bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation strategies of emotional hearing and the level of language development in preschoolers with hearing disabilities
Authors: Ciubotaru Natalia
Published: April 2024

Emotions and feelings are manifested in language through suprasegmental elements: timbre, rhythm, tempo, and intonation of the voice in the form of pauses, raising and lowering of the voice. Perceiving a person’s emotional information by voice under normal conditions is possible due to emotional hearing. To date, this important aspect in the assessment of emotional hearing and impressive and expressive language of hearing-impaired children remains understudied. Knowing the structural content and determining effective strategies for evaluating emotional hearing and impressive and expressive language will later serve as scientific-methodological support in the sense of establishing a comprehensive approach to the development of emotional hearing and verbal language (the lexical-semantic side). The article summarizes the results of the experimental research carried out in 2022–2023 regarding the evaluation of emotional hearing and the level of language development in preschoolers with hearing disabilities and presents significant data in comparison with the control group (preschoolers with typical development).
Model for the development of linguistic intelligence of small schoolchildren in the context of studying english
Authors: Botnari Valentina , Rusu Cătălina
Published: April 2024

The article includes interpretations behind the enhancement of the Pedagogical Model for the development of linguistic intelligence in elementary school students. The beginning of the text reflects arguments with reference to the actuality and necessity of creating such a pedagogical model. An analysis of existing studies is provided, the results of which were used in the development of the given model. Then follows the description of the structural elements of the model: level 1 – Intelligence as a function of the mind; level 2 – of the Model-Topic of linguistic intelligence; level 3 – Marking the development of linguistic intelligence with linguistic dimensions: semantics, phonology, syntax, praxis; level 4 – Argumentological Principle and DIL Technology. This structural element of the Model has been given priority in the description, because it is one it is one that has particular originality and significance. The conditions derived from certain principles are presented, the compliance of which might increase the effectiveness of the formation of linguistic intelligence in young students.
Stability Indicating Assay of Empaglifozin and Metformin
Authors: Sahu Lalit K , Si Sudam , Patro Saroj Kumar
Published: April 2024

The newly developed stability‐indicating RP-HPLC method is simple, robust and validated on the basis of ICH guidelines for the simultaneous determination of Empaglifozin and Metformin hydrochloride in the tablet. Retention times under the optimized condition were 2.104 and 4.0822min for Metformin and Empaglifozin respectively. This research article indicates best separation of Empaglifozin and Metformin from their degradation products. Separation was achieved on a Sunniest ECO C18, 250mm x 4.6mm, 5µm analytical column at wavelength of 225nm, using a mobile buffer (pH-1.8): acetonitrile (50:50) in an isocratic elution mode at a flow rate of 1.2ml/min, Injection volume: 10µl and run time 6mins. The %RSDs for the precision studies was less than 1.5% for both drugs. The %RSD was less than 1.5% in all the parameters of robustness. The forced degradation studies were carried by using 0.1N HCl, 0.1 N NaOH and % 3 H2O2 and there is only % 7.2 degradation in an acidic medium for emplaglifozin and 11.63% for Meformin where as there was nothing degradation in basic and oxidative method. The analyte peaks were clearly separated from the degradant peaks in forced degradation studies. So this method is a fast, sensitive, robust and efficient high performance liquid chromatographic method for the concurrent determination of Metformin and Empaglifozin in their combination, and thus this method can be effectively employed for routine quality control works.
Formulation and Antimicrobial studies of Microencapsulated Musanga cecropioides Stem Bark Extract
Authors: Ovenseri Airemwen Collins , Sebastine Nze , Halilu Emmanuel Mshelia
Published: April 2024

Microencapsulation is the process of coating small droplets of liquid or solid materials with a continuous film of polymeric substances. The aim of the research was to formulate microparticles of Musanga cecropioides stem bark extract. The dried, powdered stem bark of Musanga cecropioides was extracted by maceration. The aqueous extract was microencapsulated using polymers such as alginate, pectin and hydroxylpropyl methylcellulose acetate succinate (HPMCAS) by counter-ion coacervation method using an electrostatic droplet generator. The formulated microcapsules were evaluated for flow properties, particle size, drug content, antimicrobial activity and in vitro release kinetics in simulated gastric and intestinal fluids. The microparticles were subjected to stability tests under different temperature (45°C, 25°C and 8°C) and relative humidity (100% RH, 78% RH and 0% RH) conditions for a period of three (3) months. The mean particle size of the microcapsules decreased with increasing applied voltage of the electrostatic generator. Particle size range for the batches of formulated microparticles was 68.20-223.84µm. The percentage entrapment efficiency of the batches of microparticles ranged from 80.26-86.58%. The microparticles lost a significant percent of its drug content after storage at different temperature and relative humidity conditions. The formulated microparticles and crude extract possess significant antibacterial action against both gram positive and negative bacteria as well as antifungal effect against C. albicans. Microparticles of M. cecropioides extract were formulated by counter-ion coacervation method using an electrostatic droplet generator which conferred advantages of sustained drug release, reduced frequency of dosing, masked unpalatable taste, improved stability and enhanced patient acceptance on the crude extract.
Comparative Study of Herbal drugs and Synthetic drugs used for treatment of Intestinal Worm Infestation
Authors: Pawar Kumudini R , Kale Priyanka G , Nalawade Madhuri S , Patil Tejashree , Patil Tejashree , Gaikwad Shruti
Published: April 2024

The purpose of this study was to compare the herbal drugs which are used traditionally with commonly used synthetic drugs for the treatment of intestinal worm infestation. The experiment was conducted using adult Indian earthworms (Pheretima Posthuma) owing to their anatomical and physiological similarity to human intestinal parasites, making it convenient for the evaluation of anthelmintic compounds in a laboratory setting. As a control sample and preparation of samples normal saline solution was used. Various concentrations of both herbal and synthetic drugs were tested and result was obtained in the terms of time of paralysis and time of death in minutes. All the drugs show different time of paralysis and death of worms. In this study we took herbal powders without any extraction process or chemical process done on it.
In-vitro Anti-inflammatory Activity for Combination of Ethanol Extract from Sappan Wood (Caesalpinia sappan L.) and Red Ginger Rhizome (Zingiber officinale Roxb.)
Authors: Tukiran Tukiran , Setiawan Ahmad Rudi , Sutoyo Suyatno , Sabila Fauzia Indah
Published: April 2024

Sappan wood and red ginger rhizome have been utilized as herbal medicine due to their anti-inflammatory, anti-oxidant, anti-microbial, analgesic, and immunomodulatory activities. Previous study revealed that sappan wood and red ginger rhizome showed good anti-inflammatory activity. However, the studies are focused on single extract. There are no research about combining the sappan wood extract and red ginger rhizome extract for antiinflammatory agent. Therefore, this study was conducted to determine the anti-inflammatory activity of the combination of sappan wood and red ginger rhizome ethanol extracts for antiinflammatory properties using red blood cell (RBC) membrane stabilization test. The tested samples consisted of sappan wood ethanol extract, red ginger rhizome ethanol extract, combination formulas (F1, F2, and F3), and positive control (diclofenac sodium). F1 comprises a 1:1 mixture of ethanol extract of sappan wood and red ginger rhizome, while F2 and F3 are composed of 2:1 and 1:2, respectively. The IC50 values for sappan wood ethanol extract, red ginger rhizome ethanol extract, and formulas F1, F2, and F3 were 47.63, 181.26, 101.93, 94.98, and 104.98 ppm, respectively. Statistical analysis using the one-way Anova followed by the Games-Howell post-hoc test revealed a statistically significant difference (p < 0.05) in the IC50 of each sample. For single extracts, sappan wood is better as an anti-inflammatory. Meanwhile, in the formulations, Formula F2 is superior as an anti-inflammatory agent
Design of Colon Specific Delivery of Sulfasalazine Loaded Nanoparticles for Inflammatory Bowel Syndrome: Application of Experimental Design
Authors: R Sivakumar , P Shafin
Published: April 2024

Sulfasalazine is the first line treatment for Inflammatory Bowel syndrome. Conventional formulation have poor targeting properties for colon site.Therefore designof colon specific nanoparticles (NP) is an ideal for better therapeutic outcome. The study aims to formulate and evaluate polymeric Nanoparticles loaded with Sulfasalazine to target the colon. The nanoparticles were prepared by Nano-precipitation method using pH dependent polymer and the prepared nanoparticles were evaluated for FTIR, morphology, pH, Viscosity, Entrapment efficiency, particle size and zeta potential etc. The nanoparticles were used to formulate core tablet and core tablets were coated with Eudragit S100. The tablets were evaluated for hardness, friability, thickness, weight variation and In vitro release study. Totally six formulations were developed for preliminary trial with varying concentration of polymers. From the preliminary trial, code F2batch was found to be best formulation. The F2batch was fitted to central composite design with two independent factors i.e. Concentration of Eudragit S100 (X1) and polyvinyl alcohol (X2) with four dependent variables i.e. Entrapment efficiency -Y1; Hardness- Y2; Friability-Y3 and In-vitro drug release- Y4. The formulation (code R6) was found to be the optimizedformulation. The optimized colon targeted tablets provide satisfactory release and might be suitable for colon targeting of sulfasalazine with better therapeutic effect.
Reinforcement Learning with Human Feedback: A CartPole Case Study
Authors: Priya N. Parkhi
Published: April 2024

This research delves into the integration of human feedback within reinforcement learning (RL) algorithms, with a specific focus on the CartPole environment as a testbed. We present RLHFAgent, a revolutionary RL agent devised to capitalize on human guidance during training for the purpose of expediting the learning process. Through the acquisition of feedback from a human operator, RLHFAgent adapts its policy in a more efficient manner, resulting in enhanced performance when it comes to balancing the pole. Our approach involves the training of a neural network model that approximates the policy function, mapping observations to actions, and subsequently updating this model based on human feedback. By means of a series of experiments, we showcase the efficacy of RLHFAgent in learning the art of balancing the pole, as demonstrated by the consistent rise in episodic rewards and the decrease in episodic loss over the course of training episodes. These findings indicate that the incorporation of human intuition into RL algorithms can augment their ability to adapt and expedite the learning process in intricate environments. In essence, this study contributes to the ongoing endeavours aimed at bridging the gap between RL algorithms and human expertise, thereby paving the way for more efficient and effective learning strategies in both simulated and real-world scenarios.
Stock Price Prediction and Stock Behaviour using GA and LR with NIFTY50 index Based on Daily Charts
Authors: Vaishali R Surjuse
Published: April 2024

The price of a company's stock, which can increase in lockstep with the price of a single share, is the one of the indicator to measure its performance. Clients or stockholding companies find it challenging to make long-term projections regarding the value of specific stocks due to the unpredictable nature of stock prices. Consequently, there is no business-related subject more talked about than stock market predictions. It is crucial to resolve this issue in a way that benefits buyers and investors because they frequently experience investment losses. Machine learning is useful in developing model for stock value predictions. We are utilizing Python and Linear Regression, one of the Machine Learning statistical techniques for predictive analysis, to create a stock price prediction website in order to address this issue. Our study primarily focuses on the NIFTY50 index's performance in distributed lag with the purpose of predicting stock prices in the Indian stock market. Several useful characteristics of the NIFTY50 lag index were extracted by means of a genetic algorithm. After that, we uncovered hidden correlations between the stock index and a given stock's trend by using the linear regression classifier. For the purpose of testing our approach, we used it to forecast the future of three distinct equities. In comparison to state-of-the-art forecasting methodologies, our experimental results demonstrated an accuracy of 82.55%. For the purpose of predicting daily changes in stock prices, the NIFTY50 stock index proved to be useful.
Early Detection of Parkinson’s Disease Using Machine Learning
Authors: Samita Ganveer, Himani Bire, Rutuja Deshmukh Shweta S. Salunkhe,
Published: April 2024

A hallmark of Parkinson's disease is the degeneration of dopaminergic neurons in the midbrain's substantia nigra pars compacta. However, machine learning is needed to design and implement early Parkinson's disease detection. by using machine learning methods such as CNN and SVM, which can reliably identify voice signals and spiral images to identify early indications of Parkinson's disease. Using machine learning on handwriting, tremor, and gait datasets, the method addresses the shortcomings of individual analyses for a more complete diagnosis solution by investigating relationships between symptoms. This increases accuracy. When voice and spiral drawing data were combined, Parkinson's disease diagnosis accuracy showed promise, with the machine learning model successfully differentiating between unaffected patients and those who were affected. This observation suggests a viable path for precise Parkinson's disease diagnosis: an integrated method that combines machine learning techniques with data from spiral drawings and voice.
U-Net Segmentation for CNN Classification to Improve the Accuracy of Automatic Prediction of Brain Tumors Using MRI Images
Authors: T. Sreenivasulu Reddy S. Sujatha,
Published: April 2024

Among various cancers, a brain tumor is a serious one that affects the patient's brain and causes sudden death. Several clinical diagnoses of brain tumors help medical experts provide the proper treatment at the right time; earlier identification and detection of brain tumors are much less. With the increase of cancer patients, managing and diagnosing efficiently with massive data collected is complex. Diagnosing patients with a brain tumor in the earlier stages is also tricky. Medical image processing methods and computer-aided diagnosis methods proposed in earlier works have not provided a high level of accuracy in classification, but the segmentation accuracy was good. Some recent research works have focused on implementing machine learning algorithms for predicting brain tumors for a massive amount of data, where the accuracy still needs to be improved. Machine learning and deep learning algorithms are used to predict brain tumors and their types earlier. With a large amount of data available, the ML and DL models are trained efficiently to make accurate prediction models. The DL models provide better accuracy in processing and predicting medical images than ML models. Some pre-trained deep learning algorithms like VGG16, VGG19, InceptionV3, and ImageNet provide improved accuracy and less computation power to achieve the required results. This paper proposes a CNN algorithm with a VGG16 pre-trained model for detecting brain tumors with MRI images. The input images are segmented using a U-Net model, improving the prediction process's accuracy, and are classified using a CNN model with VGG16, which helps in the efficient and faster prediction of brain tumors from the MRI images. The results show that the proposed model provides better accuracy than the existing ones.
Intelligent, optimal charging with MSSC and PCM protocols for healthy and long life on Li-ion batteries
Authors: Abdelamin Diani , Mohammed Jamil Ouazzani , Hassan Qjidaa Ismail Boumedra
Published: April 2024

MSCC is one of the most efficient charging methods with the appropriate advantages over the CC/CV method most commonly used in the Li-ion battery charger market. Our approach to this optimal charging is based on the weak points of CC/CV charging, as well as the battery's reaction after each CC step in testing the new optimal current. Given the needs of the industrial market, which demands fast charging and safety against overcharging and side effects, we decided to develop a fast, optimal charging method aimed first and foremost at guaranteeing a long battery life cycle. Our intelligent charging strategy uses a combination of two charging protocols, MSSC and PCM, each with functionality and a communication link via an STM32 MCU. The principle is to find the optimum charging current for the battery at each stage of charging, depending on its state of health, and then apply the MSSC with a modern, optimized method based on methodological calculations, always respecting the temperature conditions.

Finally, based on several experimental tests, we were able to prove that the optimal, intelligent method proposed in this article reduces charging time by 16.8% and temperature by 20%, compared with our own intelligent CC/CV+PCM method.
Explainable AI for Adversarial Machine Learning: Enhancing Transparency and Trust in Cyber Security
Authors: Araddhana Arvind Deshmukh,
Published: April 2024

Explainable artificial intelligence (XAI) is essential for improving machine learning models' interpretability, transparency, and reliability—especially in challenging and important fields like cybersecurity. These abstract addresses approaches, structures, and evaluation criteria for putting XAI techniques into practice and comparing them, as well as offering a thorough understanding of all the important components of XAI in the context of adversarial machine learning. Model-agnosticism, global/local explanation, adversarial assault resistance, interpretability, computing efficiency, and scalability are all covered in the discussion. Notably, the suggested SHIME approach shows excellent performance in a number of dimensions, making it a promising solution. The need of carefully weighing XAI solutions based on particular application requirements is emphasized in the abstract's conclusion, opening the door for future developments in the field to handle changing difficulties at the nexus of cybersecurity and artificial intelligence.
Web-Based Application for Plant Leaf Disease Detection
Authors: Payal Kadam,
Published: April 2024

Several diseases that affect plant leaves pose a severe hazard to agriculture. Our approach identifies both the disease that harmed the leaf and the area of harm. Crop diseases, especially those that predominantly affect the leaves, have an impact on both the quantity and quality of agricultural output. The objective of the article is to raise consciousness among farmers about the latest innovations that can prevent disease of plant leaves. The techniques of data mining and image processing with an accurate algorithm have been identified to detect leaf illnesses in the potato plant as potatoes are only an easily accessible vegetable. In this study, we present a web-based automated approach for identifying and classifying plant leaf diseases. The recommended approach involves receiving an input image, delivering it to the model using a Postman API, analyzing the image using a CNN model kept inside a Docker container, and producing a result to determine if the image is classified as healthy or unhealthy. The Plant Village dataset for plants like tomatoes and potatoes is used to validate this study. The accuracy of the proposed model is 98.44% on potato leaf samples.
Advancements in Fault Location in Electrical Power Systems with Distributed Generation using Deep Learning
Authors: R. M. Moharil, R. M. Ingle,Ashish Bhagat P. S. Patil,
Published: April 2024

Distributed generation (DG) has changed the energy landscape by increasing system reliability and decreasing environmental impacts through its incorporation into electrical power systems. The additional complexity brought forth by DG, however, also calls for the creation of more precise and effective fault finding techniques. This abstract explores current developments in fault localization strategies for DG-integrated electrical power systems using deep learning (DL) methods. Due to the non-linear, dynamic, and ever-changing nature of DG-rich networks, traditional fault finding algorithms generally struggle to correctly locate faults in these networks. Deep learning's potential as a solution may be seen in its capacity to recognise complex patterns and adjust to new information. Specifically, this study looks into how DL models like CNNs and RNNs can be used to improve the precision with which faults can be detected and localised in DG-integrated power systems. The creation of DL-based fault location algorithms that use high-resolution data from different sensors like smart metres and phasor measurement units (PMUs) is a major advancement. These algorithms draw on the temporal and spatial information available in power system data to pinpoint the exact location of malfunctions. Additionally, the study looks into the stability and transferability of DL models using various DG technologies and setups. Pre-trained models are transferred learning approaches to guarantee adaptability and reliability across a wide range of DG applications. The findings show that in DG-rich contexts, fault location algorithms based on DL perform much better than their conventional counterparts. These developments have the potential to strengthen electrical grids, reduce the frequency and duration of outages, and improve the reliability of today's power systems. The incorporation of deep learning into fault location algorithms is an important step towards building a more dependable and robust power system, especially as DG integration continues to grow
Enhancing MQTT Security in the Internet of Things with an Enhanced Symmetric Algorithm
Authors: Rupali Atul Mahajan
Published: April 2024

The Internet of Things (IoT), which connects billions of gadgets to expedite operations and enhance our lives, has completely changed the way we interact with our environment. With MQTT (Message Queuing Telemetry Transport) emerging as a popular communication protocol within the IoT ecosystem, the vast proliferation of networked devices has, however, presented serious security challenges. In order to strengthen MQTT security, this study suggests using an improved symmetric algorithm.Existing MQTT implementations frequently rely on simple security safeguards, making them susceptible to dangers like data manipulation, eavesdropping, and unauthorised access. Our research presents a novel symmetric algorithm designed to meet the particular needs of MQTT communication as a defence against these weaknesses. In order to protect the confidentiality and integrity of data transferred between IoT devices and brokers, this algorithm provides powerful encryption mechanisms. Additionally, it optimises resource usage to take into account the limitations of IoT devices, which frequently have constrained computational and memory resources.
Detecting Cyber-attacks in the Industrial Internet of Things using a Hybrid Deep Random Neural Network
Authors: Mrunal K. Pathak,
Published: April 2024

Critical infrastructure now faces greater vulnerabilities and a higher risk of cyberattacks as a result of the (IIoT) quick expansion. The security and dependability of industrial systems must be ensured by identifying and thwarting these threats. In this paper, we use a hybrid approach of deep learning and RNN called hybrid deep random neural network (HDRNN) to offer a novel method of identifying cyber-attacks in the IIoT.The proposed HDRNN model combines the benefits of random neural networks with deep learning to improve the detection of IIoT cyberattacks. The deep learning component makes use of deep neural networks' capacity to extract intricate features from unstructured data, while the random neural network component offers robustness and adaptability to manage changing attack patterns.Realistic threats and benchmark datasets such as UNSW-NB15and DS2OS are used in experimental evaluations. High accuracy, precision, and recall rates are attained by the model, which successfully detects a variety of assaults including infiltration, data manipulation, and denial of service.The suggested HDRNN model offers a promising approach for improving the security of IIoT systems by precisely identifying cyber-attacks in real-time. The model's hybrid nature enables enhanced detection capabilities, adaptability to changing attack patterns, and a reduction in false positives, enabling efficient threat mitigation and protecting crucial infrastructure in the IIoT context.
Autonomous Healthcare Systems: Deep Learning-Based IoT Solutions for Continuous Monitoring and Adaptive Treatment
Authors: G. B. Sambare,
Published: April 2024

Autonomous healthcare systems are a big change in the way medicine is done. They use deep learning algorithms and Internet of Things (IoT) devices to keep an eye on patients all the time and change their treatment as needed. This new way of doing things could change the way patients are cared for by giving real-time information and personalized treatments. With the help of deep learning, these systems can look at huge amounts of data produced by IoT sensors, like those in medical implants and smart tech, to spot small changes in health and spot problems before they get worse.Autonomous healthcare systems are based on their ability to constantly gather and analyze data from a variety of sources, such as vital signs, biological markers, and patient-reported complaints. Deep learning algorithms are very important to this process because they can find complicated patterns and connections in the data. These algorithms can get useful information from raw sensor data by using methods like CNN, MobileNet, and InceptionV3. This algorithm lets healthcare professionals move quickly and proactively.Also, independent healthcare systems are made to change based on the wants and interests of each patient by using personalized treatment plans. These systems can improve results and patient happiness by constantly checking how patients respond to actions and making changes to treatment plans on the fly. Adding IoT devices also makes it easier for patients and healthcare workers to talk to each other, allowing for distant discussions and quick solutions. The automated healthcare systems are a revolutionary way to provide medical care. They use deep learning-based IoT solutions to keep an eye on patients all the time and adjust their treatment as needed. These systems might be able to improve patient results, lower healthcare costs, and raise general quality of life by using the power of data-driven insights and individual actions
Development of a Novel Robotic Rehabilitation System Using Electrical Stimulation for Upper Limb Motor Recovery
Authors: Mandar Malawade
Published: April 2024

To improve the quality of life for people with neurological disorders or accidents, it is important to create therapy tools for upper limb movement recovery that work and are easy for everyone to use. This study suggests a new artificial therapy device that uses electrical stimulation to help people recover their upper limb movement skills. The technology uses both artificial help and electrical treatment at the same time to help the brain change and learn new skills. The robotic therapy system is made up of a suit that moves with the upper limbs and supports them mechanically. It has sensors that can figure out what the user wants and change the amount of help based on that. In addition, the system has electrical activation probes that are put to target specific muscles or groups of muscles that help move the upper limbs. A closed-loop control system makes the system work. The user's moves are constantly tracked, and the robotic help and electrical stimulation are changed in real time. This flexible method lets recovery plans be made just for each person, based on their wants and results. A pilot study with people who have trouble moving their upper limbs was used to test how well the planned method would work. When the method was used for six weeks, the data showed big gains in motor function, muscle strength, and range of motion. Participants were also very happy with how easy the method was to use and how well it helped them improve their movement skills.
Bio-Inspired Robotic Exoskeletons with Adaptive Electrical Control for Enhanced Upper Extremity Rehabilitation
Authors: Trupti Yadav
Published: April 2024

Robotic exoskeletons are a new and hopeful technology for rehabbing the upper limbs. They offer exact control and adjustable help to make traditional treatment more effective. This study suggests a new way to improve the recovery process that combines bio-inspired design with flexible electrical control. It is based on the way biological systems work. The suggested exoskeleton design is biomimetic, meaning that it looks and works like a human arm. This is done to make sure that the person can move naturally and comfortably. Lightweight, flexible materials make up the exoskeleton, which has sensors that track the user's movements and muscle activity. These monitors send input to the control system in real time, which lets it make changes that are more helpful for the user while they do different recovery routines. The control system uses an adaptable electrical treatment method that is based on how the human body controls its muscles. The control system constantly checks the user's muscle action and changes the amount of electrical stimulation to give the best support and help during recovery exercises. This flexible method makes sure that the exoskeleton's help is tailored to the person using it's wants and skills, which helps them recover more quickly. A bio-inspired robotic suit with adjustable electrical control is tested in a set of tests with healthy people and people who have had a stroke. The data show that the exoskeleton improves therapy for the upper limbs by giving individualized help and encouraging natural movement patterns. The adjustable electrical control makes it a lot easier for the user to do recovery exercises, which leads to better performance and a higher quality of life.
KATA LAIN DUNIA
Authors: Febriana Dwi
Published: April 2024

Tak ada yang istimewa jika kemudian “Kata Lain Dunia” ini lahir sebagai kesaksian cinta sekelompok anak muda terhadap ibu mereka. Buku yang merupakan kumpulan puisi ini merupakan sarana untuk mengungkapkan perasaan cinta anak kepada ibu, yang barangkali tidak secara langsung dapat mereka sampaikan kepada ibu.
Anatomic location of the first dorsal extensor compartment for surgical De-Quervain’s tenosynovitis release: A cadaveric study
Authors: Thandoni Aditya , Yetter William Nicholas , Regal Steven Michael
Published: April 2024

BACKGROUND

De-Quervain’s tenosynovitis is a disorder arising from the compression and irritation of the first dorsal extensor compartment of the wrist. Patients who fail conservative treatment modalities are candidates for surgical release. However, risks with surgery include damage to the superficial radial nerve and an incomplete release due to inadequate dissection. Currently, there is a paucity of literature demonstrating the exact anatomic location of the first dorsal extensor compartment in reference to surface anatomy. Thus, this cadaveric study was performed to determine the exact location of the first extensor compartment and to devise a reliable surgical incision to prevent complications.

AIM

To describe the location of the first dorsal compartment in relation to bony surface landmarks to create replicable surgical incisions.

METHODS

Six cadaveric forearms, including four left and two right forearm specimens were dissected. Dissections were performed by a single fellowship trained upper extremity orthopaedic surgeon. Distance of the first dorsal compartment from landmarks such as Lister’s tubercle, the wrist crease, and the radial styloid were calculated. Other variables studied included the presence of the superficial radial nerve overlying the first dorsal compartment, additional compartment sub-sheaths, number of abductor pollicis longus (APL) tendon slips, and the presence of a pseudo-retinaculum.

RESULTS

Distance from the radial most aspect of the wrist crease to the extensor retinaculum was 5.14 mm ± 0.80 mm. The distance from Lister’s tubercle to the distal aspect of the extensor retinaculum was 13.37 mm ± 2.94 mm. Lister’s tubercle to the start of the first dorsal compartment was 18.43 mm ± 2.01 mm. The radial styloid to the initial aspect of the extensor retinaculum measured 2.98 mm ± 0.99 mm. The retinaculum length longitudinally on average was 26.82 mm ± 3.34 mm. Four cadaveric forearms had separate extensor pollicis brevis compartments. The average number of APL tendon slips was three. A pseudo-retinaculum was present in four cadavers. Two cadavers had a superficial radial nerve that crossed over the first dorsal compartment and retinaculum proximally (7.03 mm and 13.36 mm).

CONCLUSION

An incision that measures 3 mm proximal from the radial styloid, 2 cm radial from Lister’s tubercle, and 5 mm proximal from the radial wrist crease will safely place surgeons at the first dorsal compartment.
Teaching Linear Algebra to Engineering Students in Two Different Educational Settings:
Authors: Rensaa Ragnhild Johanne
Published: April 2024

The present paper holds a twofold perspective on a new teaching format in a linear algebra course given to engineering students. The design resembles the flipped classroom, but with various adjustments. The first part highlights the basis on which the design was built, both research results and practical issues, but also the implementation of the teaching. It did not proceed quite as expected. The second part focuses on the outcome. It concentrates on students’ feedback on learning in the course. To inform this part, answers to a questionnaire is related to results from a previous linear algebra course that was taught traditionally, and to students’ interpretation of achievements. Drawing both on the analyses of data and research literature, an adjusted teaching format is suggested to better facilitate for learning. This contributes to knowledge on how research-based teaching arrangements may develop.
questão social e a juventude no processo de transição do Estado brasileiro (1926-1930)
Authors: Santos de Santana Márcio
Published: April 2024

O objetivo deste trabalho é refletir sobre a transição estrutural, ocorrida no Estado brasileiro, de uma matriz lockeana (liberal) para uma matriz hobbesiana (corporativista), avaliando a relevância da questão social para o desenvolvimento desse processo histórico, no período compreendido entre a Reforma Constitucional de 1926 e a dita Revolução de 1930. O Código de Menores será observado como uma das fraturas abertas na matriz lockeana de Estado. Em sua órbita, refletiremos sobre as respostas apresentadas pelas forças políticas em ação no período: de um lado, os empresários industriais organizados no CIESP/FIESP; de outro, os militantes em prol do comunismo organizados na Juventude Comunista.
best-seller para a Guerra Fria:
Authors: Mozer Josiane
Published: April 2024

Nos anos iniciais da Guerra Fria, o Conselho de Segurança Nacional dos Estados Unidos elaborou um programa editorial antimarxista e contrarrevolucionário contendo títulos de variadas áreas do conhecimento, incluindo romances. Este artigo tem por objetivo analisar o uso da forma romance por esse programa editorial, e como exemplo, a publicação, no Ocidente, do romance O Dr. Jivago, do escritor soviético Boris Pasternak. O romance foi publicado, distribuído, alçado à categoria de best-seller e largamente utilizado como arma política pela ação das duas principais agências estadunidenses responsáveis pela execução do programa, a Central Intelligence Agency - CIA e a United States Information Agency – USIA.
Etnomusicologia, diversidade e inovação:
Authors: Lobo de Arruda Campos Alzira , Figueira da Hora Juliana , Lopes de Sousa Rafael
Published: April 2024

Enfocam-se as desigualdades históricas brasileiras a partir da Etnomusicologia, que estuda as condições de produção de gêneros musicais periféricos aos padrões europeus da música erudita. A música é vista como exemplo da manifestação antitética de classes, espelhada nas categorias culturais do erudito e do popular. Os preconceitos relativos à ascendência étnica, combinados com ações discriminatórias, ilustram as interpretações tradicionais sobre a presença africana na música brasileira, em imagens pluralistas, emanadas, regra geral, da dialética entre escravidão e liberdade. Esse cenário é frequentado por descendentes de uma ordem escravocrata, apresentando imagens fragmentadas pelos impactos que a globalização impõe sobre identidades excluídas dos objetivos globais do capitalismo internacional.
Educação política e “literatura da práxis” em relatos de viagem à URSS nos primeiros anos da Guerra Fria:
Authors: Dal Bosco Almeida Diego Orgel
Published: April 2024

Este texto busca compreender os relatos de viagem à URSS (União das Repúblicas Socialistas Soviéticas) na primeira metade da década de 1950 como parte importante do conjunto de práticas educativas que se realizavam entre comunistas que atuaram na clandestinidade política. A partir do tema escolhido, é problematizada a relação entre educação, clandestinidade e cultura política, no caso dos relatos de militantes do PCB (Partido Comunista Brasileiro) que frequentavam escolas de formação política na URSS, além das noções associadas à ideia de “pacto autobiográfico” e de testemunho para o caso dos escritores de literatura que detinham papel específico na educação comunista. Verifica-se que no início da década de 1950, muitos escritores assumiram o papel de disseminadores dos valores da cultura política comunista, orientados por um novo estatuto da literatura que procurava se associar às discussões de questões políticas e sociais que se voltavam à práxis e à transformação social. Analisa-se, ao final, o relato As muralhas de Jericó, de Josué Guimarães, a partir dos pressupostos desse novo estatuto da literatura entre fins dos anos 1940 e meados dos 1950.
João Goulart e a grande imprensa nas eleições presidenciais (1955 e 1960):
Authors: Wermeier Krilow Letícia Sabina , Dos Passos Martins Luis Carlos
Published: April 2024

Neste artigo, procuramos analisar a forma como a grande imprensa carioca – jornais O Globo, Jornal do Brasil, Correio da Manhã e Última Hora – representou a imagem de João Goulart entre 1955 e 1960, período em que ele foi candidato por duas vezes ao cargo de Vice-Presidente da República e exerceu o primeiro destes mandatos. A figura de Goulart é uma das mais controvérsias da história do Brasil no Período Democrático (1945-1964). Estudar a forma como a grande imprensa tratou esta personagem histórica é de fundamental importância para compreendermos a visão que difundiram sobre este político controverso, mas também a sua própria percepção sobre o funcionamento da democracia no Brasil. Em especial, pelo fato de permitir a eleição de políticos contra os quais tinham sérias resistências ideológicas.
ANÁLISE DE UMA SEQUÊNCIA DIDÁTICA PARA A EDUCAÇÃO FINANCEIRA EXPLORANDO AMBIENTES DE APRENDIZAGEM À LUZ DA EDUCAÇÃO MATEMÁTICA CRÍTICA
Authors: Binotto Rosane Rossato , Barbieri Leandra
Published: April 2024

Este artigo tem por objetivo analisar uma sequência didática para o estudo da Educação Financeira sob a ótica da Educação Matemática Crítica investigando ambientes de aprendizagem, segundo Skovsmose. A escolha do tema justifica-se em função da observação com relação a indecisão dos estudantes referente às questões de consumo, planejamento e orçamento financeiros. Constitui-se em uma pesquisa qualitativa com estudo teórico propositivo de uma sequência didática que poderá ser aplicada no Novo Ensino Médio. Realiza-se uma análise das potencialidades das atividades propostas, do ponto de vista da Educação Matemática Crítica, por meio de categorias de análise, segundo Bardin, definidas à priori. Com o desenvolvimento dessa proposta de sequência didática, a expectativa é de que os estudantes investiguem, testem, simulem, resolvam os problemas propostos movendo-se por diferentes ambientes de aprendizagem, ampliando as possibilidades de uma formação completa no desenvolvimento dos conteúdos, além da atuação em processos de ação e reflexão, usando-se dos conhecimentos matemáticos estudados. Almeja-se que esta abordagem possa interferir de forma crítica e responsável em ações que sejam exigidas a tomada de decisão referente a questões financeiras do cotidiano dos estudantes e também de seus familiares.
O ENSINO DE CIÊNCIAS NA ESCOLA DO CAMPO: A PRÁTICA PEDAGÓGICA DO PROFESSOR DE CIÊNCIAS EM FORMAÇÃO CONTINUADA
Authors: SILVA DENILSON ELIAS LIMA , RÊGO JOÃO RICARDO SOUZA DO , JÚNIOR FELIPE MAGNO DA CRUZ
Published: April 2024

Este texto propõe algumas reflexões sobre práticas pedagógicas que podem ser desenvolvidas com os professores de ciências que consoante as investigadas em sala de aula por meio dos professores que lecionam em escola de rede pública no campo. Essas formações práticas pedagógicas foram previstas em três encontros que poderá servir de reflexões em ideias teóricas. Estrados práticas enfocam a relação entre experimentos investigativos e reflexão de sua própria prática, nessa acepção entre professores e seus alunos por meio de mediações pedagógicas e interações discursivas. Nesse sentido, algumas pesquisas em consoante com contribuições sobre a educação em ciências estão relacionadas com as ideias centrais da abordagem histórico-cultural propostas por Vygotsky e colaboradores com o objetivo de destacar a importância dos docentes de ciências na formação continuada de professores de ciências em escola de campo.
ANÁLISE DE DESENHOS PRODUZIDOS POR ALUNOS DO ENSINO FUNDAMENTAL EM UMA SEQUÊNCIA DE ATIVIDADES INVESTIGATIVAS
Authors: Morais Fernanda Costa de , Valle Mariana Guelero do , Lemos Renata Araujo
Published: April 2024

A presente pesquisa objetivou analisar desenhos produzidos por alunos do Ensino Fundamental em uma sequência de ensino investigativa, tendo foco nas finalidades dos desenhos em suas produções escritas e na identificação de elementos composicionais desses desenhos. A abordagem é qualitativa e caracteriza-se como documental. Foram analisados 116 desenhos produzidos em uma sequência didática cujo problema inicial era “De onde vem o mel?”. Para analisar as finalidades dos desenhos nas produções escritas elaboradas pelos alunos, foram utilizadas as categorias: ilustrativos, parcialmente ilustrativos, complementares e independentes. Em relação à finalidade, a maior parte dos desenhos foi inserida na categoria ilustrativos, ou seja, trazia informações bastante semelhantes às do texto escrito. Os desenhos também nos revelaram elementos importantes de suas composições, que nos levaram a elaborar categorias para agrupá-los, como a organização espacial e a representação humana. Essas categorias puderam ressaltar as individualidades dos alunos em suas formas de se expressar e organizar seus pensamentos no papel. Vale reiterar que as formas de registro do desenho e da escrita podem trabalhar em conjunto, sendo, muitas vezes, complementares e possibilitando uma construção mais completa de ideias. O desenho é uma forma de linguagem interessante no contexto do ensino, permitindo acessar elementos que podem ajudar a traduzir ideias que dificilmente seriam expressas em outro tipo de linguagem, além de ser uma opção simples e acessível. As atividades investigativas propiciam não somente o aperfeiçoamento de conteúdos conceituais, mas, também, dos procedimentais e atitudinais. Em nossa pesquisa isso foi evidenciado em diferentes momentos, como no engajamento ao responder e formular perguntas, na cooperação durante a elaboração e construção de armadilhas em uma das atividades, bem como na finalização, quando houve a sistematização do conhecimento.
A EDUCAÇÃO CTS NO ENSINO MÉDIO: REVISÃO DE PESQUISAS SOBRE FORMAÇÃO DE PROFESSORES DE CIÊNCIAS
Authors: Thomas Preussler Vanda , München Sinara
Published: April 2024

Este artigo teve como objetivo identificar pesquisas vinculadas a Educação, Ciência, Tecnologia e Sociedade (CTS) no Ensino Médio através de uma revisão bibliográfica em trabalhos da Biblioteca Digital de Teses e Dissertações (BDTD) do Instituto Brasileiro  de Informação em Ciência e Tecnologias (IBICT). A abordagem metodológica é qualitativa do tipo bibliográfica, seguindo pressupostos metodológicos para compor um estado do conhecimento. Os trabalhos foram analisados adotandoa metodologia da Análise Textual Discursiva, organizando os resultados em duas categorias emergentes: (1) Implicações da realidade escolar e da motivação dos alunos na formação e atuação do professor, com perspectiva CTS; (2) Percursos e resultados da formação e da prática de professores, com perspectiva CTS, no Ensino Médio. Os resultados encontrados evidenciam uma realidade escolar fragilizada, compreensões limitadas dos professores sobre CTS e apontam a formação docente como direção para o desenvolvimento de práticas pedagógicas articuladas à educação CTS. A falta de planejamento coletivo é um fator limitante assinalado pela pesquisa ao desenvolvimento de novas propostas de ensino e a mudança de postura dos professores em relação ao ensino interdisciplinar é um destaque da pesquisa, tendo em vista ser um fator relevante para alavancar a educação baseado na tríade CTS.
CONCEPÇÕES SOBRE SAÚDE APRESENTADAS POR ALUNOS EM UMA ESCOLA QUILOMBOLA: UM DIAGNÓSTICO DA LITERACIA EM SAÚDE E AS PROPOSTAS CURRICULARES EM CIÊNCIAS
Authors: Dos Santos Souza Cassiana , Barros da Silva Wellington
Published: April 2024

A infância é uma etapa da vida de muitas descobertas e aprendizados, é nesse momento que as crianças desenvolvem habilidades de comunicação e competências para discutir diferentes informações. Devido a essas características, que é possível motivá-las a cuidar da sua saúde e a interpretar informações. No entanto, existem fatores sociais, ambientais, econômicos e políticos, que colocam muitas crianças em situações de vulnerabilidade, como a cultura, aspectos étnicos-raciais, saneamento básico, que interferem nas suas concepções de saúde. Partindo desse pressuposto, o presente estudo teve como objetivo central analisar as concepções sobre saúde apresentadas por alunos do 5° ano em uma escola quilombola em Simão Dias/SE. O presente estudo foi exploratório, com análise qualitativa, do tipo estudo de caso. Como técnica de análise de dados foi utilizada a Análise de Conteúdo (AC) proposta por Bardin, ocorrendo aplicação de um questionário para reconhecer indícios de literacia. Participaram da pesquisa 24 alunos, com uma faixa etária entre 9 a 12 anos. Assim, através da pesquisa podemos concluir que em sua maioria as crianças possuem concepções sobre saúde consideradas reducionistas, mas apesar disso, compreendem a importância da saúde para seu bem-estar, e a importância da prevenção e avaliação da saúde para a qualidade de vida, elas mostram-se potenciais para tomar decisões sobre saúde de acordo com seus discursos e falas.