bluuebunny/crossref_metadata_2025 huggingface dataset:
Stone formation caused by hem-o-lok clip migration into common bile duct after laparoscopic cholecystectomy and choledocholithotomy: A case report
Authors: Zhang Haidong ,   Department of Hepatobiliary Surgery, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Published: July 2024

Stone formation due to the migration of hem-o-lok clip into the common bile duct after laparoscopic surgeries remains rare. Here, we reported a case of acute cholangiolithiasis caused by hem-o-lok clip migration into the common bile duct 3 years later after laparoscopic surgeries.
Extensor tendons rupture in rheumatoid wrist: The management challenge in the light of a clinical observation
Authors: Erraoui Mariam ,   Department of Rheumatology, Souss Massa university Hospital Center, Morocco.
Published: July 2024

Rheumatoid arthritis is a frequent inflammatory rheumatic disease. It is characterized by synovitis and tenosynovitis. Involvement of wrist is very common especially dorsal face in relation to extensor tendon tenosynovitis. Rupture of these tendon can be occurred as a complication
Multimodal imaging evaluation of femoral exostosis in a pediatric patient: A radiation-free approach
Authors: Quadrelli Marco ,   Synlab Euganea Medica Diagnostic Center, Italy.
Published: July 2024

A 12-year-old girl presented with left knee pain following an accidental fall. Physical examination revealed local pain and restricted joint movement. The patient had no significant prior medical conditions or traumas.
Single live 8 weeks ectopic pregnancy in rudimentary horn of unicornuate uterus
Authors: Taware Smritika Mohanrao ,   Dr. D. Y. Patil College of Ayurved & Research Centre, Pimpri, Pune, Maharastra, India.
Published: July 2024

Female aged 26 years detected as single live pregnancy of 8 weeks in right tube. She was diagosed as right tubal ectopic and resent for confirmation of diagnosis to other Sonography centre as pregnancy grew upto 8 weeks with normal Foetal heart sound and if it was tubal would have ruptured earlier.
Surprises in surgery
Authors: Coco Ornella ,   Mediterranean Foundation GB Morgagni, Catania Italy.
Published: July 2024

Gallbladder agenesis represents the 0.01-0.04% of congenital anomalies (0.016 in autopsies). Patient become symptomatic in over the 20% of cases with false-positive imaging of cholelithiasis on ultrasound examination that leads to elective laparoscopic cholecystectomy.
Weeping blisters – Clinical presentation in an uncommon site
Authors: Rajagopalan Rajesh ,   Department of Dermatology, Venereology and Leprology, Government Erode Medical College and Hospital, India.
Published: July 2024

Lymphangioma Circumscriptum (LC), a microcystic hamartomatous lymphatic malformation is a rare benign condition characterized by the proliferation of dilated lymphatic vessels in the skin. Usually, it is congenital, but some may become clinically apparent later in life. They account for 4% of all vascular malformations. Usually seen in and around the shoulder and pelvic girdle. This case report discusses LC in a teenage girl with a 13-year-old history of painless, grouped fluid filled blisters on the right side of her chest, an uncommon site presenting after several years seeking intervention. The diagnosis was established through clinical examination and skin biopsy. With normal basic laboratory investigations, we initiated her with lesional cryotherapy as the primary efficacious intervention before planning for surgical excision. Cryotherapy is cheap, safe and easily available option.
Sobre a irremediável educação
Authors: De Lajonquière Leandro
Published: July 2024

Na esteira da tradição dos estudos psicanalíticos em educação, o autor elucida a insistência atual dos adultos, ora profissionais ora simples pais, de administrarem medicamentos às crianças quando estas não estariam de fato doentes. O raciocínio se estrutura em torno do contraponto entre duas experiências educativas ocorridas no século XIX, a do “garoto selvagem do Aveyron”, na França pós-revolucionária e a de Helen Keller, uma menina de sete anos cega e surda, nos Estados Unidos pós-guerra de secessão. Tal contraponto permite esclarecer o que está em pauta em toda educação que se preze; não uma educação eficaz, como costuma-se sonhar hoje em dia, mas subjetivante, isto é, passível de fazer emergir um sujeito de palavra.
Considerações sobre a prescrição de psicofármacos na primeira infância
Authors: Caponi Sandra Noemi
Published: July 2024

Tomando como ponto de partida os motores para modelar pessoas propostos por Ian Hacking, analiso os motores envolvidos na definição de depressão infantil. Considero as classificações psiquiátricas, as escalas psicométricas, a hipótese de desequilíbrio neuroquímico, em particular o déficit de serotonina, bem como a suposta eficácia e efeitos adversos de antidepressivos como os Inibidores Seletivos da Recaptação de Serotonina (ISRS) (Selective serotonin reuptake inhibitors SSRIs). Argumento que estas estratégias induzem uma transformação completa na forma como as crianças pensam sobre si mesmas, causando um efeito looping na forma como definem o seu self, suas emoções e seus comportamentos, ao mesmo tempo que silenciam as causas sociais dos sofrimentos.
O instituto ortofrenico do Dr Voisin e o cuidado das crianças "fora da linha normal" na França durant a Monarquia de Julho
Authors: Doron Claude-Olivier
Published: July 2024

Cet article étudie la manière dont s'est mise en place, sous la Monarchie de Juillet, une première expérimentation médico-pédagogique visant les enfants réputés "hors de la ligne ordinaire", et qui cherchait à la fois à adapter leur éducation à leurs particularités et à prévenir, ce faisant, la formation de criminels ou d'aliénés potentiels. Cette expérimentation fut mise en place par l'aliéniste Félix Voisin sous le nom d'institut orthophrénique. Nous montrons à la fois quels furent les principes, inspirés de la phrénologie, qui guidèrent cette expérimentation; quelles étaient les techniques médico-pédagogiques mises en oeuvres; et surtout en quoi ces travaux furent à l'origine d'une première thématisation des enfants dits "anormaux" et de qui deviendra les troubles des conduites de l'enfant difficile.
Sailfish generate foraging opportunities for seabirds in multi-species predator aggregations
Authors: Licht Max
Published: July 2024

While various marine predators form associations, the most commonly studied are those between subsurface predators and seabirds. However, the mechanisms underlying these associations remain poorly understood. Three hypotheses have been proposed to explain the prevalence of these associations: (1) subsurface predators herd prey to the surface and make prey accessible to birds, (2) subsurface predators damage prey close to the surface and thereby provide food scraps to birds, and (3) attacks of underwater predators lower the cohesion of prey groups and thereby their collective defences making the prey easier to be captured by birds. Utilising drone footage, we investigated the interaction between Indo-Pacific sailfish (Istiophorus platypterus) and terns (Onychoprion sp.) preying on schooling fish off the eastern coast of the Malaysian peninsula. Through spatio-temporal analysis of the hunting behaviour of the two predatory species and direct measures of prey cohesion we showed that terns attacked when school cohesion was low, and that this decrease in cohesion was frequently caused by sailfish attacks. Therefore, we propose that sailfish created a by-product benefit for the bird species, lending support for the hypothesis that lowering cohesion can facilitate associations between subsurface predators and seabirds.
La sindrome diencefalica: magro, lungo e sorridente
Authors: Mercuri Claudia , Piccolo Gianluca , Molteni Marta , Verrico Antonio , Piatelli Gianluca , Gaggero Gabriele , Ramaglia Antonia , Milanaccio Claudia
Published: July 2024

The Authors describe the case of a 10-month-old boy with growth arrest and nystagmus related to a pilomyxoid astrocytoma in a paramedian suprasellar location.
Endodontic management of maxillary first molar with two palatal canals - A case report
Authors: Kaur Kuljit
Published: July 2024

An awareness and understanding of unusual root canal morphology is essential as it deter-mines the successful outcome of endodontic treatment. Aberrations in root canal anatomy are commonly occurring phenomena. I report a clinical case of a maxillary left first molar with two palatal root canals. The morphology is atypical because it has two palatal canals with separated orifices, running parallelly but joined at the apex. Modifications to the usual access opening and examination of the pulpal floor for additional canals are essential in such atypical cases for the success of endodontic treatment
Choose your own adventure: understanding why students prefer certain types of assessment
Authors: Jopp Ryan , Pallant Jessica L. , Russell Heather
Published: July 2024

Empowering students with choice when it comes to assessment is shown to have a positive impact on student satisfaction and success, with previous studies finding a more flexible approach to assessment can promote engagement and performance. However, very little is known about why students choose certain types of assessment. Building on previous research, this study examines the many factors that influence student choice of assessment in an undergraduate business unit. Leveraging data collected over two semesters, our study found that student choice of assessment was primarily influenced by whether students thought the assessment was interesting, with 46% of overall respondents stating that this strongly influenced their decision. Requirements easy to understand (41%) and Better schedule fit (39%) rounded out the top three reasons for choosing an assessment. In contrast, only 22% of students were strongly influenced by the relevance of the assessment to their current career and 23% to the relevance to their future career. This raises some critical questions for educators, and the higher education sector more broadly, given the apparent focus on the creation of career-ready graduates. As such, our results can help higher education institutes determine the best possible mix of assessment tasks, by better understanding the wants and needs of students in order to provide a high quality learner experience.
Insights into professional learning for intensive Block Model: Lessons from a participatory evaluation for capacity building
Authors: Samarawickrema Gayani , Cleary Kaye , Gauci Sally
Published: July 2024

We share lessons gained through supporting an institution-wide curriculum innovation via a post-graduate professional learning program. At the inception of the innovation, an intensive Block Model (BM) was unfamiliar to both the institution and its professional learning facilitators. The Graduate Certificate of Tertiary Education was re-modelled with BM as the heart of professional learning so academics would encounter BM as students. The program modelled BM principles, reinforced by meta-conversations to provide students with a reflective, immersive experience. Through a participatory evaluation, professional learning facilitators’ individual reflections were distilled to generate collaborative insights into academics’ capacity building for BM. Their lessons inform strategies to cultivate institution-wide capability-building including their own professional growth. Lessons shape the study recommendations. Recommendations originate from effectively engaging time-poor, diverse cohorts. (1) In recognition of the ease with which students can fall behind, embedding strategies to manage their time and stress helps to maintain a realistic study pace. (2) Authentic assessments provide students with useful products for their teaching. (3) Peer-feedback and examples of students’ work exposes them to how their colleagues present their work and illustrates good BM practices. (4) Modelling BM principles must be reinforced by meta-conversations to provide students with a reflective, immersive experience of the pedagogical principals. We observed that well planned efficiencies for students often provide consequent efficiencies for staff. These insights are captured in a model for scalable institutional-based professional learning practice. Capability growth flourishes at the intersection of action, reflection and evaluation. Professional collegial conversations are the catalyst for developing context-relevant professional learning.
Editorial: Artificial Intelligence is Awesome, but Good Teaching Should Always Come First.
Authors: Crawford Joseph , Vallis Carmen , Yang Jianhua , Fitzgerald Rachel , O'Dea Christine , Cowling Michael
Published: July 2024

The explosion of generative artificial intelligence into the mainstream of society some twelve months ago has seriously challenged learning and teaching practice. Since then, AI companies such as OpenAI are constantly improving their language models and releasing new features to make them more capable and useful. So, given there have been many disruptors in the past and emerging disruptions in the present, what can we learn in this situation, where Generative AI stands poised to challenge the purpose and relevance of assessment models? From our examples, disruptive technologies only have a major impact when they positively transform practice and are informed by pedagogic models and learning theory. GenAI as a disruptor is only likely to have this positive impact when it informs quality learning and teaching practice. We should be focused on the opportunities that GenAI now presents to higher education. It is argued here and elsewhere that the relative weakness of GenAI is that it creates poor quality output, delivering uninformed, incorrect, biased and bland responses. In itself, this offers opportunities for ‘teachable moments’ (Newell et al, 2023) and gives us room to support students with their capabilities in an AI informed world. Historically, these opportunities enable higher education to grow and progress. What we have learned so far would appears to be that for research to contribute to the literature, they needed to be informed by it. Likewise, need to ensure that pedagogy, andragogy, and heutagogy come first. We also need to remember that people processes happen, artificial intelligence happens around them, and that artificial intelligence comes after human intelligence.
Exploring Cross-Cultural Teacher Perspectives on Student Engagement in Virtual Learning Environments During The COVID-19 Pandemic
Authors: Al-Naabi Ishaq , Cifuentes-Faura Javier , To Louert , Odu Obor Deborah
Published: July 2024

The rapid transition to emergency remote teaching during the COVID-19 pandemic posed challenges for teachers, students, and higher education institutions, impacting students' learning and engagement in the learning process. Based on Self-Determination Theory, this paper employs a collective case study research methodology to examine teachers' strategies for supporting students' learning and engagement in virtual learning environments during emergency remote teaching in the pandemic era, with the goal of offering guidelines to assist teachers in fostering student learning and engagement in these virtual settings. The inductive thematic analysis of eight semi-structured interviews with teachers from Spain, Oman, Nigeria and Cambodia revealed some challenges faced by teachers in engaging their students in virtual environments and some teaching and support strategies that teachers adopted to enhance students’ engagement in virtual classrooms. The study synthesised a set of strategies for teachers in higher education to support students’ engagement and learning in online environments. Teachers’ autonomy, structure and involvement support strategies had a behavioural, emotional, cognitive, and agentic engagement on students’ learning process. The paper discussed limitations and future research endeavours in online teaching and learning and students’ engagement.
DEVELOPMENT OF UNDERWATER ROV (REMOTELY OPERATED VEHICLE) SYSTEMS AND MECHANISM
Authors: Tianur Tianur , Suswanto Bagas Prayoga Suswanto , Hendriko Hendriko , Rahmawaty Made
Published: July 2024

Remotely Operated Vehicle or abbreviated as ROV is an underwater explorer robot where the robot is controlled remotely by an operator using a remote control system.  Water ecosystems certainly have various kinds of hazards that cause harm to divers, such as wild animals, waters contaminated with toxic waste, limited oxygen, and many other risks. Based on these problems, research was carried out, namely the Design and Development of Underwater ROV (Remotely Operated Vehicle) Mechanisms and Systems where the tool built is an underwater robot that uses 8 DC motors to be able to perform 12 types of movements under water. The final result obtained by the ROV is that it is able to perform all of these movements except for the right somersault and left somersault. The speed and direction of rotation of the motor is controlled by a device, namely the BTS7960 motor driver. This robot aims to measure pressure and temperature and capture images underwater to a depth of 4m. Pressure and temperature measurements were made using a BMP180 pressure sensor and a DS18B20 temperature sensor where the measurement results will be displayed on a 16x2 LCD. Based on the test results, the pressure sensor has an error rate of 3.6% and a temperature sensor of 0.34%. Images and videos are captured by a device, namely a CCTV camera where the device is connected to a laptop device as a monitor. The test produced results in the form of images and videos that were quite clear.
PERFORMANCE ANALYSIS OF BIO-BRIQUETTING MACHINE WITH SCREW EXTRUDER TYPE BASED ON QUALITY OF BIO-BRIQUETTES
Authors: Puspa Dilia , Yerizam Muhammad , Zamheri Ahmad , Wardhana Agum Try
Published: July 2024

he purpose of this study is to design a bio-briquetting machine with screw extruder type to convert biomass into higher density based on quality standard of charcoal briquette SNI 01-6235-2000 and SNI 19-4791-1998. The machine had production capacity of 30 kg/hour, equipped with the motor of 1 HP which transmitted to the machine through pulley and belt, and also has cutter which rotates continuously. The raw materials of the bio-briquette were using bagasse and coconut shells. There are 6 parameters that are used to test the quality of bio-briquette such as calorific value, inherent moisture, ash content, dependability pressure, volatile matter content, and fixed carbon content. The bio-briquette was made from the raw materials using amylum adhesives with ratio 30:70:30. With feed as 5 kg, the machine produced 50 pieces of bio-briquette. The machine could produce good bio-briquettes such as calorific value of 6,511.3 cal/g, inherent moisture of 1.97%, ash content of 5.88%, volatile matter of 27.71%, fixed carbon content of 64.99% and compressive strength was 16.98 kg/cm2.
DESIGN OF BAJAKAH ROOT CHOPPING MACHINE INTO TEA POWDER
Authors: Abdi Muhammad Zainul , Saparin Saparin , Setiawan Yudi , Wijianti Eka Sari
Published: July 2024

Tea (camellia sinensis) is one type of plant that is often found as a processed beverage. There are several raw materials for making tea that are often used including using leaves, shoots, flowers, and roots. One of the teas made from roots is bajakah root tea where the manufacturing process is still done in the traditional way, namely chopped using a machete. Therefore, in this study, a bajakah root chopping machine was designed with the aim of helping the bajakah root production process into tea powder. This bajakah root chopping machine into tea powder is designed using the France method with the demands of a sturdy frame and easy to move. This bajakah root chopping machine has dimensions of 340 mm long, 240 mm wide and 600 mm high. The engine is driven by an electric motor with a power of 0.5 Hp, a rotation of 1400 rpm using a pulley drive system with a ratio of 1: 1.5 and the rotation of the chopper shaft is 933 rpm. From the test results taken from a sample of 500 grams of raw material with 3 times testing, the resulting average is perfectly chopped as much as 389 grams, not perfectly chopped as much as 54 grams, with a time of 167.3 seconds. The production capacity of the machine is 8.37 kg/hour.  The machine that has been made is able to chop bajakah roots with a machine production efficiency of 77.8%.
ANALISIS STRATEGI PENGEMBANGAN PEMBANGKIT LISTRIK WILAYAH KALIMANTAN BARAT GUNA MENDUKUNG PERTAHANAN NEGARA REPUBLIK INDONESIA
Authors: Nugroho Fajar Bayu , Murtiana Sri , Pahlevi Rinaldi , B Z Cahya Maharani
Published: July 2024

Kalimantan Barat termasuk salah satu propinsi di Indonesia yang berbatasan langsung dengan negara asing, yaitu dengan Serawak, Malaysia Timur. Dengan posisi ini, Kalimantan Barat kini merupakan satu-satunya propinsi di Indonesia yang secara resmi telah mempunyai akses jalan darat untuk masuk dan keluar dari negara asing. Import listrik dari Malaysia masih berlangsung untuk mencukupi kebutuhan listrik di Kalimantan Barat yang rawan akan ancaman keamanan nasional. Penelitian ini bertujuan untuk menganalisa stategi pengembangan pembangkit di wilayah Kalimantan Barat (Kalbar) yang berada dalam satu pulau berbatasan langsung dengan Malaysia, guna mendukung kemandirian energi dan pertahanan negara. Data penelitian diambil dari jurnal, buku-buku, penelitian terdahulu, artikel ilmiah, literatur dan berita dari situs-situs resmi. Perlu adanya strategi pengembangan pembangkit listrik di wilayah Kalimantan Barat guna mendukung pertahanan negara. Koordinasi terkait pengembangan pembangkit akan menjadi dukungan kemampuan serta kekuatan sektor pertahanan terhadap keamanan nasional, baik ketika terjadi ancaman ataupun negara dalam keadaan damai.
ANALISIS KARAKTERISTIK MEMBRAN PENGOLAHAN AIR DARI BAHAN POLYETHERSULFONE DENGAN PENAMBAHAN PERAK NITRAT
Authors: Mataram A , Nasution J.D , Bizzy I , Mohruni A.S , Rizal S , Pataras M , Kurnia A.Y , Kurnia R.D , Jambak M.I , Bactiar M , Sutanto H
Published: July 2024

Pada penelitian ini, membran dengan polimer berbahan Polyethersulfone (PES), zat penguat Perak Nitrat (AgNO3), serta tambahan zat pelarut N,N-Dimethylformamide (DMF). Perbandingan di masing-masing sampel adalah 1,5wt%, 2wt%, dan 2,5wt%. Larutan tersebut dihubungkan dengan listrik DC bertegangan sebesar 15000V selama 30 detik. Pada pengujian tarik menunjukkan bahwa seiring dengan bertambahnya konsentrasi campuran, kekuatan tarik membran atau kemampuan membran untuk menahan beban cenderung menurun, di mana membran PES@AgNO3 dengan konsentrasi 1,5wt% memiliki nilai kekuatan tarik rata-rata tertinggi yaitu 5,74206 MPa, kemudian nilai kekuatan tarik rata-rata terendah terdapat pada membran PES@AgNO3 dengan konsentrasi 2,5wt% yaitu 3,07480 MPa, Penurunan nilai kekuatan tarik disebabkan oleh penyebaran pori yang tidak merata dan terdapat aglomerasi yang terbentuk sehingga saat dilakukan pengujian tarik membran menjadi lebih rapuh dan mudah patah. Pada pengujian CWP, terjadi penurunan signifikan hasil fluks pada variasi konsentrasi membran, di mana saat konsentrasi campuran membran PES@AgNO3 1,5wt% didapatkan nilai fluks tertinggi mencapai 3,88797 L.m-2 .h-1 .bar-1 , berbanding terbalik dengan semakin bertambahnya konsentrasi campuran membran yakni 2,5wt% didapatkan nilai fluks terendah yaitu 1,01345 L.m-2 .h-1 .bar-1. Penurunan fluks pada variasi konsentrasi membran disebabkan oleh peningkatan jumlah dan ukuran partikel perak (Ag) yang terbentuk pada campuran membran yang mengakibatkan terjadinya penggumpalan (aglomerasi) di permukaan membran, sehingga terjadi penurunan porositas dan ukuran pori yang terbentuk, serta memicu pemblokiran pori yang berpengaruh terhadap nilai fluks yang dihasilkan. Pada pengamatan SEM terdapat perbedaan, di mana membran memiliki struktur seperti jari dan spons dengan rongga serta terdapat aglomerasi yang berbeda-beda pada permukaan di masing-masing konsentrasi.
An Efficient Algorithm for Stock Market Prediction Using Attention Mechanism
Authors: Zena Zena ,   Department of Computer, College of Science for Women, University of Babylon, Babylon, Iraq,   Department of Computer, College of Science for Women, University of Babylon, Babylon, Iraq, Al-Sultan Ali Yakoob Al
Published: July 2024

Forecasting the stock market is a significant challenge in the financial industry due to its time series' complicated, noisy, chaotic, dynamic, volatile, and non-parametric nature. Nevertheless, due to computer advancements, an intelligent model can assist investors and expert analysts mitigate the risk associated with their investments. In recent years, substantial research has been conducted on deep learning models. Many studies have investigated using these techniques to anticipate stock values by analyzing historical data and technical indications. However, since the goal is to create predictions for the financial market, validating the model using profitability indicators and model performance is crucial. This article incorporates the attention mechanism model, incorporating attention from both feature and time perspectives. Utilize artificial neural networks. This approach addresses issues in time series prediction. The issue is the varying degrees of influence that many input features have on the target sequence. To tackle this, the method utilizes a feature attention mechanism to obtain the weights of distinct input features. An enhanced feature association relationship is achieved, whereas the data before and following the sequence exhibit a significant time correlation. An attention technique is employed to address this issue, allowing for the acquisition of weights at various time intervals to enhance robustness and temporal dependence. The system is applied to the three global SMs (TESLA, SP500, and NASDAQ) datasets, the best enhancement results are 99% in Acc, and the better results improvement to minimize error in MSE, MAPE, and RMSE are 0.004, 0.004 and 0.01 respectively.
What affects customers online shopping behavior, research that applied machine learning to Amazon product reviews
Authors: Qin Ziqin , Dong Kefan , Xie Bingzhao
Published: July 2024

The application of Natural Language Processing (NLP) in marketing has undergone significant evolution, with machine learning algorithms playing a crucial role in extracting valuable insights from complex textual data. This study focuses on comparing the performance of Support Vector Machine (SVM), Random Forest (RF), Nave Bayes (NB), and a specialized sentiment analysis model, Latent Dirichlet Allocation (LDA), in the context of online platform reviews. While previous research has delved into individual algorithms, there is a paucity of horizontal comparisons. Suitable algorithms for sentiment analysis on online platform reviews, specifically for Amazon, were filtered in this work. A dataset from Kaggle (https://www.kaggle.com/datasets/arhamrumi/amazon-product-reviews) comprising 500,000 reviews and 10 columns was utilized, overcoming time and resource constraints by opting for secondary data analysis. The primary objective was to assess the performance metrics of SVM, RF, NB, and LDA in classifying reviews into positive, neutral, and negative sentiments. Despite the massive size of the dataset posing challenges to the accuracy of the algorithms, nuanced results in precision, recall, and F-score were observed, not replicated in prior studies. Attempts to enhance accuracy by switching vectorizers yielded marginal improvements. Interestingly, LDA emerged as a transformative model, leveraging its ability to generate WordClouds for a systematic analysis of customers' emotional attachments. In addition to sentiment analysis, an investigation into the identification of factors influencing consumer purchasing behavior on Amazon was conducted. By training the LDA model on positive, neutral, and negative comments, distinctive features associated with each sentiment category were extracted. This analysis aims to unravel the underlying product features that contribute significantly to customer decision-making processes. In conclusion, this work provides a comprehensive evaluation of SVM, RF, NB, and LDA in the realm of sentiment analysis on Amazon product reviews. The findings shed light on the challenges posed by large datasets, the limitations of traditional vectorizers, and the unique capabilities of LDA in uncovering emotional nuances. Moreover, the investigation into consumer purchasing behavior offers valuable insights for marketers seeking to understand the factors influencing online shopping decisions.
Unveiling bias in artificial intelligence: Exploring causes and strategies for mitigation
Authors: Liu Yuhan
Published: July 2024

With the rapid advancement of Artificial Intelligence (AI), the emergence of various AI models such as Stable Diffusion, ChatGPT, and MidJourney has brought numerous benefits and opportunities. Through users' extensive utilization, they have discovered biases towards gender, race, and other factors in these AI systems. This paper focuses on bias in AI and aims to investigate its causes and propose strategies for mitigation. Through a comprehensive literature review, the paper has explored the phenomenon of bias in AI-generated content. Furthermore, we examine the reasons behind bias and solutions from social and intelligence science perspectives. From a social science perspective, we examine the effects of gender bias in AI and highlight the importance of incorporating diversity and gender theory in machine learning. From an intelligence science standpoint, we explore factors like biased datasets, algorithmic fairness, and the role of machine learning randomness in group fairness. Additionally, we discuss the research methodology employed, including the literature search strategy and quantity assessment. The results and discussions confirm the existence of bias in current AI products, particularly in the underrepresentation of women in the AI development field. Finally, we present future perspectives on reducing bias in AI products, including the importance of fair datasets, improved training processes, and increased participation of female engineers and intelligence scientists in the AI field. By addressing bias in AI, the paper can strive for more equitable and responsible AI systems that benefit diverse users and promote social progress.
A random forest-based prediction of cardiovascular diseases
Authors: Tao Xuanyi
Published: July 2024

With the rapid development of human arithmetic and new algorithms the use of machine learning in the healthcare industry is growing rapidly. A cardiovascular dataset is used to explore the correlation between various data in the dataset and cardiovascular diseases. Five types of machine learning algorithms, namely logistic regression, Adaboost, decision tree classifier, random forest, and neural network, are used to predict cardiovascular diseases. After training, it can efficiently process input samples with high-dimensional features, and the Random Forest model, which integrates multiple trees through the Bagging idea of integrated learning, has the highest score; it evaluates the importance of each feature in the classification problem, and the performance of various algorithms is comprehensively compared using accuracy, precision, recall, and F1-score, which results in the Random Forest as the best model. The use of random forests not only accurately predicts cardiovascular disease, but may also prolong the lives of patients with cardiovascular disease if appropriate methods are used to treat them.
Research on the technical principles and applications of massage robots
Authors: Zhong Yi
Published: July 2024

Massage robots, as innovative therapeutic devices, have garnered significant attention and application due to technological advancements and society's increasing focus on wellness. However, amidst this progress, gaps persist in fully understanding their capabilities and optimizing their functionalities, highlighting the need for further research in this domain. This paper focuses on exploring the intricate technical principles, mechanical structure, sensing technology, and intelligent control systems of massage robots, with the aim of providing both theoretical foundations and practical insights for researchers and practitioners in this field. Specific inquiries encompass the elucidation of working principles, emulation of human massage techniques, mechanical design considerations, applications of sensor technology, and implementations of intelligent control systems within massage robots. Employing a blend of literature review, comparative analysis, and experimental validation, this paper aims to not only deepen the understanding of massage robot functionality but also contribute to the advancement and refinement of this technology, fostering innovation and growth within the industry.
An improved random forest model for music genre classification algorithm based on sparrow search algorithm
Authors: Wang Yaohua , Chen Haoqi
Published: July 2024

In this study, a random forest model optimised based on the sparrow search algorithm is explored for the music genre classification problem. By introducing the sparrow search algorithm, the model is able to search the feature space more comprehensively during the training process, which improves the learning ability of complex data relationships. After 15 iterations of optimisation, the fitness value of the model is improved from 0.2002 to 0.197, showing better convergence. The performance on the training set shows that the model predicts 2,543 music genres with an accuracy of 99.59% and only 10 incorrect predictions, which verifies its excellent performance on the training data. While on the test set, the model's prediction accuracy for 623 music genres is 79.78%, which is slightly lower than the result of the training set, but still reflects good generalisation ability. The results of this paper imply that the improved random forest model based on the sparrow search algorithm has potential application prospects in the music genre classification task. By optimising the parameter settings and feature selection, we not only improve the prediction accuracy and generalisation ability of the model, but also provide new ideas for solving other complex data classification problems. In the future, we can further expand the sample size, try different feature combinations, and conduct in-depth research by combining more domain knowledge in order to further improve the model performance and promote the development of related fields.
Evolution and advancements in deep learning models for Natural Language Processing
Authors: Chai Yingxuan , Jin Liangning , Feng Shujie , Xin Zhuo
Published: July 2024

This paper provides a comprehensive review of the evolution and advancements in deep learning models for Natural Language Processing (NLP). It explores the transition from statistical models to neural networks, highlighting the paradigm shift towards data-driven methodologies and the implications for NLP tasks. The emergence of neural network architectures, such as Recurrent Neural Networks (RNNs) and transformer-based models like BERT and GPT, has revolutionized language understanding and generation. Furthermore, the integration of deep learning in traditional NLP tasks, such as part-of-speech tagging and named entity recognition, has led to significant improvements in accuracy and efficiency. The paper also discusses the quantitative analysis of deep learning models, including performance metrics, computational efficiency, and mathematical modeling of language tasks. Case studies and applications, including sentiment analysis, machine translation, and automated content generation, exemplify the transformative impact of deep learning in NLP.
Text sentiment analysis and classification based on bidirectional long and short term memory networks
Authors: Mai Lijun , Li Dongyang , Zhao Zehua
Published: July 2024

In this study, we explored the method of text sentiment analysis and classification using bi-directional long and short-term memory networks, which brings new ideas to the field of sentiment classification. In the text pre-processing stage, we processed chat phrases, non-alphanumeric characters, deactivated words, numbers, special characters, blanks, and addresses to convert the raw text data into numerical data that can be processed by the model. Subsequently, word frequency statistics were conducted for texts of different mood types to analyse the frequency of occurrence of different words in texts of different mood types. By introducing a bi-directional long and short-term memory network model for training, we observed the accuracy and loss value performance on the training and validation sets. The accuracy of the training set increases from the initial 94.6% to 99.5%, and the accuracy of the validation set gradually grows to 99.4%; at the same time, the loss value of the training set decreases from the initial value of 0.2075 to 0.0194, and the loss value of the validation set gradually increases from the initial value of 0.0538 to 0.0372. This indicates that the bi-directional long short-term memory network achieves a significant and stable performance improvement. The confusion matrix results for the test set show that our model achieves more than 99% prediction accuracy on the test set. This implies that our proposed method can efficiently classify textual sentiment with high prediction accuracy. This research result helps to improve the effectiveness of the application of sentiment analysis techniques in the fields of social media opinion monitoring and product review analysis, and provides a useful reference for further deepening the research on text sentiment analysis. In conclusion, this study achieves the optimisation of the text sentiment classification task by means of a bidirectional long and short-term memory network model, and achieves satisfactory results. In the future, the model performance and generalisation ability can be further improved by combining more corpus and optimising the model structure, so as to be better applied in real-world scenarios.
An improved efficientnet B7 underwater marine organism image classification model based on the spatial group enhancement attention mechanisms
Authors: Wang Zhengjie
Published: July 2024

In this paper, we adopt the Efficientnet B7 model, which is an excellent performer in deep learning algorithms, and improve it by combining with the Spatial Group Enhancement attention mechanism to provide an innovative method for underwater biological image classification. After a reasonable division of the dataset, we introduced the improved Efficientnet B7 model for training. By observing the change of the loss curve on the training set, it can be seen that the loss value of the model gradually decreases from 0.6 to 0.05 and tends to be stable, which indicates that the classification prediction of the model is gradually close to the real result, and it can classify the underwater marine organisms images effectively. The accuracy on the training set gradually increases from the initial 80.12% to 98.31%, and the recall also grows from 75.82% to 98.93%, showing that the model predicts well. The performance on the test set shows an accuracy of 84.86%, a recall of 82.40%, and an F1 value of 80.2%, indicating that the model has good generalisation ability. This means that our proposed improved methods and ideas have achieved significant results in the task of underwater biological image classification, providing a useful reference for research in related fields. By comparing the experimental results, it can be seen that the optimisation of the Efficientnet B7 model by introducing the Spatial Group Enhancement attention mechanism achieves better performance in the underwater marine organisms image classification task, which lays the foundation for the future application of deep learning in underwater environments. This study is of positive significance for the promotion of underwater biology research, marine protection and other fields, and provides new ideas and methods for carrying out more in-depth related research.
A cross-sectional survey of current nutritional counseling practices and attitudes towards dairy among pediatric dentists.
Authors: Martinelli Alexa , Milano Michael , Riggs Bruce , Schoenbaum Todd
Published: July 2024

Aims: This study sought to explore current dietary recommendations by pediatric dentists and to examine their attitudes toward sugar-sweetened milk. Methods: A cross-sectional 13-item survey instrument was developed by an expert team and disseminated to 7,783 emails through Qualtrics from February 2-27, 2023. The question topics included 3 domains: nutritional guidelines, types of foods recommended, and the level of dairy counseling given to parents for their children. Descriptive statistics were used to evaluate responses. Results: Out of 304 responses, 96.3% of pediatric dentists consider flavored milk to be a sugar-sweetened beverage (SSB). Nearly 65% of pediatric dentists refer to a USDA-published resource for nutritional recommendations. Only 12% of pediatric dentists routinely offer obesity counseling. Conclusions: While pediatric dentists continue to provide nutritional counseling at a high capaci ty, they do not have a universal resource on which to base their recommendations. The majority of pediatric dentists also consider flavored milk to be a SSB, despite the current exclusion of flavored milk from SSB classification.
On the path to leadership: California's administrator induction programs
Authors: Wechsler Marjorie E. , Patrick Susan Kemper , Thompson Charlie , Levin Stephanie
Published: July 2024

This report explores the landscape of new administrator induction in California using statewide survey data and case studies of administrator induction programs. The California Commission on Teacher Credentialing (CTC), which accredits and oversees all administrator induction programs, conducts a program completer survey asking about administrators’ experiences with their induction programs and overall preparedness when they apply for their clear credential. This report analyzes 4 years of survey data from 6,812 administrators who completed induction between September 2017 and August 2021. It also draws on qualitative data from six case studies of administrator induction programs that were highly rated on the program completer surveys to highlight exemplary structures and practices. These case studies draw on interviews and focus groups with key program staff, coaches, and current and former program participants, as well as observations and documents. Together, these data sources informed an in-depth analysis of induction program design, implementation, and effects, along with implications for state policy.
Pengembangan Media Video Pembelajaran pada Mata Pelajaran Sejarah Kebudayaan Islam untuk Siswa Kelas VII MTSN
Authors: Fauzi Ahmad , Hidayati Abna , Syafril Syafril , Novrianti Novrianti
Published: July 2024

This research aims to produce learning video media for SKI Class VII MTsN subjects that are valid and practical. This type of research is development research, known as Research and Development (R&D), using the 4-D development model. The development procedure in this research consists of 4 stages, namely, (1) Define, (2) Design, (3) Develop, (4) Disseminate. The product validity test was carried out by 3 validators, namely 1 material validator and 2 media validators. Product trials were carried out on class VII students at MTsN 5 Tanah Datar with the aim of finding out the practicality of the product being developed. The results of material validation by material expert validators obtained an average of 4.94 in the "very valid" category. The results of the media validator assessment with validator I obtained an average of 4.93 in the "very valid" category and the validation results with validator II obtained an average of 4.93 in the "very valid" category. This learning multimedia is said to be practical based on the results of the practicality test with an average of 4.5. Based on the results of the validity test and practicality test, it can be concluded that learning multimedia in PAI subjects for class IV elementary school is suitable for use in learning
Hybrid Genetic-ADAM Optimizer to Improve Disassembly Line Balancing Problem
Authors: Siddig Nadir , Zhang Zeqiang , Mokhtar Abdallah , Abualnor Ahmed
Published: July 2024

Modern products are designed to meet the needs of customized and short-lived products, after a short period of time, these products become waste products, these waste products are accumulating at an exponential rate, resulting in environmental degradation as a result of pollution, in order to keep this environment clean, it is necessary to have disassembly lines for those waste products, so in this research a Hybrid Genetic ADAM optimizer Algorithm was proposed to facilitate the disassembly operations at work stations placed in the disassembly lines, the proposed algorithm used to solve multi-objective disassembly line balancing problem, and Pareto optimal solution was used to determine non inferior solutions from a population. The trend of results reveal 6.1 percent a reduction in disassembly workstations, reduction of at least 0.05 percent of the idle time, reducing to minimum run time 1.5 percent comparing proposed and other meta heuristics algorithms. Furthermore, the plans of disassembly data are created to be valuable regarding trace ability and disassembly improvement processes in the future.
Electrocardiograph Signals Diagnosis Using Adaptive Neuro-Fuzzy Inference System
Authors: Emam Ahmed Abdalla , Mahmoudy Meinas Ahmed
Published: July 2024

Electrocardiograph (ECG) is a bioelectrical signal that is obtained by non-invasive method to register the electrical activities of the heart. This paper provides an attempt to develop computerized system for ECG signal filtering and classification. The proposed system encompass: pre-processing of the signal, extraction of pattern features through Independent Component Analysis (ICA), power spectrum, and RR interval calculation. These processes provide an input feature vector to the Adaptive Neuro Fuzzy Inference System (ANFIS) that acts as a signal classifier. All of the classification process steps are implemented in MATLABenvironment. This paper also provides a Graphical User Interface (GUI) that makes classification process easier. Three cases of ECG waveforms that are selected from MIT-BIH database are considered for the system test; they are Normal (N), Ventricle Fibrillation (VF), and Ventricular tachycardia (VTachy). An accuracy of 96.66% has been achieved by the proposed system
Effect of the Main Operating Variables on Efficiency of Treating Industrial Flue Gases Using Potash and Yellow Phosphorus
Authors: Alzaky Montaser , Basheer Rawya Adam , Ibrahim Tomadir , Ahmed Samah SirElhkateem , Siddig Rawia , Asal Eltayeb , GariebAllah Mohamed Magzoub
Published: July 2024

Fossil energy is considered as an important source of energy used in different types of industries. However it emits a great amount of flue gases. Some components of flue gases such as SOx and NOx can cause negative environmental impacts. Acid rains, increasing greenhouse effects and causing humans health problem (for Respiratory system) are some of them. A bench scale experiments using: 2900 ppm SOx, 550 ppmNOx , 1L deionize water, 0.5 g P4, and 10 g MOP were conducted in order to investigate the effects of operating conditions in removing process. These conditions are: emulsion temperature, yellow phosphorus weight, and the emulsion stirring intensity. Optimum conditions for removing process are (55oc, 1743 rpm, using 0.5 g P4). It was also observed that emulsion’s pH and oxygen concentration are declined with reaction time, but they have a positive correlations with removing efficiency, whereas efficiency increasing when rising emulsions temperature and stirring intensity. The obtained results identified the optimum conditions to get the highest efficiency for removing SOx and NOx from flue gases to eliminate their environmental impact