bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Deep Learning Framework with Learning without Forgetting for Intelligent Surveillance in IoT-enabled Home Environments in Smart Cities
Authors: Dalal Surjeet , Dahiya Neeraj , Verma Amit , Faujdar Neetu , Rathee Sarita , Jaglan Vivek , Rani Uma , Le Dac-Nhuong 
Published: November 2024

Background:
Internet of Things (IoT) technology in smart urban homes has revolutionised
sophisticated monitoring. This progress uses interconnected devices and systems to
improve security, resource management, and resident safety. Smart cities use technology to
improve efficiency, sustainability, and quality. Internet of Things-enabled intelligent monitoring
technologies are key to this goal.

Objectives:
Intelligent monitoring in IoT-enabled homes in smart cities improves security,
convenience, and quality of life from advanced technologies. Using live monitoring and risk
identification tools to quickly discover and resolve security breaches and suspicious activity to
protect citizens. Intelligent devices allow homeowners to remotely control lighting, security
locks, and surveillance cameras. Using advanced technologies to regulate heating, cooling, and
lighting based on occupancy and usage.

Method:
This study introduces a deep learning architecture that uses LwF (Learning without
Forgetting) to keep patterns while absorbing new data. The authors use IoT devices to collect
and analyse data in real-time for monitoring and surveillance. They use sophisticated data preprocessing
to handle IoT devices' massive data. The authors train the deep learning model with
historical and real-time data and cross-validation to ensure resilience.

Result:
The proposed model has been validated on two different Robloflow datasets of 7382
images. The proposed model gains an accuracy level of 98.27%. The proposed Yolo-LwF
model outperforms both the original Yolo and LwF models in terms of detection speed and
adaptive learning.

Conclusion:
By raising the bar for intelligent monitoring solutions in smart cities, the suggested
system is ideal for real-time, adaptive surveillance in IoT-enabled households. By embracing
adaptability and knowledge retention, authors envision heightened security and safety levels
in urban settings.
Real Time Object Detection Algorithm in Foggy Weather Based on WVIT-YOLO Model
Authors: Zhang Huiying , Zhang Qinghua , Gong Yifei , Yao Feifan , Xiao Pan 
Published: November 2024

Introduction:
To address the challenges of low visibility, object recognition difficulties,
and low detection accuracy in foggy weather, this paper introduces the WViT-YOLO
real-time fog detection model, built on the YOLOv5 framework. The NVIT-Net backbone
network, incorporating NTB and NCB modules, enhances the model's ability to extract both
global and local features from images.

Method:
An efficient convolutional C3_DSConv module is designed and integrated with
channel attention mechanisms and ShuffleAttention at each upsampling stage, improving the
model's computational speed and its ability to detect small and blurry objects. The Wise-IOU
loss function is utilized during the prediction stage to enhance the model's convergence efficiency.

Result:
Experimental results on the publicly available RTTS dataset for vehicle detection in
foggy conditions demonstrate that the WViT-YOLO model achieves a 3.2% increase in precision,
a 9.5% rise in recall, and an 8.6% improvement in mAP50 compared to the baseline model.
Furthermore, WViT-YOLO shows a 9.5% and 8.6% mAP50 improvement over YOLOv7
and YOLOv8, respectively. For detecting small and blurry objects in foggy conditions, the
model demonstrates approximately a 5% improvement over the benchmark, significantly enhancing
the detection network's generalization ability under foggy conditions.

Conclusion:
This advancement is crucial for improving vehicle safety in such weather. The
code is available at https://github.com/QinghuaZhang1/mode.
Aquaponics and art in education: Encouraging sustainability, ecological awareness, and creative expression in high school
Authors:   University States of Rio Grande do Sul, Brazil, Luciene Monteiro Pimentel Quintas , Rita Rodrigues ,   University States of Rio Grande do Sul, Brazil
Published: November 2024

Biology education equips students with theoretical and practical knowledge, nurturing critical and scientific thinking, alongside awareness of vital life, health, and environmental issues. However, educators in this domain encounter specific challenges when addressing certain subjects in basic education. This research aimed to identify the main difficulties faced by teachers and their experiences with biology content. Through interviews with 38 teachers from both public and private schools, employing a mixed-method approach, this study sought insights into the methodologies used, the obstacles encountered, and the most demanding content areas. It was found that Human Evolution, Genetics, and Cytology pose the greatest challenges, primarily due to limited time for ongoing professional development. To address these issues, the study proposes a lesson plan model leveraging artificial intelligence (AI) tools designed to generate text responses and ideas for practical activities, experiments, and engaging resources. By equipping teachers with AI tools for lesson planning, the research aims to mitigate time constraints, offering resources and strategies to create more dynamic, varied, and student-centered educational activities. This initiative seeks to enhance the effectiveness and stimulation of biology teaching in basic education, contributing to the cognitive, critical, and scientific development of students.
Enhancing early childhood music education through interactive learning objects
Authors:   University of São Paulo, Brazil, Sandra Pereira Gomes , Durval Rodrigues ,   University of São Paulo, Brazil
Published: November 2024

Introducing interactive learning objects into early childhood music education revolutionizes how young learners engage with music, emphasizing creativity and cognitive growth. This research evaluates the impact of such tools on enhancing musical understanding and appreciation beyond traditional methods. By incorporating interactive games and digital resources, educators can offer immersive experiences that bridge pedagogical gaps in teaching complex musical concepts. These innovative approaches encourage children to explore music through play, significantly boosting their engagement and fostering a deeper connection with music. Findings highlight that interactive learning not only aids in developing musical skills but also creates a more dynamic and participatory educational environment. The effectiveness of learning objects in music education suggests a new path for addressing educational challenges, empowering teachers to deliver engaging and comprehensive lessons. This shift towards integrating interactive tools in music teaching promises to cultivate a generation of children proficient in music and deeply connected to its expressive power, underscoring the transformative potential of interactive learning in early music education.
Reprocessing of Aluminum Production Residues by Roasting and Magnetic Separation
Authors: Silva Juliana Costa , Ramalho Janaína Maria Nogueira , Fonseca Gabriel Mateus Cubi , Oliveira Guilherme José Ramos , Horta Daniela Gomes 
Published: November 2024

Objective: The aim of this work was to contribute for the development of a strategy for the reprocessing of red mud and marginal bauxite for iron recovery by magnetic roasting.

Theoretical Framework: Red mud and marginal bauxite contain a considerable amount of iron, but it is not possible to recover this iron content with conventional mineral concentration techniques. Magnetic roasting is a process applied to alter the magnetic properties of minerals, typically aimed at the conversion of weakly magnetic iron oxides, such as goethite and hematite, into magnetite, a strongly magnetic phase. After roasting, iron can be recovered by magnetic separation even at low field intensities.

Method: To achieve the purpose of this work, marginal bauxite and red mud samples were prepared, characterized chemically and mineralogically, roasted in a rotary kiln at different temperatures and with or without the addition of coke or hydrogen as reducing agents; and subjected to magnetic separation. The products of the roasting pre-treatment were characterized in terms of mineralogical composition by X-ray powder diffraction to assess the phase changes that occurred.
Results and Discussion: The results obtained revealed that magnetic roasting is a promising technique to recover iron from marginal bauxite and red mud. Although recoveries higher than 90% were achieved, the selectivity of magnetic separation was low, indicating that Al was also being recovered in the magnetic product. Further conditions may be investigated.

Research Implications: Tons of red mud are disposed of in tailing dams, and tons of marginal bauxite are stored in ore piles. Both pose environmental risks that might be significantly reduced if a strategy for reprocessing these aluminum production residues is applied.

Originality/Value: The relevance and value of this research are evidenced by the promising results achieved, that showed that it is possible to recover iron from red mud and marginal bauxite by combining roasting pre-treatment and magnetic separation.
Using Visual Basic for Applications to Implement Markov Analysis in Market Share Forecasting
Authors: İnanç Şahin 
Published: November 2024

Objective: This paper is an examination of the use of Markov analysis in market share prediction, and the use of Visual Basic for Applications (VBA) to increase the speed and precision of the computation and model.

Theoretical Framework: The Markov analysis, used to predict future states from current transitions, sheds light on the consumer behavior and brand loyalty dynamics With VBA, the data processing can be automated, and transition matrices built that are dynamic, so that forecasts can be updated in real time.

Method: Markov analysis is based on transition matrices, which define the probabilities of transitioning from one state to another. These matrices are then used to predict long term probabilities by raising the transition matrix to some power that is equal to the number of future steps being studied.

Results and Discussion: By utilizing Visual Basic for Applications (VBA), the study facilitates the swift creation of transition matrices and enables real-time modifications in response to new data. This implies that with the use of VBA and Markov analysis, not only is the forecasting process made easier, but also more accurate predictions are made, and for that reason VBA should be one of the tools that any firm looking to maximize its market share strategies should employ.

Research Implications: This capability results in a more flexible and responsive forecasting instrument, enhancing the overall adaptability of market share predictions.

Originality/Value: The originality of this research is found in its innovative methodological framework that builds upon conventional Markov analysis.
Microprudential Regulations and Bank Lending in Nigeria
Authors: Biiranee Kelvin Friday Barida , ZorBari-Nwitambu Bright 
Published: November 2024

Objectives: The current study aimed at investigating the relationship between microprudential regulations and bank lending in Nigeria.

Theoretical framework: The theoretical underpinning of the study is drawn on the theory of risk-return tradeoff which maintains that potential return rises with an increase in risk.

Method: Financial data of 13 banks over a period of 15 years were sourced from the repository of Central Bank of Nigeria, and were analyzed using the Panel (Least Squares) Multiple regression technique.

Results and Discussions: The result of analyses revealed a high R-squared value of 0.75, an indication that microprudential regulation accounted for 75% variation in profitability in the Nigeria banking institution. Also, the F-statistic of 101.31 and a p-value of 0.000, indicated that the Ordinary Least Square regression model is statistically significant at the 1% level. Overall, microprudential regulation impact on lending and profitability in Nigeria.

Research Implications: More so, the study’s relevance is evident in its applications to the principles and practice of lending, fund mobilization and other finance related policies and decisions in the banking sector.

Originality/ Value: The outcome of the current study is valid and can be used for statistical inference. The conceptual framework of the study is a novel model that demonstrates how the variables in the study are interconnected. Furthermore, the model specification presents a viable template for financial speculations and calculations.
The Role of the Federal Institutes of Education, Science and Technology in Forming a Workforce Aligned with Circular Economy Principles and Strategies
Authors: Faria Emília de Oliveira , Oliveira Phillip , Chagas Milton Jarbas Rodrigues , Lima-Silva Alisson 
Published: November 2024

Objective: This study aims to assess the adherence of the themes and the current curricular matrices of the courses after high school at the Brazilian Federal Institutes of Education, Science, and Technology regarding the principles and strategies of the Circular Economy (CE).

Theoretical Framework: In the current scenario of resource scarcity and climate emergency, CE emerges as a viable alternative. In Brazil, the Federal Institutes (FIs) can play a crucial role in training a qualified workforce for CE, offering professional education that meets market demands.

Method: The methodology adopted is classified as qualitative and applied. Regarding its classification, it is descriptive. Regarding the design, it is classified as a documentary since the primary data source was the IFs' websites.

Results and Discussion: The results reveal that 26.17% of the 554 courses evaluated have content related to CE. The analysis showed a predominance of disciplines, such as Environmental Management, but a gap in specific CE courses.

Research Implications: From a practical perspective, this study provides insights into the need for changes in the current economic model regarding public education policies adopted in Brazil to prioritize areas of study. As for the theoretical contribution, the study discusses conceptual issues of CE based on findings from the Brazilian context in order to fill a research gap.

Originality/Value The research highlights the importance of training professionals with critical and practical skills in CE, highlighting the need for curricular reform that meets new market demands and promotes a circular mindset.
PENGARUH FUNDAMENTAL MAKROEKONOMI TERHADAP PENANAMAN MODAL ASING DI ASEAN
Authors: Kristianto Andriyan , Zuhroh Idah , Anindyntha Firdha Aksari 
Published: November 2024

This study aims to see the effect of macroeconomics fundamental that proxied by economic growth, inflation and interest rates on foreign investment in ASEAN 5. This research uses panel data regression analysis. The data source is from World Bank that are consist of Foreign Direct Investment, Gross Domestic Product, inflation, and interest from 2007 to 2022. The results show that the Gross Domestic Product has a positive effect on Foreign Direct Investment. Meanwhile, the inflation and interest rates have a negative effect on Foreign Direct Investment
Bow Hunter’s syndrome surgical approach and outcome: Two new cases and literature review
Authors: Elizondo-Ramirez Javier , Herrera-Castro Jose Carlos , del Rio-Martinez Christopher Jesus , Torres-Figueroa Oscar José , Munoz-Cobos Arturo , Ordonez-Solorio Luis Alberto 
Published: November 2024

Background:Bow Hunter’s syndrome (BHS) is a rare entity known as rotational vertebral artery occlusion syndrome. Classically, it presents with nausea, vertigo, and dizziness elicited by extension or rotation of the neck. There are several management approach modalities, including surgical and nonsurgical alternatives.Methods:We conducted an electronic database search on PubMed and Scopus. The search was performed on February 18, 2024, using a combination of keywords related to Bow Hunter Syndrome regarding management. From the latter query, 97 results followed, from which we included 76 and excluded 21 due to the information being irrelevant to our study and non-retrievable publications.Results:A total of 121 patients were retrieved. The mean age of presentation was 50 years, with a female-to-male ratio of 3:1. There were 108 adult cases, and only 13 were pediatric and adolescents. Symptoms were elicited by right rotation (46%). The most affected levels were C1-C2 (44%). The anterior approach was the most common (40%) and had a better outcome (84%), followed by the posterior (30%), which had more cases with partial recovery (19% vs. 16%).Conclusion:BHS management is still challenging as there are many factors that we must consider when deciding on the approach. There is inconclusive evidence on the proper management of these patients. Although the suggestions found in our review and our experience are valuable, no definitive management ensures a good quality of life and outcome for these patients. Further research is needed on this topic.
Delayed clinical response to focused ultrasound thalamotomy in essential tremor in a patient with suboptimal skull density ratio – A case report
Authors: Chowdhury Elma A. , Sivan Vijay , Kumar Rohit Prem , Ruzicka IV Francis F. , Azmi Hooman 
Published: November 2024

Background:
Magnetic resonance imaging-guided focused ultrasound (MRgFUS) thalamotomy offers incisionless treatment for essential tremor or tremor-dominant Parkinson’s disease, gaining acceptance as an alternative to deep brain stimulation. Compared to other methods, it offers real-time efficacy assessment without ionizing radiation.

Case Description:
A 63-year-old male underwent MRgFUS, initially yielding subtle results due to skull limitations. However, significant tremor relief emerged 6 hours post-procedure, sustained for 5 days. Imaging confirmed thalamotomy effect. A second treatment was delivered at day five for longevity.

Conclusion:
For patients with challenging skull characteristics and initial suboptimal outcomes, staged procedures may be considered, with potential delayed benefits and the need for lesion expansion for long-term relief.
The learning curve and outcomes of 1038 endoscopic endonasal transsphenoidal pituitary tumor surgeries – A single surgical team experience
Authors: Gurses Muhammet Enes , Gökalp Elif , Gecici Neslihan Nisa , Shah Khushi Hemendra , Baboun Stephanie Rose , Eatz Tiffany Alyssa , Valdez Mynor Mendez , Costello Meredith Claire , Roach Caleigh Samantha , Merenzon Martin A. , Lu Victor M. , Shah Ashish H. , Ivan Michael E. , Sargi Zoukaa , Komotar Ricardo J. 
Published: November 2024

Background:Pituitary adenomas rank third among adult intracranial tumors, with an incidence of 3.9– 7.4 cases/per 100,000 annually. Transsphenoidal surgery has evolved to include endoscopic endonasal surgery (EEA) in many centers due to technological and surgical advancements over the past two decades. We aim to analyze a 12-year cohort of pituitary adenomas operated through EEA, highlighting the evolution of surgical techniques and outcomes.Methods:A retrospective review of patients undergoing EEA was conducted. A team of an otolaryngologist and neurosurgeon performed surgeries. The cohort was divided into three groups: Phase 1 (P1, 2012–2015), Phase 2 (P2, 2016–2019), and Phase 3 (P3, 2020–2023). Patient demographics, clinical data, and outcomes were collected from electronic medical records and compared over time.Results:The mean age was 54.2 years, with 53.5% being female. The gross total resection rate was 75.6%, increasing from 62.3% in P1 to 76.3% in P3 (P= 0.003). The mean operative duration was 274.61 min, with no significant correlation to case number. Complication rates, excluding cerebrospinal fluid (CSF) leaks, were similar between the groups, with no statistically significant differences observed for complications such as visual deficit, cranial nerve palsy, and epistaxis. However, meningitis decreased significantly from 3.8% to 0.3% (P< 0.001). Intraoperative CSF leaks decreased from 65.1% to 55% (P= 0.003). The need for revision surgery was lower in P3 (8.5% vs. 5.4% vs. 2.1,P< 0.001). Length of hospitalization decreased from 5.3 days to 3.9 days (P< 0.001).Conclusion:Our experience with EEA for pituitary adenomas shows significant improvements in surgical outcomes, reduced complications, and better postoperative management, underscoring the importance of experience, technical refinement, and a multidisciplinary approach.
Tailored sacroplasty for sacral fracture secondary to an epileptic seizure
Authors: Prandzhev Vladimir Stoyanov , Georgiev Nikolay Dinev , Vezirska Donika Ivova 
Published: November 2024

Background:
Sacral fractures causing neurological deficits secondary to epileptic seizures are very rare. They are traditionally treated by laminectomy and sacral fixation. However, minimally invasive techniques such as sacroplasty offer more limited surgery with decreased morbidity. Here, a 23-year-old male with a seizure-induced sacral fracture was successfully treated with a decompressive laminectomy and transcorporal sacroplasty.

Methods:
After a grand-mal seizure, a 23-year-old male presented with severe paraparesis accompanied by bilateral S1/S2 radiculopathy and urinary/fecal incontinence (Gibbons grade 4). When studies documented a Roy-Camille type 2 sacral fracture with severe central compression of the S1/S2 spinal canal, he underwent an S1-S2 laminectomy with transcorporal sacroplasty.

Results:
On the 1st postoperative day, he ambulated without assistance and demonstrated only mild residual sensory deficits (Gibbons grade 2); 1-month later, he walked without assistance.

Conclusion:
A 23-year-old male with a seizure-induced sacral fracture was successfully treated with a decompressive S1/S2 laminectomy/transcorporal sacroplasty.
ПРИМЕНЕНИЕ БАЙЕСОВСКИХ СЛОЕВ ДЛЯ ПОЛУЧЕНИЯ ВЕРОЯТНОСТНЫХ ОЦЕНОК ПРЕДСКАЗАНИЙ ПО РАСХОДУ ГАЗА, ПОЛУЧЕННЫХ ПРИ ПОМОЩИ LSTM НЕЙРОСЕТИ
Authors: Е. АБРАМКИН С , К. ПЕТРОВА А 
Published: November 2024

Для идентификации отклонений в результатах измерения объемов газа может быть применена LSTM нейросеть. Однако необходимо получить вероятностные оценки предсказанных нейросетью значений, математическое ожидание и дисперсию прогнозируемых результатов, и, соответственно, доверительные интервалы этих значений, которые с заданной вероятностью накрывают оцениваемый параметр. В данной статье рассматривается подход, при котором, для получения вероятностных характеристик результатов предсказаний LSTM нейросети, синтезируется нейросеть с дополнительными Байесовскими слоями, при помощи которых, используя распределение весов вместо единичных значений весов, можно оценить неопределенность прогнозов сети.
An LSTM neural network can be used to identify deviations in the results of measuring gas volumes. However, it is necessary to obtain probabilistic estimates of the values predicted by the neural network, the mathematical expectation and variance of the predicted results, and, accordingly, confidence intervals of these values, which cover the estimated parameter with a given probability. This article considers an approach in which, in order to obtain probabilistic characteristics of the results of LSTM neural network predictions, a neural network with additional Bayesian layers is synthesized, with the help of which, using the distribution of weights instead of single values of weights, it is possible to estimate the uncertainty of network forecasts.