchain in educational systems will have a positive impact on the entire educational process and student achievement. Researchers, academics, and practitioners will benefit from this study to focus on a wider range of educational applications and solve the related issues of machine learning and blockchain technology in the education sector.
Personalized E-commerce based recommendation systems using deep-learning techniques
Authors: Nagraj Shruthi , Palayyan Blessed Prince
Published: December 2023

As technology is surpassing each day, with the variation of personalized drifts relevant to the explicit behavior of users using the internet. Recommendation systems use predictive mechanisms like predicting a rating that a customer could give on a specific item. This establishes a ranked list of items according to the preferences each user makes concerning exhibiting personalized recommendations. The existing recommendation techniques are efficient in systematically creating recommendation techniques. This approach encounters many challenges such as determining the accuracy, scalability, and data sparsity. Recently deep learning attains significant research to enhance the performance to improvise feature specification in learning the efficiency of retrieving the necessary information as well as a recommendation system approach. Here, we provide a thorough review of the deep-learning mechanism focused on the learning-rates-based prediction approach modeled to articulate the widespread summary for the state-of-art techniques. The novel techniques ensure the incorporation of innovative perspectives to pertain to the unique and exciting growth in this field.
Optimized robust fuzzy sliding mode control for efficient wastewater treatment: a comprehensive study
Authors: Kumara Varuna , Ganesan Ezhilarasan
Published: December 2023

Wastewater treatment plants (WWTPs) are plagued by nonlinearities, uncertainties, and disturbances that degrade control performance and may even lead to severe instability. The WWTP control issue has received a lot of research and development during the last several decades. One well-known way of designing a resilient control system is called sliding mode control (SMC). The SMC's greatest strength lies in its innate resistance to disturbances and uncertainty. Incorporating fuzzy SMC would eliminate the chattering effect, the primary drawback of traditional sliding-mode controller, without sacrificing robustness against parametric uncertainties, modeling errors, and variable dynamic loads. This article discusses the hybridization of fuzzy logic with sliding mode control to provide highly excellent stability and accuracy in a control system. As a means of optimizing the fuzzy SMC, the gradient-free optimization technique known as the Jaya algorithm is investigated. By repeatedly altering a population of individual solutions, this population-based method can deal with both limited and unbounded optimization issues.
Potentials of artificial intelligence in digital marketing and financial technology for small and medium enterprises
Authors: Enshassi Mohammed , Nathan Robert Jeyakumar , Soekmawati Soekmawati , Al-Mulali Usama , Ismail Hishamuddin
Published: December 2023

Small and medium enterprises small and medium enterprises (SMEs) play a crucial role in nations’ economy, through job creations, reducing unemployment rate as well as increase the overall productivity and gross domestic product (GDP) of a country. However, most SMEs are often lagging in technology adoption which could be a game changer for their success. SMEs could adopt new technologies to improve their business operations and profitability. They are also useful in supporting SMEs to penetrate international market. This research suggests that implementation of the artificial intelligence (AI) through digital marketing (DM) and financial technology (Fintech) would assist SMEs to be competitive, current in leveraging on technology and increase their overall profitability. Based on secondary data analysis, this paper presents a conceptual framework of determining factors in adoption of AI through digital marketing and Fintech. It contributes to the academic knowledge of AI, DM and Fintech for small businesses, and presents a testable framework that can be replicated and adapted for future empirical study. 
Attendance management system using face recognition
Authors: Jadhav Ashish , Kamble Dhwaniket , Rathod Santosh B. , Kumar Sumita , Kadam Pratima , Dalwai Mohammad
Published: December 2023

Traditional attendance systems consist of registers marked by teachers, leading to human error and a lot of maintenance. Time consumption is a key point in this system. We wanted to revolutionize the digital tools available in today's time i.e., facial recognition. This project has revolutionized to overcome the problems of the traditional system. Face recognition and marking the present is our project. A database of all students in the class is kept in single folder, and attendance is marked if each student's face matches with one of the stored faces. Otherwise, the face is ignored and not marked for attendance. In our project, face detection (machine learning) is used.
Evaluating sentiment analysis and word embedding techniques on Brexit
Authors: Moudhich Ihab , Fennan Abdelhadi
Published: December 2023

In this study, we investigate the effectiveness of pre-trained word embeddings for sentiment analysis on a real-world topic, namely Brexit. We compare the performance of several popular word embedding models such global vectors for word representation (GloVe), FastText, word to vec (word2vec), and embeddings from language models (ELMo) on a dataset of tweets related to Brexit and evaluate their ability to classify the sentiment of the tweets as positive, negative, or neutral. We find that pre-trained word embeddings provide useful features for sentiment analysis and can significantly improve the performance of machine learning models. We also discuss the challenges and limitations of applying these models to complex, real-world texts such as those related to Brexit.

 
Machine learning for text document classification-efficient classification approach
Authors: Mohammed Ali Sura I. , Nihad Marwah , Mohamed Sharaf Hussien , Farouk Haitham
Published: December 2023

Numerous alternative methods for text classification have been created because of the increase in the amount of online text information available. The cosine similarity classifier is the most extensively utilized simple and efficient approach. It improves text classification performance. It is combined with estimated values provided by conventional classifiers such as Multinomial Naive Bayesian (MNB). Consequently, combining the similarity between a test document and a category with the estimated value for the category enhances the performance of the classifier. This approach provides a text document categorization method that is both efficient and effective. In addition, methods for determining the proper relationship between a set of words in a document and its document categorization is also obtained.
Face and liveness detection with criminal identification using machine learning and image processing techniques for security system
Authors: Shinde Pratibha , Raundale Ajay R.
Published: December 2023

In the past, real-world photos have been used to train classifiers for face liveness identification since the related face presentation attacks (PA) and real-world images have a high degree of overlap. The use of deep convolutional neural networks (CNN) and real-world face photos together to identify the liveness of a face, however, has received very little study. A face recognition system should be able to identify real faces as well as efforts at faking utilizing printed or digital presentations. A true spoofing avoidance method involves observing facial liveness, such as eye blinking and lip movement. However, this strategy is rendered useless when defending against replay assaults that use video. The anti-spoofing technique consists of two modules: the ConvNet classifier module and the blinking eye module, which measure lip and eye movement. The results of the testing demonstrate that the developed module is capable of identifying various face spoof assaults, including those made with the use of posters, masks, or smartphones. To assess the convolutional features in this study adaptively fused from deep CNN produced face pictures and convolutional layers learned from real-world identification. Extensive tests using intra-database and cross-database scenarios on cutting-edge face anti-spoofing databases including CASIA, OULU, NUAA and replay-attack dataset demonstrate that the proposed solution methods for face liveness detection. The algorithm has a 94.30% accuracy rate.
Towards a new approach to maximize tax collection using machine learning algorithms
Authors: Ourdani Nabil , Chrayah Mohamed , Aknin Noura
Published: December 2023

Efficient tax debt collection is a challenge for Moroccan local tax authorities. This article explores the potential of machine learning techniques and novel strategies to enhance efficiency in this process. We present a practical use case demonstrating the application of machine learning for taxpayer segmentation, improving accuracy in identifying high-risk debtors. Using a comprehensive dataset of tax payment behavior, we showcase the effectiveness of machine learning algorithms in segmenting taxpayers based on their likelihood of non-compliance or debt accumulation. We also investigate innovative strategies that integrate behavioral economics principles to enable better targeted interventions. Real-world case studies in local tax debt collection highlight the impact of these strategies. The findings underscore the transformative potential of machine learning techniques and novel strategies in improving the efficiency of local tax debt collection. Accurate identification of high-risk debtors and tailored enforcement actions help maximize revenue while minimizing resource waste. This research contributes to the existing knowledge by providing insights into the implementation of machine learning techniques and novel strategies in tax debt collection. It emphasizes the importance of data-driven approaches and highlights how local tax authorities can drive efficiency and optimize revenue collection by embracing these advancements.
Predicting baccalaureate student result to prevent failure: a hybrid model approach
Authors: Essayad Abdesslam , Moulay Abdella Kassimi
Published: December 2023

The Moroccan Ministry of National Education has seen substantial modifications over the previous ten years, which have contributed to improving the quality of education. However, there is a discrepancy in the percentage of academic achievement between the regional directorates and educational institutions. Machine learning techniques have become a powerful tool for proactively predicting student admission. The goal of our paper is to build machine learning models using various algorithms to predict the final baccalaureate school year outcomes. We compare regression and classification to find the reasons behind students' failure and to choose an appropriate model for predicting the results. This helps decision-makers make appropriate interventions.
Hyperparameters optimization XGBoost for network intrusion detection using CSE-CIC-IDS 2018 dataset
Authors: Chimphlee Witcha , Chimphlee Siriporn
Published: December 2023

With the introduction of high-speed internet access, the demand for security and dependable networks has grown. In recent years, network attacks have gotten more complex and intense, making security a vital component of organizational information systems. Network intrusion detection systems (NIDS) have become an essential detection technology to protect data integrity and system availability against such attacks. NIDS is one of the most well-known areas of machine learning software in the security field, with machine learni ng algorithms constantly being developed to improve performance. This research focuses on detecting abnormalities in societal infiltration using the hyperparameters optimization XGBoost (HO-XGB) algorithm with the Communications Security Establishment-The Canadian Institute for Cybersecurity-Intrusion Detection System2018 (CSE-CIC-IDS2018) dataset to get the best potential results. When compared to typical machine learning methods published in the literature, HO-XGB outperforms them. The study shows that XGBoost outperforms other detection algorithms. We refined the HO-XGB model's hyperparameters, which included learning_rate, subsample, max_leaves, max_depth, gamma, colsample_bytree, min_child_weight, n_estimators, max_depth, and reg_alpha. The experimental findings reveal that HO-XGB1 outperforms multiple parameter settings for intrusion detection, effectively optimizing XGBoost's hyperparameters.
You only look once model-based object identification in computer vision
Authors: Reddy Shiva Shankar , Maheswara Rao Venkata Rama , Voosala Priyadarshini , Nrusimhadri Silpa
Published: December 2023

You only look once version 4 (YOLOv4) is a deep-learning object detection algorithm. It is used to decrease parameters and simplify network structures, making it suited for mobile and embedded device development. The YOLO detector can foresee an object's Class, bounding box, and probability of that Object's Class being found inside that bounding box. A probability value for each bounding box represents the likelihood of a given item class in that bounding box. Global features, channel attention, and special attention are also applied to extract more compelling information. Finally, the model combines the auxiliary and backbone networks to create the YOLOv4's entire network topology. Using custom functions developed upon YOLOv4, we get the count of the objects and a crop around the objects detected with a confidence score that specifies the probability of the thing seen being the same Class as predicted by YOLOv4. A confidence threshold is implemented to eliminate the detections with low confidence. 
CRNN model for text detection and classification from natural scenes
Authors: Prakash Puneeth , Yeliyur Hanumanthaiah Sharath Kumar , Bannur Mayigowda Somashekhar
Published: December 2023

In the emerging field of computer vision, text recognition in natural settings remains a significant challenge due to variables like font, text size, and background complexity. This study introduces a method focusing on the automatic detection and classification of cursive text in multiple languages: English, Hindi, Tamil, and Kannada using a deep convolutional recurrent neural network (CRNN). The architecture combines convolutional neural networks (CNN) and long short-term memory (LSTM) networks for effective spatial and temporal learning. We employed pre-trained CNN models like VGG-16 and ResNet-18 for feature extraction and evaluated their performance. The method outperformed existing techniques, achieving an accuracy of 95.0%, 96.3%, and 96.2% on ICDAR 2015, ICDAR 2017, and a custom dataset (PDT2023), respectively. The findings not only push the boundaries of text detection technology but also offer promising prospects for practical applications.
Predictive maintenance framework for assessing health state of centrifugal pumps
Authors: Mallioris Panagiotis , Diamantis Evangelos , Bialas Christos , Bechtsis Dimitrios
Published: December 2023

Combined with advances in sensing technologies and big data analytics, critical information can be extracted from continuous production processes for predicting the health state of equipment and safeguarding upcoming failures. This research presents a methodology for applying predictive maintenance (PdM) solutions and showcases a PdM application for health state prediction and condition monitoring, increasing the safety and productivity of centrifugal pumps for a sustainable and resilient PdM ecosystem. Measurements depicting the healthy and maintenance-prone stages of two centrifugal pumps were collected on the university campus. The dataset consists of 5,118 records and includes both running and standstill values. Additionally, Spearman statistical analysis was conducted to measure the correlation of collected measurements with the predicted output of machine conditions and select the most appropriate features for model optimization. Several machine learning (ML) algorithms, namely random forest (RF), Naïve Bayes, support vector machines (SVM), and extreme gradient boosting (XGBoost) were analyzed and evaluated during the data mining process. The results indicated the effectiveness and efficiency of XGBoost for the health state prediction of centrifugal pumps. The contribution of this research is to propose an effective framework collectong multistage health data for PdM applications and showcase its effectiveness in a real-world use case.
Automated signal pre-emption system for emergency vehicles using internet of things
Authors: Sothenahalli Krishnaraju Pushpa , Thimmasandra Narayanappa Manjunath , Kathavate Sheela , Shirwaikar Rudresh , Chandrakanth Rao Anupchandra Rao , Rohith Jayachnadra
Published: December 2023

Vehicle administration systems are one of the major highlights especially in urban areas. One important critical component that requires attention are signal preemption systems. Every single work on traffic congestion identification either requires prior learning or long time to distinguish and perceive the closeness of congestion. FutureSight performs predictive analysis and control of traffic signals through the application of machine learning to aide ambulances in such a way that, a signal turns green beforehand so as to ensure an obstacle free path to the ambulance from source to destination based on various parameters such as traffic density, congestion length, previous wait times, arrival time thereby eliminating the need for human intervention. The method allows flexible interface to the driver to enter the hospital details to reach the destination with in time. The app then plans out the fastest route from the pickup spot to the selected hospital and sends this route to the system. The system then predict the amount of time that is required by the signal to remain green so as to clear all traffic at that specific junction before the ambulance arrives at that location.
The prediction of Bitcoin price through gold price using long short-term memory model
Authors: Choi Jae Won , Choi Young Keun
Published: December 2023

The majority of research on predicting the price of Bitcoin employs technical methods to enhance long short-term memory models' effectiveness. Although some studies employ different machine learning techniques, such as economic or technical indicators, their precision is inadequate. Thus, this research aims to introduce a model that predicts the price of Bitcoin by utilizing the long short-term memory (LSTM) technique and incorporating gold's economic and technical data as features. The research collected gold and Bitcoin price data from FinanceDataReader for around seven years, from January 1, 2016, to January 22, 2023, consisting of six categories: date, open, high, low, close, volume, and change (based on dollars). The normalized closing price data was trained for 50 epochs, resulting in the loss value reaching close to zero. The model's accuracy was measured by mean squared error, resulting in a score of 0.0004. This study's importance is two-fold: firstly, it can provide cryptocurrency-related businesses with more accurate predictions and improved risk management indicators. Secondly, incorporating economic metrics can address the limitations of overfitting and a single model's poor performance.
Explainable ensemble technique for enhancing credit risk prediction
Authors: Nooji Pavitha , Sugave Shounak
Published: December 2023

Credit risk prediction is a critical task in financial institutions that can impact lending decisions and financial stability. While machine learning (ML) models have shown promise in accurately predicting credit risk, the complexity of these models often makes them difficult to interpret and explain. The paper proposes the explainable ensemble method to improve credit risk prediction while maintaining interpretability. In this study, an ensemble model is built by combining multiple base models that uses different ML algorithms. In addition, the model interpretation techniques to identify the most important features and visualize the model's decision-making process. Experimental results demonstrate that the proposed explainable ensemble model outperforms individual base models and achieves high accuracy with low loss. Additionally, the proposed model provides insights into the factors that contribute to credit risk, which can help financial institutions make more informed lending decisions. Overall, the study highlights the potential of explainable ensemble methods in enhancing credit risk prediction and promoting transparency and trust in financial decision-making.
Extracting features of tomato viral leaf diseases using image processing techniques
Authors: Sagar Sanjeela , Singh Jaswinder
Published: December 2023

Agriculture is the main livelihood of Indians. More than 50% of Indian population Is dependent on it and it contributes about 18% of Indian gross domestic product (GDP). According to Inc42, the agricultural sector of India is predicted to increase to US$ 24 billion by 2025. With the increase in population, the demand for food also increases, but more than 30% of crops get affected due to crop diseases. Overall, India lost approximately five million hectares of crop area to flash floods, cyclonic storms, floods, cloudbursts, and landslides till 2021. In that case, there is a need to prevent crops from diseases to fulfil demand supply ratio. This paper presents the feature extraction of tomato viral leaf diseases using various image processing techniques. Most of the research uses Convolutional Neural networks to extract the features of these diseases, but these neural networks are not performing much accurately in real scenarios, so there is a need to extract the features using image processing methods. During the study, it is found that these diseases have different colours, shapes and textures and these features can be used with convolution neural networks to bring more accurate results in real scenarios.
Early stroke disease prediction with facial features using convolutional neural network model
Authors: Ahmad Ali , Usama Muhammad , Niaz Khan Yasir
Published: December 2023

Past researcher has proposed computed tomography (CT) and magnetic resonance image (MRI) scan images as the most efficient ways to diagnose stroke disease. These methods are not only hectic and take much time but are also costly. This paper proposes a new approach to diagnosing this disease and gives a time and cost-efficient solution. We have offered a two-step solution to diagnose stroke disease in a patient using only the patient’s facial image. In the first step, we gathered a dataset of several stroke patients and normal persons. Then we applied several pre-processing operations, including red, green and blue (RGB) to grayscale conversion, scaling/ resizing, and normalization on dataset images before training them. In the second step, we trained the cropped images of their face regions and trained them using a convolutional neural network (CNN). We have successfully achieved an efficiency of 98%. The accuracy, precision, recall, and f-measure of the results were measured at 98%, 97%, 99%, and 98% respectively.
Implementation of deep neural networks learning on unmanned aerial vehicle based remote-sensing
Authors: Ahmed Shouket Abdulrahman , Desa Hazry , T. Hussain Abadal-Salam , A. Taha Taha
Published: December 2023

Due to efficient and adaptable data collecting, unmanned aerial vehicle (UAV) has been a popular topic in computer vision (CV) and remote sensing (RS) in recent years. Inspiring by the recent success of deep learning (DL), several enhanced object identification and tracking methods have been broadly applied to a variety of UAV-related applications, including environmental monitoring, precision agriculture, and traffic management. In this research, we present efficient neural network (ENet), a unique deep neural network architecture designed exclusively for jobs demanding low latency operation. ENet is up to quicker, takes fewer floating-point operations per second (FLOPs), has fewer parameters, and offers accuracy comparable to or superior to that of previous models. We have tested it on the street and cityscapes reports on comparisons with current state-of-the-art approaches and the tradeoffs between a network's processing speed and accuracy. We give measurements of the proposed architecture's performance on embedded devices and offer software enhancements that might make ENet even quicker.
A deep learning-based approach for early detection of disease in sugarcane plants: an explainable artificial intelligence model
Authors: Pudupet Ethiraj Rubini , Paranjothi Kavitha
Published: December 2023

In many regions of the nation, agriculture serves as the primary industry. The farming environment now faces a number of challenges to farmers. One of the major concerns, and the focus of this research, is disease prediction.
 A methodology is suggested to automate a process for identifying disease in plant growth and warning farmers in advance so they can take appropriate action. Disease in crop plants has an impact on agricultural production. In this work, a novel DenseNet-support vector machine: explainable artificial intelligence (DNet-SVM: XAI) interpretation that combines a DenseNet with support vector machine (SVM) and local interpretable model-agnostic explanation (LIME) interpretation has been proposed. DNet-SVM: XAI was created by a series of modifications to DenseNet201, including the addition of a support vector machine (SVM) classifier. Prior to using SVM to identify if an image is healthy or un-healthy, images are first feature extracted using a convolution network called DenseNet. In addition to offering a likely explanation for the prediction, the reasoning is carried out utilizing the visual cue produced by the LIME. In light of this, the proposed approach, when paired with its determined interpretability and precision, may successfully assist farmers in the detection of infected plants and recommendation of pesticide for the identified disease.
1-dimensional convolutional neural networks for predicting sudden cardiac
Authors: Reddy Karna Viswavardhan , Vishnu Vardhana Reddy Karna
Published: December 2023

Sudden cardiac arrest (SCA) is a serious heart problem that occurs without symptoms or warning. SCA causes high mortality. Therefore, it is important to estimate the incidence of SCA. Current methods for predicting ventricular fibrillation (VF) episodes require monitoring patients over time, resulting in no complications. New technologies, especially machine learning, are gaining popularity due to the benefits they provide. However, most existing systems rely on manual processes, which can lead to inefficiencies in disseminating patient information. On the other hand, existing deep learning methods rely on large data sets that are not publicly available. In this study, we propose a deep learning method based on one-dimensional convolutional neural networks to learn to use discrete fourier transform (DFT) features in raw electrocardiogram (ECG) signals. The results showed that our method was able to accurately predict the onset of SCA with an accuracy of 96% approximately 90 minutes before it occurred. Predictions can save many lives. That is, optimized deep learning models can outperform manual models in analyzing long-term signals.
Efficient commodity price forecasting using long short-term memory model
Authors: Tami Mohammad , Owda Amani Yousef
Published: December 2023

Predicting commodity prices, particularly food prices, is a significant concern for various stakeholders, especially in regions that are highly sensitive to commodity price volatility. Historically, many machine learning models like autoregressive integrated moving average (ARIMA) and support vector machine (SVM) have been suggested to overcome the forecasting task. These models struggle to capture the multifaceted and dynamic factors influencing these prices. Recently, deep learning approaches have demonstrated considerable promise in handling complex forecasting tasks. This paper presents a novel long short-term memory (LSTM) network-based model for commodity price forecasting. The model uses five essential commodities namely bread, meat, milk, oil, and petrol. The proposed model focuses on advanced feature engineering which involves moving averages, price volatility, and past prices. The results reveal that our model outperforms traditional methods as it achieves 0.14, 3.04%, and 98.2% for root mean square error (RMSE), mean absolute percentage error (MAPE), and R-squared (R2), respectively. In addition to the simplicity of the model, which consists of an LSTM single-cell architecture that reduced the training time to a few minutes instead of hours. This paper contributes to the economic literature on price prediction using advanced deep learning techniques as well as provides practical implications for managing commodity price instability globally.
COMMODIFICATION AND EXPLOITATION OF AESTHETIC LABOR
Authors: Yeşilyurt Temel Hülya
Published: December 2023

This study examines what bank employees do in the face of their managers' aesthetic expectations and controls and how they experience this process. The main argument of the research is how one's aestheticism plays a role in working life. The research is designed on the main question 'Is the aestheticism of bank employees used as a means of labor exploitation? Phenomenology, one of the qualitative research designs, was adopted and the 'interpretative phenomenology' type of phenomenology research design was used. For sample selection, a total of 20 participants, both male and female, from public and private bank employees were interviewed using snowball sampling method. The research data were obtained through semi-structured in-depth interviews with the participants. Analyses were conducted using MAXQDA 22, a computer-aided qualitative data analysis program.

As a result of the research; employees evaluated aesthetic labor not only as physical/visual aesthetics, but also as a whole from different perspectives such as auditory, intellectual and harmony/harmony aesthetics. Employees strive for aesthetic labor in order to participate in employment and sustainability of employment in a postmodern order based on competition. Due to the functioning of the surveillance mechanism, employees take care to keep these efforts dynamic in both manager and customer relations. The growth of the services sector has led employees to develop aesthetic labor strategies as well as aesthetic labor efforts and skills. In the research, the results of aesthetic labor exploitation of the employee in various aspects such as gender-based discrimination, commodification of labor, surveillance mechanism, obligation, emotional mask, glass ceiling, self-alienation, unfair rewarding, time and cost were reached. In this context, when a sectoral and gender-based evaluation is made, aesthetic labor exploitation is more intense in private banks and female employees.
The 2021 Bo Lindell Lecture: Inclusive, accountable, transparent: the direction we should take for the benefit of present and future generations
Authors: Ogino H.
Published: December 2023

The International Commission on Radiological Protection (ICRP) is recognised as the de-facto world authority in the field of radiological protection. The ICRP Recommendations have been used as a basis for regulations and policy in almost every country, and with the current review and revision of the System of Radiological Protection, it will continue to make significant contributions in radiation safety for patients, workers, the public, and the environment. In a society undergoing significant change, it is necessary to give careful thought to which groups will be perceived as authoritative organisations by the constituents of the future. The ideal form of an authoritative organisation in the new society of the future is to continue to show how it came to make such recommendations, how it reflected the opinions of interested parties in the process, and how it discloses its records with as much transparency as possible. The question now is what we must do to ensure that decision-making advances in a way that not only makes sense to the present generation, but will be easily consumed by future generations. The path that ICRP is taking to formulate the next set of General Recommendations is doing just that, in line with the key procedural values of INCLUSIVE, ACCOUNTABLE, AND TRANSPARENT.
Women’s Leadership Development Through Entrepreneurship in Bangladesh
Authors: Afrin Sharmina , Hoque Mohammad Shamsul , Akter Bina
Published: December 2023

Women own and manage only an approximate 7% of all economic establishments in Bangladesh, indicating a lack of women leadership development and economic participation. This research aims to identify women entrepreneurship development (WED) factors (WEDFs) and investigate how they relate to leadership development of female entrepreneurs. The study identified 370 women entrepreneurs from the Bangladesh Women Chamber of Commerce and Industry list of seven divisional headquarters and collected primary data for systematic probability sampling in this causal research. The study initially conducted an exploratory factor analysis technique to reduce larger measured variables into smaller factors. Data were further analysed through the structural equation modelling technique. According to the findings, women entrepreneurs in Bangladesh develop emotional intelligence, creativity and innovative leadership qualities that influence leadership skills by exercising four WEDFs. These factors are idea generation, resourcing, enterprise management and harvesting and growth. The study’s main theoretical contribution is that it has been able to combine two concepts, ‘women’s leadership development’ and ‘women’s entrepreneurship development, into one framework. The study suggests that policymakers, practitioners and women entrepreneurs should implement the interventions focusing on these factors to strengthen the development of women’s leadership. One of the limitations of this study is that it covers urban Bangladeshi women entrepreneurs only.
Lithium-ion battery state of charge estimation using improved coulomb counting method with adaptive error correction
Authors: Wu King Hang , Seyedmahmoudian Mehdi , Mekhilef Saad , Shrivastava Prashant , Stojcevski Alex
Published: December 2023

Accurate state of charge (SoC) estimations are vital to guarantee the safe and efficient operation of Lithium-ion battery systems in electric vehicles. In this paper, an improved coulomb counting method with adaptive error correction (ICC-AEC) with dual closed-loop control system (DCLCS) for SoC estimation is proposed. Where, a DCLCS with first-order RC battery modeling using forgetting factor-based recursive least square (FF-RLS) algorithm is utilized to achieve high estimation accuracy by recalibrating the SoC factor and fast response time. To evaluate and validate the performance of the proposed algorithm, a dynamic load profile with a large range of temperature variation is considered. Further, SoC estimation results obtained from traditional mixed method (MM) with closed-loop control system (CLCS) and proposed ICC-AEC with DCLCS are compared. The results show that the SoC estimation error obtained from ICC-AEC with DCLCS is lesser than MM with CLCS under considered dynamic operating conditions.
Government Subsidy Nexus: The Case of Employment and Performance Dynamics
Authors: Ahia Bright Nana Kwame , Anafo Solomon Abugri , song Na , Boakye Elijah Asante , Armah Gabriel Kofi
Published: December 2023

In this article, the impact of nexus of subsidy on economic growth and performance is examined, with focus on age influence. We analyse data from 1,272 Chinese manufacturing firms from 2007 to 2017 using quantile regressions and Generalized Method of Moments. The findings indicate that subsidies have a positive effect on employment growth but a negative impact on performance. Age is found to have a weaker moderating effect on the subsidy-performance relationship but a stronger effect on the subsidy-employment-growth relationship. Young and private firms experience alternating growth patterns, while state and matured firms consistently exhibit positive growth across all quantiles. The study also reveals that lagged employment and subsidy levels have a significant positive relationship with current employment-related issues, while performance shows a change in direction with lags. Our result shows that subsidy impacts better on growth rather than performance as a by-product. However, with limiting recommendations on governments efforts to overspend on young firms. We discuss our findings within the theoretical frameworks of the economics of subsidy and the theory of cost and benefit for emerging markets.
How the Performance of Medium, Small and Micro Co-operatives Should be Measured? A Perspective from Malaysia
Authors: Aziz Mohd Taufix A. , Saleh Norman Mohd , Jabbar Siti Faridah Abdul , Hamzah Noradiva , Salleh Hayati Md
Published: December 2023

Researchers have focused too much on the financial performance of co-operatives, neglecting the original purpose and nature of co-operatives. There is a gap in the literature, especially in developing a holistic measurement of co-operative performance based on stakeholder perspectives. Thus, the objective of this study is to develop a measurement of co-operative performance, considering co-operative principles and economic, social, and sustainable governance perspectives, suitable for the context of medium, small, and micro co-operatives. Malaysia is used as the context. In conducting this study, the Delphi technique is utilized where a specific focus group consisting of five clusters representing the stakeholders as participants. Qualitative analysis of results suggests performance indicators that reflect the purpose (objectives) and principles of co-operatives, which are divided into three aspects, economic, social and sustainable governance. This study is one of very few studies that develop the indicators for co-operative performance measurement. Hence, it is important for co-operatives in this category to consider the use of indicators suggested in this study to comprehensively measure their performance.
Predicting recurrent interventions after radiocephalic arteriovenous fistula creation with machine learning and the PREDICT-AVF web app
Authors: Heindel Patrick , Dey Tanujit , Fitzgibbon James J , Mamdani Muhammad , Hentschel Dirk M , Belkin Michael , Ozaki Charles Keith , Hussain Mohamad A
Published: December 2023

Objective:

Kidney Disease Outcomes Quality Initiative (KDOQI) guidelines discourage ongoing access salvage attempts after two interventions prior to successful use or more than three interventions per year overall. The goal was to develop a tool for prediction of radiocephalic arteriovenous fistula (AVF) intervention requirements to help guide shared decision-making about access appropriateness.

Methods:

Prospective cohort study of 914 adult patients in the United States and Canada undergoing radiocephalic AVF creation at one of the 39 centers participating in the PATENCY-1 or -2 trials. Clinical data, including demographics, comorbidities, access history, anatomic features, and post-operative ultrasound measurements at 4–6 and 12 weeks were used to predict recurrent interventions required at 1 year postoperatively. Cox proportional hazards, random survival forest, pooled logistic, and elastic net recurrent event survival prediction models were built using a combination of baseline characteristics and post-operative ultrasound measurements. A web application was created, which generates patient-specific predictions contextualized with the KDOQI guidelines.

Results:

Patients underwent an estimated 1.04 (95% CI 0.94–1.13) interventions in the first year. Mean (SD) age was 57 (13) years; 22% were female. Radiocephalic AVFs were created at the snuffbox (2%), wrist (74%), or proximal forearm (24%). Using baseline characteristics, the random survival forest model performed best, with an area under the receiver operating characteristic curve (AUROC) of 0.75 (95% CI 0.67–0.82) at 1 year. The addition of ultrasound information to baseline characteristics did not substantially improve performance; however, Cox models using either 4–6- or 12-week post-operative ultrasound information alone had the best discrimination performance, with AUROCs of 0.77 (0.70–0.85) and 0.76 (0.70–0.83) at 1 year. The interactive web application is deployed at https://predict-avf.com .

Conclusions:

The PREDICT-AVF web application can guide patient counseling and guideline-concordant shared decision-making as part of a patient-centered end-stage kidney disease life plan.
Perspectives of autistic adolescent girls and women on the determinants of their mental health and social and emotional well-being: A systematic review and thematic synthesis of lived experience
Authors: O’Connor Rachel AG , Doherty Mary , Ryan-Enright Theresa , Gaynor Keith
Published: December 2023

Autistic girls and women experience more mental health difficulties and poorer well-being than their non-autistic peers. Little emphasis has been placed on the perspectives of the girls and women within the literature. This review aims to provide an overview of the factors that impact autistic females’ emotional and social well-being and mental health, as described in self-report qualitative studies. The protocol for the present review was pre-registered on PROSPERO (CRD42020184983), and this article follows Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PsycInfo, Academic Search Complete and MEDLINE were systematically searched using a pre-defined search string. This yielded 877 unique records, which were systematically screened by two reviewers, resulting in 52 eligible studies. Structured data extraction and quality appraisal were completed. The present review presents the perspectives of 973 autistic females aged 13–70+. Thematic synthesis identified three themes and nine subthemes. Our findings suggest that autistic girls’ and women’s experiences can be conceptualised within a social model, where biological and psychological factors (‘The Autistic Neurotype’) are experienced through the lens of social factors (‘The Neurotypical World’ and ‘Stigma’), together shaping well-being and mental health outcomes.

Lay abstract

Difficulties with mental health and low levels of well-being are more common among autistic girls and women than non-autistic people, but we do not fully understand why. Research does not focus enough on what autistic girls and women could tell us about this. This review aims to summarise the studies where autistic girls and women explain things that affect their mental health and well-being to help us understand how to prevent these difficulties from developing. Three research databases were searched to find possibly relevant studies. There were 877 studies found, which two researchers screened according to particular criteria. They found 52 studies that could be included in this review. One researcher evaluated the quality of these studies and extracted the key information from them. This review summarises the views of 973 autistic girls and women aged between 13 and 70+. The findings from the 52 studies were analysed, and we found many factors that affect the mental health and well-being of autistic girls and women. These factors fall into two categories: (1) difficulties living in a world not designed for autistic people and (2) the impact of stigma due to being autistic.
Outcome after total pancreatectomy with islet autotransplantation: A European single-center study
Authors: Fröberg Klara , Halimi Asif , Vujasinovic Miroslav , Caballero-Corbalan José , Arnelo Urban , Sparrelid Ernesto , Korsgren Olle , Löhr Johannes-Matthias , Lundgren Torbjörn , Ghorbani Poya
Published: December 2023

Background and aims:

Chronic pancreatitis may cause intractable abdominal pain, with total pancreatectomy sometimes being the last resort. To mitigate the subsequent diabetes, total pancreatectomy can be followed by islet autotransplantation (TP-IAT). The primary aim of this study was to assess the outcomes in patients undergoing TP-IAT at Karolinska University Hospital with respect to safety, postoperative complications, and islet graft function. A secondary aim was to compare liver to skeletal muscle as autotransplantation sites.

Methods:

Single-center observational cohort study on patients undergoing TP-IAT. Islets were transplanted either into the liver or skeletal muscle. Data on baseline characteristics and pretransplantory conditions were collected. Outcome measures included mortality and major postoperative complications as well as the glycemic measures: insulin use, fasting C-peptide, and HbA1c.

Results:

Between 2004 and 2020, 24 patients underwent TP-IAT. Islets were transplanted into the liver in 9 patients and into skeletal muscle in 15 patients. There was no 90-day mortality, and major complications (Clavien–Dindo ⩾IIIa) occurred in 26.7%, all related to the procedure of total pancreatectomy. Fasting C-peptide could be detected postoperatively, with higher levels in patients receiving islet autotransplantation into the liver (p = 0.006). Insulin independence was not achieved, although insulin doses at last follow-up were significantly lower in patients receiving islet autotransplantation into the liver compared to skeletal muscle (p = 0.036).

Conclusion:

TP-IAT is safe and associated with tolerable risk, the component of islet autotransplantation being seemingly harmless. Although islet grafts maintain some endocrine function, insulin independence should not be expected. Regarding islet autotransplantation sites, the liver seems superior to skeletal muscle.

Clinical trial registration:

Not applicable.