bluuebunny/crossref_metadata_2025 huggingface dataset:
Indirect feedback alignment in deep learning for cognitive agent modeling: enhancing self-confidence analytics in the workplace
Authors: Yuttachai Hareebin , Arbaoui Billel , O-manee Yusraw
Published: October 2024

The innovative application of indirect feedback alignment (IFA) in deep learning enhances workplace self-confidence analytics through cognitive agent modeling. IFA addresses the challenge of credit assignment in multi-layer neural networks, offering a more efficient and biologically plausible alternative to traditional backpropagation methods. The paper delves into the integration of IFA in workplace dynamics, focusing on the development of a state-determined system to describe and analyze the dynamics of self-confidence, self-concept, self-esteem, and self-efficacy among employees. Utilizing a combination of endogenous and exogenous factors, the study presents a comprehensive model that captures the complex interplay of these factors in professional settings. The research further conducts experiments to observe and analyze the behavior and pattern formation among real workers in various settings, demonstrating the practical implications of the theoretical model. The findings highlight the potential of IFA in enhancing and accelerating the components of deep learning associated with self-confidence in the workplace, contributing significantly to the fields of neural computation and cognitive psychology. The proposed method was tested in various situations to assess its alignment with the core concepts of workplace self-confidence. Mathematical analysis was employed to explore feasible equilibrium conditions and compatible cases found in the literature.
Aspect-based sentiment analysis: natural language understanding for implicit review
Authors: Suhariyanto Suhariyanto , Sarno Riyanarto , Fatichah Chastine , Abdullah Rachmad
Published: October 2024

The different types of implicit reviews should be well understood so that the developed extraction technique can solve all problems in implicit reviews and produce precise terms of aspects and opinions. We propose an aspect-based sentiment analysis (ABSA) method with natural language understanding for implicit reviews based on sentence and word structure. We built a text extraction method using a machine learning algorithm rule with a deep understanding of different types of sentences and words. Furthermore, the aspect category of each review is determined by measuring the word similarity between the aspect terms contained in each review and aspect keywords extracted from Wikipedia. Bidirectional encoder representations from transformers (BERT) embedding and semantic similarity are used to measure the word similarity value. Moreover, the proposed ABSA method uses BERT, a hybrid lexicon, and manual weighting of opinion terms. The purpose of the hybrid lexicon and the manual weighting of opinion terms is to update the existing lexicon and solve the problem of weighting words and phrases of opinion terms. The evaluation results were very good, with average F1-scores of 93.84% for aspect categorization and 92.42% for ABSA.
Deep learning-based attention models for sarcasm detection in text
Authors: Chandrasekaran Ganesh , Chowdary Mandalapu Kalpana , Babu Jyothi Chinna , Kiran Ajmeera , Kumar Kotthuru Anil , Kadry Seifedine
Published: October 2024

Finding sarcastic statements has recently drawn a lot of curiosity in social media, mainly because sarcastic tweets may include favorable phrases that fill in unattractive or undesirable attributes. As the internet becomes increasingly ingrained in our daily lives, many multimedia information is being produced online. Much of the information recorded mostly on the internet is textual data. It is crucial to comprehend people's sentiments. However, sarcastic content will hinder the effectiveness of sentiment analysis systems. Correctly identifying sarcasm and correctly predicting people's motives are extremely important. Sarcasm is particularly hard to recognize, both by humans and by machines. We employ the deep bi-directional long-short memory (Bi-LSTM) and a hybrid architecture of the convolution neural network+Bi-LSTM (CNN+Bi-LSTM) with attention networks for identifying sarcastic remarks in a corpus. Using the SarcasmV2 dataset, we test the efficacy of deep learning methods BiLSTM, and CNN+BiLSTM with attention network) for the task of identifying text sarcasm. The suggested approach incorporating deep networks is consistent with various recent and advanced techniques for sarcasm detection. With attention processes, the improved CNN+Bi-LSTM model achieved an accuracy rate of 91.76%, which is a notable increase over earlier research.
An analysis of diverse computational models for predicting student achievement on e-learning platforms using machine learning
Authors: Tirumanadham Naga Satya Koti Mani Kumar , Sekhar Thaiyalnayaki , Muthal Sriram
Published: October 2024

Coronavirus disease 2019 (COVID-19) has led many colleges and students to use online learning. In educational databases with so much data, evaluating student development is difficult. E-learning is essential for egalitarian education since it uses technology and contemporary learning techniques. This review research found three ways for predicting online course performance: i) To choose the best features to raise student performance; ii) The most effective algorithms for transforming unbalanced data into balanced data; and iii) The best machine learning algorithms to predict online course performance. This study also offered insights into using hybrid techniques and optimization algorithms to educational data sets to improve student performance prediction. The utilization of data from independent e-learning products to enhance education today requires data processing to ensure quality. In addition to these techniques, our abstract highlights the effectiveness of hybrid feature selection methods like L2 regularization (Ridge) and recursive feature elimination (RFE) and ensemble learning models like random forest, gradient boosting, and AdaBoost. These approaches considerably improve prediction accuracy and tackle huge and sophisticated educational dataset challenges. Our work uses advanced machine-learning approaches to optimize e-learning settings and boost academic achievements in the shifting online education landscape caused by the COVID-19 pandemic.
Optical coherence tomography angiography image classification and analysis of diabetic retinopathy, using Wasserstein generative adversarial network augmentation
Authors: Hatode Pranali Pradeep , Edinburgh Maniroja
Published: October 2024

Deep learning algorithms effectively work with, a significant amount of data. trained on small datasets tend to have poor generalization. Data augmentation techniques can be used to make better use of existing training data, improving the applicability of deep learning methods. However, traditional data augmentation methods often produce limited additional credible data. The deep learning approach's performance can be enhanced by generating new data by employing generative adversarial networks (GANs). Although GANs have been extensively used to improve the performance of convolutional neural networks (CNNs), there has been relatively less research on data augmentation methods specifically for GAN training. This study focuses on using a Wasserstein GAN (WGAN) architecture for generating synthetic optical coherence tomography angiography (OCTA) images of diabetic retinopathy to aid in the detection of different types of diabetic retinopathy diseases, including proliferative diabetic retinopathy (PDR), Severe non-PDR (NPDR), Moderate NPDR, and Mild NPDR. WGAN, provides the generator with a more informative learning signal, making training more stable, particularly in high-dimensional spaces. The trained WGAN model is saved in .h5 file format (HDF), converted to portable network graphics (PNG) image format, and then classified into different categories of diabetic retinopathy using a ResNet50 model with various fine-tuning methods. The proposed model has demonstrated better results than the previous study. 99.95% accuracy is exhibited.
Load forecasting using fuzzy logic, artificial neural network, and adaptive neuro-fuzzy inference system approaches: application to South-Western Morocco
Authors: Stitou Hicham , Atillah Mohamed amine , Boudaoud Abdelghani , Aqil Mounaim
Published: October 2024

The demand for energy on a global scale is continuously rising due to the expansion of energy infrastructure and the increasing number of new appliances. To address this growing need, an efficient energy management system (EMS) has become indispensable. By implementing EMS, both residential and commercial buildings can significantly improve their energy efficiency and consumption. One crucial aspect of enabling EMS to operate efficiently is load forecasting. The accuracy of load forecasting depends on numerous factors. A reliable load forecast model should consider the region’s weather forecast, as it plays a crucial role in developing an accurate prediction. This study is about the medium-term load forecasting (MTLF) for the Province of Taroudant, Morocco, using historical monthly load and weather data for five years (2018 to 2022). To forecast consumed energy three methods are used namely artificial neural network (ANN), fuzzy logic (FL) and adaptive neuro-fuzzy inference system (ANFIS). This paper selects absolute percentage error (APE), mean absolute percentage error (MAPE), correlation coefficient (R) and root mean square error (RMSE) to compare and evaluate the prediction accuracy of models. It has been observed through results analysis that the ANFIS model produces very accurate forecasting prediction with MAPE of 4.75% while ANN and FL models give respectively MAPE of 7.36% and 8.42%.
A comparative study of long short-term memory based long-term electrical load forecasting techniques with hyperparameter optimization
Authors: Mani Geetha , Seetharaman Suresh , Kandasamy Jothinathan , Ladha Lekshmy Premachandran , Mohandas Anish John Paul , Sivasubramoniam Swamy , Renugadevi Valarmathi Iyappan
Published: October 2024

Long-term load forecasting (LTLF) is crucial for reliable electricity supply, infrastructure planning, and informed energy policies, ensuring grid stability and efficient resource allocation. Traditional methods, like statistical models and expert judgment, rely on historical data but may struggle with dynamic changes in technology, regulations, and consumer behavior. Addressing challenges such as economic uncertainties, seasonal variations, data quality, and integrating renewable energy requires advanced forecasting models and adaptive strategies. This research aims to develop an efficient LTLF model for the Coimbatore region in Tamil Nadu, India, using long short-term memory (LSTM) networks. While LSTM has limitations in capturing long- term dependencies and requires high data quality and complex management, optimizing hyperparameters, including through the opposition-based hunter- prey optimization (OHPO) technique, is explored to enhance its predictive performance. The results show that the proposed OHPO-configured LSTM model for LTLF achieves superior performance compared to other techniques, with a mean square error (MSE) of 0.25, root mean square error (RMSE) of 0.5 and mean absolute percentage error (MAPE) of 0.27. This research underscores the significance of improving LTLF precision for informed decision-making in infrastructure planning and energy policy formulation.
A semantic similarity search engine for movies
Authors: Mustafa Ahmad , Mheidat Hammam , Shatnawi Adam
Published: October 2024

Semantic similarity has been gaining traction in the field of natural language processing. It is a measure of how similar two pieces of text are in terms of their meaning. It can be used to improve search engine results. We propose a deep learning-based approach to build a semantic similarity search engine for movies based on a movie summary. Filmmakers can gain insight into audience preferences and trends, allowing them to create more engaging and successful films. The dataset used in this study was gathered from internet movie database (IMDb), it includes movie summaries along with their corresponding name movies. The test dataset was generated using ChatGPT to be very close to human input. The universal sentence encoder (USE) model presented promising results in semantic similarity, the model results show that for the top 5 similar movies, the model returned 176 out of 300 movies (58.6%). For the top 10 similar movies, the model returned 211 out of 300 movies (70.3%). Additionally, for the top 15 similar movies, the model returned 229 out of 300 movies (76.3%). And, for the top 20 similar movies, the model returned 249 of 300 movies (83%). This method can be applied to movie recommendation systems or to organize films in a collection automatically.
Framework for detecting and resisting cyberattacks on cyber-physical systems in internet of things
Authors: Metan Jyoti , Mathapati Mahantesh , Yogegowda Prasad Adaguru , Ananda Kumar Kurilinga Sannalingappa
Published: October 2024

Cyber-physical system (CPS) is an integral part of an internet of things (IoT) with established wide spread applications. An increasing concern towards being highly vulnerable to various forms of dynamic cyber-attacks has been increasingly evolving. A review of existing research methodology showcases complex solutions that can offer sub-optimal security strength when exposed to dynamic cyber-attack forms while increasing the computational burden. Therefore, this manuscript presents a novel yet simplified computational framework capable of determining and resisting critical anomalies within internet-of-cyber physical systems (IoCPS). The presented scheme contributes towards preprocessing following a distinct oversampling method targeting balancing the data. An ensemble machine learning model using a discrete variant of AdaBoost and neural decision tree (NDT) has been implemented to optimize the learning process and improve the threat detection efficiency. The comparative outcome of the proposed study showcases that it offers approximately 7.2% increased threat detection accuracy and approximately 68% reduced response time compared to frequently adopted learning mechanisms towards threat detection over an IoT environment.
Youth and patriarchy in the ICT society: A reflection on symbolic gender violence in social media
Authors:   Universidad Austral de Chile, Flores Paula , Browne Rodrigo ,   Universidad Austral de Chile, Chile.
Published: October 2024

This article analyzes how representations of gender violence - a result of patriarchy - are transformed in a society influenced by information and communication technologies, a process in which young people are protagonists. The article specifically focuses on social media, as their level of use by Chile’s young people has reached a transcendent level that reconfigures identity processes and relational paradigms. A scenario is configured that magnifies or annuls expressions of gender violence (both explicit and symbolic). The authors carry out an analytical theoretical reflection using the literature review methodology, highlighting the need to question how the Internet impacts the way in which young people construct their gender identities and social relationships as actors within a paternalistic culture.
Cromatismo espacial em Steven Holl
Authors: Silva Bruno Firmino Costa da , Amorim Anália Maria Marinho de Carvalho
Published: October 2024

El artículo aborda la relación entre música y arquitectura a partir de la operación de transposición gráfica de la partitura musical para generar ideas formales-espaciales en arquitectura. Para comprender el diálogo entre ambos campos se utilizó como objeto de estudio el proyecto Sarphatistraat Offices del arquitecto Steven Holl, que utilizó como precedente en su desarrollo la partitura gráfica de la pieza Patterns in a Chromatic Field, de Morton Feldman. concepción. Además de discutir la transposición gráfica, el texto hace una analogía entre el cromatismo musical y el cromatismo espacial a través del comportamiento de la luz en el volumen del edificio estudiado.
Classifying the Perception of Difficult Life Tasks: Machine Learning and/or Modeling of Logical Processes
Authors: Bityutskaya Ekaterina V. , Gasanov Elyar E. , Khazova Kseniia V. , Patrashkin Nikita A.
Published: October 2024

Background. Although quite a few classifications of coping strategies have been proposed, with different premises, much less is known about the methods of interpretation and how people using different types of coping perceive their life difficulties. Objective. To develop a verifiable algorithm for classifying perceived difficulties. The proposed classification was developed deductively, using “approach–avoidance” as the basis for cognitive activity aimed at taking on (approaching) a difficult situation or escaping from it, avoiding a solution to the problem. The classification comprises 1) driven, 2) maximum, 3) optimal, 4) ambivalent, and 5) evasive types of perception of difficult life tasks (DLTs). Types 1, 2, and 3 correspond to approaching a difficult situation, and 5 to avoiding it. Type 4 involves a combination of approach and avoidance. Design. The type is determined by an expert psychologist in a complex way, based on a combination of 1) the respondent’s profile according to the “Types of Orientations in Difficult Situations” questionnaire (TODS) and 2) features that are significant for the type as shown in qualitative data – descriptions of DLTs (answers to open questions). Machine learning methods and A.S. Podkolzin’s computer modeling of logical processes are used to develop the algorithm. The sample comprised 611 adult participants (Mage = 25; SD = 5.8; 427 women). Results. Using machine-learning algorithms, various options were tested for separation into classes; the best results were obtained with a combination of markup and questionnaire features and sequential separation of classes. Using computer modeling of logical processes, decisive classification rules were tested, based on the psychologist’s description of the features of the type of perception. The classification accuracy using these rules of the final algorithm is 77.17% of cases. Conclusion. An algorithm was obtained that allows step-by-step tracing of the process by which a classification problem is solved by the psychologist. We propose a new model for studying situational perception using a mixed research design and computer-modeling methods.
Response of the Indian Ocean Meridional Overturning Circulation to the Subtropical Indian Ocean Dipole
Authors: Zhang Linfang , Leung Marco Yu-Ting , Yao Fengchao , Zhang Guangli , Wang Dongxiao
Published: October 2024

Using the German contribution to Estimating the Circulation and Climate of the Ocean, version 3 (GECCO3), reanalysis data, this work explores the Indian Ocean meridional overturning circulation (IMOC) variability and mechanism during the mature phase of the subtropical Indian Ocean dipole (SIOD). The IMOC is decomposed into the Ekman component, geostrophic component, external mode, and residue. The IMOC exhibits counterclockwise circulation anomaly in 0°–30°S during the mature phase of the SIOD. While the Ekman component dominates in 10°–30°S, the geostrophic component prevails in 5°–20°S. During the mature phase of the positive SIOD events, while an anticyclonic wind anomaly over the southern Indian Ocean causes a convergence and sinking of the seawater near 30°S, a cyclonic wind field anomaly near 10°S induces a divergence and rising, causing a counterclockwise Ekman component anomaly in 10°–30°S. The geostrophic component anomaly in 5°–20°S is caused by the sea level anomaly (SLA) gradient around 10°S related to a Rossby wave. A linear, 1D baroclinic Rossby wave model shows that the negative SLA in the west is caused by local and remote wind forcing, whereas the positive SLA in the east is generated by radiation from the eastern boundary and is slightly contributed by local wind forcing. Further, Parallel Ocean Program, version 2 (POP2), experiments confirmed that the Ekman component anomaly primarily responds to the wind field of the mature phase of the SIOD and revealed that the geostrophic component anomaly is affected by the wind field of both the developing and mature phases of the SIOD.
Optimal Transition Paths for AMOC Collapse and Recovery in a Stochastic Box Model
Authors: Soons Jelle , Grafke Tobias , Dijkstra Henk A.
Published: October 2024

There is strong evidence that the present-day Atlantic meridional overturning circulation (AMOC) is in a bistable regime, and hence, it is important to determine probabilities and pathways for noise-induced transitions between its equilibrium states. Here, using large deviation theory (LDT), the most probable transition pathways for the noise-induced collapse and recovery of the AMOC are computed in a stochastic box model of the World Ocean. This allows us to determine the physical mechanisms of noise-induced AMOC transitions. We show that the most likely path of an AMOC collapse starts paradoxically with a strengthening of the AMOC followed by an immediate drop within a couple of years due to a short but relatively strong freshwater pulse. The recovery on the other hand is a slow process, where the North Atlantic needs to be gradually salinified over the course of 20 years. The proposed method provides several benefits, including an estimate of probability ratios of collapse between various freshwater noise scenarios, showing that the AMOC is most vulnerable to freshwater forcing into the Atlantic thermocline region. Moreover, a comparison with a quasi-equilibrium approach reveals the contrasts in the behavior of a bifurcation- and noise-induced collapse of the AMOC.
PDK Connection: Organizational news about PDK International
Authors: Lane James F.
Published: October 2024

In this month’s PDK Connection, CEO James F. Lane offers an update on the association; two current and two future educators discuss what inspires them; member DeShanna Reed shares her story as a teacher who entered the field through alternative certification; and a member poll examines competency-based strategies used in the classroom.
Tuberculous calcific constrictive pericarditis in an adult female with a past history of abdominal tuberculosis
Authors: Bansal Dhruv , Negi Moti Lal , Bansal Priya , Pandey Aakash
Published: October 2024

Tuberculous pericarditis, one of the rarer extrapulmonary manifestations of tuberculosis, remains a challenge in endemic countries such as India. As the clinical presentation of tuberculous pericarditis is highly variable, the diagnosis is often missed, delayed, or incorrectly diagnosed. Constrictive pericarditis (CP) is one of tuberculous pericarditis's most serious and advanced sequelae. Several invasive and non-invasive diagnostic modalities are crucial to address the challenges of confirming the diagnosis of CP in suspected cases. This study reports the case of an Indian adult female with a past history of previously treated abdominal tuberculosis diagnosed anew with tuberculous calcific constrictive pericarditis.
Training the Future: Themes From a Content Analysis of Psychology Research Lab Manuals
Authors: Lilley Malin K. , Castellanos Isabella , Wicks Anastacia , Raya Alena , Weatherford Dawn R. , Huynh Ho Phi
Published: October 2024

Background

Lab manuals help researchers and students share a common understanding of the rules, guidelines, and expectations related to being involved with a research laboratory. However, no formal guidelines direct the creation of lab manuals in psychology.

Objective

In this study, we conducted qualitative analyses of 10 psychology lab manuals to provide some broad guidelines for crafting a lab manual that would improve the research experience for undergraduate students.

Method

Taking an inductive approach, a team of researchers created codes from the contents of the manuals, and then derived themes from those codes.

Results

Themes included lab philosophy and direction, expectations and roles in the lab, communication inside and outside the lab, ethics, preparing for research, conducting research, presenting research, networking, and benefits of undergraduate research.

Conclusion

We defined and provided specific examples of each theme for consideration by educators and discussed limitations of our approach.

Teaching Implications

Ultimately, these themes could serve as a modest outline from which teacher-scholars can create their lab manuals from scratch or to revise/expand current manuals.
Evaluation of lymphovascular space invasion in endometrial carcinoma by APTw and mDixon-Quant
Authors: Meng Xing , Zhang Xiaowen , Tian Shifeng , Lin Liangjie , Chen Lihua , Wang Nan , Liu Ailian
Published: October 2024

Background

Lymphovascular space invasion (LVSI) is a strong and independent risk factor that increases the probability of endometrial carcinoma (EC) recurrence and reduces the survival rate of patients.

Purpose

To investigate the value of amide proton transfer weighted (APTw) and mDixon-Quant techniques in evaluating EC lymphovascular space invasion (LVSI).

Material and Methods

Data of 50 EC patients (18 LVSI+ and 32 LVSI–) confirmed by surgery and pathology were retrospectively analyzed. Preoperative magnetic resonance imaging (MRI) scans included APTw and mDixon-Quant imaging. APT, transverse relaxation rate (R2*), and fat fraction (FF) plots were obtained by postprocessing. The APT, R2*, and FF values of the two groups of cases were measured by two observers.

Results

The agreement between the two observers was good. The mean APT, R2*, and FF values of LVSI+ EC were 2.947% ± 0.399%, 20.605 /s (range = 18.525–27.953), and 2.234% ± 1.047%, respectively, while the parameters of LVSI– EC were 2.628% ± 0.307%, 18.968 /s (range = 16.225–20.544), and 2.103% ± 1.070%, respectively. The APT and R2* values of LVSI+ EC were higher than those of LVSI– EC ( P < 0.05). There was no significant difference in FF value between the two groups. The AUC values of APT, R2*, and APT + R2* for LVSI were 0.751, 0.713, and 0.781, respectively (all P > 0.05). APT value was moderately correlated with R2* value (r = 0.528, P < 0.001) and weakly correlated with FF value ( r = 0.312, P = 0.027).

Conclusion

APTw and mDixon-Quant techniques could evaluate the LVSI status of EC, and their combined application could improve diagnostic efficiency.
Abscesses in different body sites among children: a single-center prospective study
Authors: Ajaj Omar Abdulqader , Hashim Hashim Talib , Hussein Zainab Ali
Published: October 2024

Objectives

An abscess is a localized collection of pus contained within a fibrous capsule. In this study, we aimed to determine the demographic pattern, common anatomical sites, risk factors, and the microbial profile of abscesses in different body sites among children.

Methods

We conducted a prospective study in our pediatric surgical department among children with abscesses in different body sites between January 2019 and December 2022.

Results

During the study period, 85 children were included. The participant age range was 0 to 14 years old, and 66% of participants were boys. The most common anatomical sites where abscesses formed were the pelvis (n = 29, 34%), abdomen (n = 22, 26%), neck (n = 14, 16%), and extremities (n = 12, 14%). Risk factors of abscesses in different body sites included cannulation, lymphadenitis, mastitis, perforated appendix, and perianal fistula. We observed that 74% of abscesses were of a polymicrobial nature.

Conclusion

The most common anatomical sites for abscesses in children included the pelvis, abdomen, neck, and extremities. Most abscesses in these sites were polymicrobial in nature.
Squamous cell carcinoma of the cervix associated with choriocarcinomatous differentiation: a case report and review of the literature
Authors: Wang Fenfen , Xu Shanshan , Zhang Xiaofei , Qian Yeqing , Chen Yaxia
Published: October 2024

The morphology of the choriocarcinomatous variant of cervical squamous cell carcinoma (SCC) suggests an undifferentiated aggressive biological behaviour and a poor outcome, for which standard treatment has not been established. In addition, cases are rarely reported, with only five cases of patients with cervical carcinoma with choriocarcinoma reported previously in the literature. This current case report describes in detail a patient who was diagnosed with cervical SCC mixed with choriocarcinomatous differentiation. The case report includes details of the diagnosis, pathology, short tandem repeat genotyping, treatment and follow-up of this patient. As there is no standard treatment for this variant, the patient underwent surgery followed by radiotherapy. Unfortunately, 4 months after therapy discontinuation, radiological evaluation and laboratory tests documented a recurrence of the disease and the patient died. This report also systematically reviews the literature on cervical cancer associated with choriocarcinomatous differentiation and the five previous cases. It provides the most up-to-date summary of this disease, including its clinical manifestations, histopathology, diagnosis, treatment and prognosis.
Aging With Emerging Social Welfare and Disappearing Filial Piety: Chinese Migrant Workers’ Pathways Toward Financial Retirement Planning
Authors: Liu Jing , Zhan Heying Jenny , Qiu Fengxian , Setar Anna Catherine
Published: October 2024

This study examines the pathways and mechanisms linking Chinese migrant workers’ migration experiences and their financial retirement planning. Using a mixed-methods approach with 1083 surveys and 32 interviews, this study finds that having a good financial status and social support system and maintaining a hopeful attitude toward retirement are direct pathways toward good financial retirement planning. Good health and hope for retirement are further enhanced by a good financial status and social support. Conversely, poor health and negative employment experiences are linked to poor financial retirement planning. The qualitative findings provide a contextual understanding of the pathways identified in the quantitative analyses. Migrant workers often face a dilemma between self-reliance for retirement and relying on filial piety. These findings apply not only to Chinese migrant workers but also to all migrant workers with limited access to healthcare and public pensions for retirement.
Ship pictures on the municipal seals of medieval seaports: Ship seals show notices to mariners on tokens of municipal identity
Authors: Blussé Dolph
Published: October 2024

Many late-medieval western European seaports showed a ship in the central image of their municipal seal (a ship seal). Municipal seals to identify and authorize documents ranging from important supra-regional charters to local ephemera were widely used at the time, and probably also on trade contracts and (predecessors to) maritime bills of lading. This study of 115 ship seals from 80 seaports between ad 1200 and ad 1450 reveals that the central image of all ship seals contained striking details, strongly reminiscent of semiotic pictorial signals that conveyed information. The supposed signals were assigned a probable meaning by linking them to nautical aspects of the approach to each seaport and to the state of the harbour at the time. The results strongly suggest that the signals provided information about the risks and hazards in the approach to port, the water depth, and the facilities at the port in question. Compelling evidence comes from ports issuing successive ship seals over an extended period of time: updated images with properly adjusted signals were shown on subsequent seals when significant changes in the approach or port facilities had occurred, but the signals remained the same as long as the local situation stayed unchanged. These findings provide a clear explanation for the divergent ship portraits; ship seals served a dual purpose: first, to confirm the identity of the seaport and the authority of the council in its various actions and tasks, and, second, to provide up-to-date insight into the nautical challenges when approaching the port and in the local port facilities, in a manner similar to the current ‘notices to mariners’ from hydrographic offices. The rapidly increasing number of ports using ship seals in the thirteenth century and their continued use for two centuries supports the success of this late-medieval nautical information system. The study results also provide up-to-date impressions of the 80 seaports involved and of various nautical aspects of the hazardous maritime traffic in the thirteenth and fourteenth centuries, about which written archival information is scarce. The results may help to put the rare yield of maritime archaeology of this period into perspective. The ship seals lost their appeal as buoys and beacons made approaches safer, harbours became deeper and better equipped, and written information (‘rutters’) became widely available.
Impact of renality-CASE training on knowledge, skills, and practices in peritoneal dialysis catheter placement among nephrologists
Authors: Sahutoglu Tuncay , Kazancioglu Rumeyza , Ozkan Mehmet Kemal , Erkasap Pelin Çelikbilek , Ates Kenan
Published: October 2024

Introduction

Despite being an effective home-based kidney replacement therapy, peritoneal dialysis (PD) remains underutilized. The aim of the Renality-CASE training program was to assess its impact on nephrologists by expanding their knowledge of PD therapy and enhancing their catheter placement skills to better offer the PD option.

Methods

The Renality-CASE program provided two days of training, including theoretical lectures, virtual reality sessions, and hands-on practice of PD catheter placement on live pigs. Eighty-eight participants attended four sessions. An anonymized online questionnaire collected demographic data, self-assessments of knowledge and skills, and feedback. Pre- and post-training comparisons were analyzed using paired samples t-tests and the Wilcoxon signed-rank test, with significance at p < 0.05.

Results

Fifty-seven out of 88 participants responded to the survey one-year post-training. Post-training, 94.7% felt at least moderately knowledgeable compared to 61.4% pre-training ( p < 0.001). Confidence in skills increased to 73.6% post-training from 26% pre-training ( p < 0.001). The number of PD catheters placed by participants also rose significantly ( p = 0.012). The program received positive feedback and high satisfaction rates.

Conclusions

The Renality-CASE program significantly improved nephrologists’ PD catheter placement skills through comprehensive training. Despite limitations such as sample size and response bias, the study highlights the need for standardized PD training to enhance clinical practice and increase PD utilization in CKD management.
Modelling and optimisation of thermal expansion and thermal conductivity using face-centred central composite design-based response surface methodology: An experimental approach
Authors: Balachandran Gurukarthik Babu , Baskaran Vishnu Karan , Thangaraj Hariharasudhan , David Prince Winston
Published: October 2024

Due to the shortage in availability of potable water the risk of water scarcity has reached its heights. Researchers have developed methods to increase the availability of potable water in various ways. Solar desalination technique using solar still would assure to have efficient water yield. Thermal energy plays an essential role in the output efficiency of PV panel and the production of solar still. The purpose of our paper is focused on optimisation of thermal properties of Aluminium, Copper and Iron. To obtain the optimised value of thermal properties namely thermal conductivity and thermal expansion an approach called response surface methodology is used. It would reduce the count in which the experiment is repeated as it has a pre-determined set of input parameters and further helps in cost reduction. The optimised input parameters are the volume percentage of Aluminium-0.17 percentage, Copper-2.42 percentage and Iron-2.64 percentage. The optimized output values obtained are of thermal conductivity and thermal expansion. A method called the two-probe method is used for determining the thermal conductivity and the Buoyancy method helps in determining the thermal expansion of the considered samples. The obtained optimised values are inferred from ANOVA. For thermal conductivity, the Adjusted R2 is 0.9796 and for thermal expansion the Adjusted R2 is 0.9032. The Standard Deviation is observed to be 0.202 whereas the Mean value is observed to be 0.4666. The resulting optimised values of thermal conductivity and thermal expansion are 0.467 W/(m*K) and 1.534*10−5(1/K). This ensured the reduced repetition of the experimental run to identify the optimal output value of the system using response surface methodology through central composite design.
Comparison of the thermal and vibrational stress relief methods in terms of mechanical and metallurgical aspects of rolled AA2024 specimens
Authors: Jafari Vardanjani Mehdi , Safavi Seyed Mohsen
Published: October 2024

Vibrational stress relief (VSR) and thermal stress relief (TSR) are among the most common treatments after manufacturing processes to enhance the mechanical and metallurgical qualities of the products. Residual stress relief is of the important purposes for such treatments. In this study VSR and TSR treatments are compared regarding the mechanical and metallurgical properties of cold rolled AA2024 specimens. VSR and TSR treatments are separately performed on the identical cold rolled specimens after which dimensional distortion, tensile strength, hardness, and microstructure are evaluated on the specimens while residual stress has been analyzed in finite element model. According to the results, VSR treatment has had better performance in the field of distortion reduction, tensile strength, and elasticity modulus by 25.00%, 2.21%, and 2.98% respectively, while TSR had a slightly more constructive effect on hardness by 2.91%. These could be due to the metallurgical slight effect of stable grain size and shape in VSR versus minor grain growth for TSR. Finite element analysis results have also demonstrated the average reduction of the equivalent residual stress about 31% after TSR treatment, and about 42% after VSR treatment. Grain shape has been influenced slightly by VSR due to minor plastic deformations during mechanical vibration compared to TSR, while grain size has been observed to be slightly more in TSR due to minor grain growth.
Prediction of tensile strength using machine learning algorithms in fused deposition modeling
Authors: Patel Kautilya S , Trivedi Nisarg , Shah Dhaval B , Joshi Shashikant J
Published: October 2024

Additive manufacturing provides unique advantages in creating complex parts, and among its methods, fused deposition modeling uses molten thermoplastic layers for diverse applications. Machine learning, an artificial intelligence technique, enhances additive manufacturing capabilities by enabling predictions and improving process control, designs, material properties, and production efficiency. The paper aims to utilize accurate machine learning-based algorithms for data analysis, and parameter optimization to predict the mechanical properties of the product. The results obtained through the support vector machine model integrating with the Tsai–Wu theory are in good agreement with experimental values observed at a print speed of 40 mm/s and a layer thickness of 0.2 mm. While the Levenberg–Marquardt algorithm in the artificial neural network has better prediction accuracy with mean absolute percentage errors of 4.22% and 3.53% for tensile and flexural strength, which is higher than the support vector machine. Furthermore, the research emphasizes the significant impact of printing speed on product quality by implementing analysis of variance with a percentage contribution ratio of ∼45%. This comparison allows manufacturers to make informed decisions and effectively optimize the additive manufacturing process. Machine learning algorithm utilization in additive manufacturing holds immense potential for elevating quality, efficiency, and reliability in this transformative industry.
Transport route planning for AGVs under fire hazard based on LSTM and improved A* algorithm
Authors: Guo Zhibao , Wu Zhengtian , Xu Xinyin
Published: October 2024

In response to the challenges of temperature and smoke sensor errors in AGV transportation routes under complex fire scenarios, aiming to reduce economic losses and minimize the risk of firefighting and rescue operations, this paper proposes a method for AGV transportation route planning under fire conditions based on LSTM and an improved A* algorithm. By utilizing LSTM to establish predictive models for temperature and smoke concentration in fire scenarios, this method addresses the issue of AGV sensor errors induced by high temperatures. The spread of fire model is employed to determine the radius of fire influence, serving as constraints for route planning. Subsequently, an enhanced A* algorithm is applied to plan routes, ensuring the safe transportation of AGVs in fire scenarios. Experimental results demonstrate that the proposed method can effectively predict fire conditions, plan safe routes, and enhance the safety of AGV transportation in practical applications.
Grandparent Alienation: A Mixed Method Exploration of Life Satisfaction and Help-Seeking Experiences of Grandparents Alienated From Their Grandchildren
Authors: Degges-White Suzanne , Hermann-Turner Katherine , Kepic Marcela , Randolph Amber , Killam Wendy
Published: October 2024

Grandparent alienation is an intentional behavior in which a parent turns a child against a grandparent through emotional manipulation of the child. In this study, we explored alienation behaviors experienced, life satisfaction, relationship status, and help-seeking experiences of 551 adults who identified as alienated grandparents. Analysis included ANOVA, regression analysis, correlation, and thematic analysis of all participants’ responses to an open-ended question addressing help seeking. Life satisfaction was negatively affected by the number of alienating behaviors experienced. Grandchild brainwashing and withholding information about grandchildren significantly predicted life satisfaction in this sample as did relationship status and participant age. Thematic analysis revealed two main themes related to participants’ help-seeking experiences with counselors: (1) their yearning for support was satisfied through validation, coping skill acquisition, and engaging with an empathic listener, and (2) they experienced disappointment and frustration through clinician lack of experience and understanding. Implications and best practices for counselors are provided.
Investigation of 5-fluorouracil cardiotoxicity in combinational therapy: Influence of risk factors and demographics in a Pakistani population
Authors: Raza Hina , Javaid Mariyam , Rehman Wajiha , Rafiq Sana , Rashid Zermina , Shamim Rahat , Jaradat Abdolelah , Yousif Mohamed Deifallah
Published: October 2024

Introduction

5-Fluorouracil (5-FU) is a chemotherapeutic agent used to treat various types of cancers. Although widely used, it has consistently been attributed to cardiotoxicities after administration. The purpose of this study was to assess the parameters and predictors of cardiotoxicities associated with various 5-FU-based chemotherapeutic protocols in patients with GI/colorectal cancer, as well as the correlation of these cardiotoxic events with age, sex, cumulative dose, and risk factors such as obesity, hypertension, and family history of cardiac diseases.

Methods

A prospective study consisting of 396 patients of both sexes was conducted in the oncology ward of Nishtar Hospital in Multan, Pakistan. Patients were grouped according to the therapeutic protocol they received (5-FU monotherapy or in combination, with different dosing regimens). Electrocardiography and serum troponin levels were used to assess 5-FU-induced cardiotoxicity. In cases where cardiotoxicity was detected, 5-FU treatment was interrupted; nitroglycerin, nitrates, and calcium channel blockers were administered; and cardiac monitoring was initiated. 5-FU was discontinued in all cases of acute myocardial infarction.

Results

Of the 396 patients, 28.5% reported different cardiotoxic symptoms after receiving various 5-FU-containing protocols. 35% had anginal pain, 13% suffered a myocardial infarction, 11% developed hypertension, and 10% presented heart failure. Patients receiving 5-FU combination therapy showed cardiotoxic events that were significantly different from those on 5-FU monotherapy. Based on the ECG results, only the QTc-d interval increased significantly (p < 0.001) after therapy. 68% of the patients had troponin levels > 2 ng/mL at the end of treatment.

Conclusions

Pre-existing cardiac diseases, treatment duration, smoking, and obesity were found to be influential components in the development of cardiotoxicity, and patients with cancer should be closely monitored during 5-FU chemotherapy.
Longitudinal structural changes in central serous chorioretinopathy: A multimodal imaging-based study
Authors: Sahoo Niroj Kumar , Ong Joshua , Selvam Amrish , Maltsev Dmitri , Sacconi Riccardo , Venkatesh Ramesh , Reddy Nikitha Gurram , Madan Shivam , Tombolini Beatrice , Lima Luiz H , Pramil Varsha , Anantharaman Giridhar , Casella Antonio Marcelo , Ledesma-Gil Gerardo , Waheed Nadia , Borrelli Enrico , Querques Giuseppe , Chhablani Jay
Published: October 2024

Purpose

To analyse the longitudinal changes in imaging parameters in eyes with acute or chronic central serous chorioretinopathy (CSCR).

Methods

This was a multicentric, retrospective, longitudinal, observational study in patients with a diagnosis of CSCR and having at least 4 years of follow-up. Trend in choroidal thickness (CT), area of double layer sign (DLS), area of retinal pigment epithelium (RPE) alterations, and area of hyper-autofluorescence were analysed.

Results

A total of 175 eyes of 146 patients with a mean age of 52.9 ± 12.5 years were included. Fifty-two eyes had acute and 123 had chronic CSCR at baseline. There was an overall decreasing trend of CT values during follow up (from 354.4 ± 74.8 at baseline to 343.2 ± 109.4 at final visit). There was a steady rise in DLS width and RPE alteration in the overall cohort (from 811.7 ± 760.6 microns and 3.5 ± 3.3 disc areas respectively at baseline to 1209.1 ± 1113.18 microns, 4.1 ± 3.6 disc areas at final visit). An initial rise in hyper-autofluorescent area followed by steady fall was seen in chronic CSCR. A significant positive correlation (r = 0.54, p = 0.004) was seen between change in DLS width and change in area of hyper-autofluorescence. On linear regression analysis, lower CMT and absence of intraretinal fluid at baseline were associated with greater increase in RPE abnormalities.

Conclusion

Acute and chronic CSCR had different pattern of change in imaging parameters with area of RPE alterations in acute CSCR not reaching values close to that of chronic CSCR eyes even after long follow up.