bluuebunny/crossref_metadata_2025 huggingface dataset:
Start to Move: Measuring the Feasibility of a Teacher-Led Digital Fundamental Movement Skills Assessment Tool
Authors: van Rossum Tom , Foweather Lawrence , Hayes Spencer , Morley David
Published: June 2024

Purpose: This study evaluated the feasibility of the “Start to Move” (S2M) digital assessment of children’s fundamental movement skills being implemented by primary school teachers within PE lessons. Methods: Nine primary school teachers in the United Kingdom trialed S2M weekly over a 6-week period. Posttrial surveys and interviews were used to ascertain responses. Results: Feasibility was measured using seven dimensions of Bowen et al. framework; acceptability, demand, implementation, practicality, adaptation, integration, and expansion. Acceptance and demand of S2M was high with participants feeling that its contents aligned to the PE curriculum. Participants were able to implement S2M with PE lessons without assistance and stated that they would continue to use it within their teaching. They felt S2M would enhance their teaching and would recommend it to other teachers. Discussion/Conclusion: S2M is feasible for primary teachers to implement within PE lessons and has the potential to heighten the use of assessment for learning within PE in primary schools.
New Electrochemical Systems for Sodium-Ion Batteries
Authors: Kulova T. L. , Gavrilin I. M. , Skundin A. M. , Kovtushenko E. V. , Kudryashova Yu. O.
Published: June 2024

Two new electrochemical systems have been developed for sodium-ion batteries with a positive electrode based on manganese-doped sodium iron phosphate (NaFe0.5Mn0.5PO4) and a negative electrode based on a CoGe2P0.1 nanostructure, as well as with a positive electrode based on iron-doped sodium vanadophosphate (Na3V1.9Fe0.1(PO4)3) and a negative electrode based on a CoGe2P0.1 nanostructure. The results of cycling of battery models showed that the energy density of the NaFe0.5Mn0.5PO4/CoGe2P0.1 and Na3V1.9Fe0.1(PO4)3/CoGe2P0.1 electrochemical systems is ~165 and 167 W h/kg, respectively.
Impact of COVID-19 pandemic on neurodevelopmental outcomes of premature infants: a retrospective national cohort study
Authors: Tseng Tzu-Cheng , Wang Teh-Ming , Hsu Ya-Chi , Hsu Chung-Ting , Lin Yi-Hsuan , Lin Ming-Chih
Published: June 2024

Objective

To compare the neurodevelopmental outcomes of preterm infants before and during the COVID-19 pandemic.

Design

Premature infants born in 2018 were assigned to the pre-pandemic group, while those born in 2019 were assigned to the during-pandemic group.

Setting

Nationwide cohort study.

Patients

Very low birthweight premature infants registered in the Taiwan Premature Infant Follow-up Network database.

Interventions

Anti-epidemic measures, including quarantine and isolation protocols, social distancing, the closure of public spaces and restrictions on travel and gatherings during COVID-19 pandemic.

Main outcome measures

Outcomes were measured by Bayley Scales of Infant and Toddler Development Third Edition at corrected ages of 6, 12 and 24 months old. Generalised estimating equation (GEE) was applied to incorporate all measurements into a single model.

Results

Among the 1939 premature infants who were enrolled, 985 developed before the pandemic, while 954 developed during the pandemic. Premature infants whose development occurred during the pandemic exhibited better cognitive composite at the corrected age of 6 months (beta=2.358; 95% CI, 1.07 to 3.65; p<0.001), and motor composite at corrected ages of 12 months (beta=1.680; 95% CI, 0.34 to 3.02; p=0.014). GEE analysis showed that infants who had grown during the pandemic achieved higher scores in cognitive composite (beta=1.416; 95% CI, 0.36 to 2.48; p=0.009).

Conclusion

Premature infants in Taiwan who developed during the pandemic showed better neurodevelopment compared with those born before the pandemic.
Wide-suction-range hysteretic water retention behaviour of compacted soils amended by decomposing hydrochar
Authors: Dong Huan , Leung Anthony Kwan , Chen Rui
Published: June 2024

Hydrochar, which is produced by hydrothermal carbonisation, is an alternative to biochar for the alleviation of plant water stress. However, hydrochar shows vulnerability to decomposition. This study measured the effects of grass-derived hydrochar on hysteretic wide-suction range water retention curves (WRCs) of compacted soils and investigated how hydrochar decomposition affected the WRCs and pore size distributions. Hydrochar addition increased the first air-entry value (AEV) at a relatively low suction range due to the reduction in the macro-pore peak diameter but reduced the second AEV at a higher suction range following the increase in pore volume at diameters larger than 0.3 µm. A strongly positive correlation existed between the AEVs and the peaks of the degree of hysteresis, which suggests that the peak hysteresis is related to the ink-bottle effect despite the modification of soil pore structure due to hydrochar addition. After 120 days of decomposition to an approximately stable state, the hydrochar did not affect all the WRCs at a wide suction range, regardless of the emergence of bacteria and fungi. The test results highlight the potential of hydrochar in promoting plant growth through improvements of the soil’s WRCs and the increase in abundance of functional bacteria and fungi.
Implications of vanishing complexity condition in $$f({\textbf{R}})$$ theory
Authors: Naseer Tayyab , Sharif M.
Published: June 2024

This paper introduces the concept of complexity for a static spherical spacetime and extends it to the modified $$f({\textbf{R}})$$

f
(
R
)

framework. The formulation of the corresponding field equations is then carried out to describe the anisotropic interior. The spherical mass function is defined in both geometric as well as matter terms. Utilizing the orthogonal splitting of the curvature tensor, specific scalars are developed, with one of them denoted as $${\mathcal {Y}}_{TF},$$

Y

TF

,

identified as the complexity factor for the considered fluid setup. In addressing the system of field equations admitting some extra degrees of freedom, the complexity-free condition is introduced. In conjunction with this condition, three other constraints are applied, leading to the development of different models. We also provide a graphical representation of the resulting solutions, using specific parametric values. From this analysis, we conclude that our all three models exhibit the properties required for the existence of physically viable and stable structures for certain values of the model parameter.
Advancements in Federated Learning: Models, Methods, and Privacy
Authors: Chen Huiming , Wang Huandong , Long Qingyue , Jin Depeng , Li Yong
Published: June 2024

Federated learning (FL) is a promising technique for resolving the rising privacy and security concerns. Its main ingredient is to cooperatively learn the model among the distributed clients without uploading any sensitive data. In this article, we conducted a thorough review of the related works, following the development context and deeply mining the key technologies behind FL from the perspectives of theory and application. Specifically, we first classify the existing works in FL architecture based on the network topology of FL systems with detailed analysis and summarization. Next, we abstract the current application problems, summarize the general techniques, and frame the application problems into the general paradigm of FL base models. Moreover, we provide our proposed solutions for model training via FL. We have summarized and analyzed the existing FedOpt algorithms, and deeply revealed the algorithmic development principles of many first-order algorithms in depth, proposing a more generalized algorithm design framework. With the instantiation of these frameworks, FedOpt algorithms can be simply developed. As privacy and security are the fundamental requirements in FL, we provide the existing attack scenarios and the defense methods. To the best of our knowledge, we are among the first tier to review the theoretical methodology and propose our strategies since there are very few works surveying the theoretical approaches. Our survey targets motivating the development of high-performance, privacy-preserving, and secure methods to integrate FL into real-world applications.
When Journalism Meets AI: Risk or Opportunity?
Authors: Cheng Sophia
Published: June 2024

In an era where technology, particularly machine-learning and AI, seems to have irretrievably changed the landscapes of almost every industry, it is crucial to consider how AI will play the role of journalism in society. However, there is currently a relative dearth of articles delving into the intersection of these two fields, particularly ones that approach it from both sociological and technological perspectives. This article navigates through the historical evolution of journalism, discussing the profound impact of the recent AI revolution on the field. Then, we investigate and experiment with some popular AI model customization technologies, such as embedding and Constitutional AI, to improve a generic LLM in a way that aligns with the foundational principles of journalism. Through these investigations, we aim to address issues of misinformation and distrust in journalism by using LLM technology.
PnA: Robust Aggregation Against Poisoning Attacks to Federated Learning for Edge Intelligence
Authors: Liu Jingkai , Lyu Xiaoting , Duan Li , He Yongzhong , Liu Jiqiang , Ma Hongliang , Wang Bin , Su Chunhua , Wang Wei
Published: June 2024

Federated learning (FL), which holds promise for use in edge intelligence applications for smart cities, enables smart devices collaborate in training a global model by exchanging local model updates instead of sharing local training data. However, the global model can be corrupted by malicious clients conducting poisoning attacks, resulting in the failure of converging the global model, incorrect predictions on the test set, or the backdoor embedded. Although some aggregation algorithms can enhance the robustness of FL against malicious clients, our work demonstrates that existing stealthy poisoning attacks can still bypass these defense methods. In this work, we propose a robust aggregation mechanism, called
Parts and All
(
PnA
), to protect the global model of FL by filtering out malicious local model updates throughout the detection of poisoning attacks at layers of local model updates. We conduct comprehensive experiments on three representative datasets. The experimental results demonstrate that our proposed
PnA
is more effective than existing robust aggregation algorithms against state-of-the-art poisoning attacks. Besides,
PnA
has a stable performance against poisoning attacks with different poisoning settings.
Physicochemical Evaluation of Coated Ginger during Long-Term Storage: Impact of Chitosan and Beeswax Bilayer Coatings at Different Temperatures
Authors: Hernani  , Winarti Christina , Hidayat Tatang , Bin Arif Abdullah , Yuliani Sri
Published: June 2024

Fresh ginger can spoil quickly owing to a variety of factors, including inappropriate postharvest handling, microbial and enzymatic activities, and chemical reactions during storage. This study evaluated the physicochemical properties of ginger coated with chitosan and beeswax during storage for 6 months at different temperatures (18°C and 25°C). Fresh ginger was treated with chitosan coating (1.5 and 3.5%), followed by beeswax coating (3 and 6%). The coated ginger was wrapped in a plastic net and stored at ambient (25°C) and low temperatures (18°C) for six months. The results confirmed that coating treatment slowed down the changes in physicochemical properties (moisture, phenolic content, and so on) of ginger during storage. Ginger stored at 25°C showed shorter shelf lives than those stored at 18°C. Coating ginger with 3% chitosan followed by 6% beeswax exhibited the best results in maintaining the moisture and phenolic content, reducing weight loss, and increasing total soluble solid (TSS) and cell compartment size for six months of storage. This study provides a promising approach to delaying the spoilage of fresh ginger by applying coating treatments useful for developing handling protocols for fresh ginger during storage and distribution.
Theoretical Analysis on Thermodynamic and Economic Performance Improvement in a Supercritical CO2 Cycle by Integrating with Two Novel Double‐Effect Absorption Reheat Power Cycles
Authors: Yan Shilin , Zhao Minwei , Zhang Hongfu , Zheng Hongtao , Deng Fuquan
Published: June 2024

To enhance the overall performance of recompression supercritical carbon dioxide‐ (sCO2‐) based systems, two new double‐effect absorption reheat power cycles (DARPC) were developed in this study. These methods are based on the typical absorption power cycle (APC). For the proposed sCO2/DARPC systems, a parametric analysis of the thermodynamic and economic performances, as well as additional parametric optimisations, were performed quantitatively. The results indicate that replacing the APC subsystem with DARPC subsystems can enhance the total function of the sCO2 system even further, owing to the increased H2O vapour created in the separator and the reheating process, which adds to the greater net power output. Furthermore, compared to the DARPC2 subsystem, the DARPC1 subsystem may produce more H2O vapour from the generator and separator, resulting in an increase in net output power. When compared to a single sCO2 power cycle, multiobjective optimisations showed that the sCO2/DARPC1 and sCO2/DARPC2 systems could increase the exergy efficiency by 12.95% and 11.51% and decrease the total product unit cost by 9.67% and 8.37%, respectively. Furthermore, the sCO2/DARPC1 and sCO2/DARPC2 systems can achieve improvements in exergy efficiency of 4.95% and 3.61% and a total product unit cost of 4.52% and 3.15%, respectively, compared with the sCO2/APC system.
Primary Dysgerminoma of the Uterine Cervix: A Rare Case Report
Authors: Gashawbeza Biruck , Dereje Bethel , Abubeker Ferid A.
Published: June 2024

Introduction. Primary extragonadal germ cell tumors (EGCTs) are a very rare clinical encounter most commonly reported in males. Among females, the placenta, pelvis, uterus, brain, and mediastinum are the most common extragonadal sites and predominantly display nondysgerminoma histology. In this report, we present a case of a primary cervical dysgerminoma in a young female patient. Case Report. An 18-year-old nulligravid woman presented with a 12-month history of vaginal bleeding and discharge. Routine blood tests and serum levels of tumor markers were within normal limits. The chest X-ray was normal. A high-resolution pelvic MRI showed a well-defined lobulated cervicovaginal mass measuring 8×6×5 cm expanding into the vaginal canal with mild homogenous contrast enhancement. An incisional biopsy was performed vaginally under anesthesia, and histologic findings were consistent with dysgerminoma. A repeat follow-up pelvic MRI was done and showed a reduction in the size of the mass by more than 70%. The patient was treated with 4 cycles of bleomycin, etoposide, and cisplatin chemotherapy. Additional external pelvic beam radiation treatment was administered for a partial response. After 3 months of radiotherapy, a contrast abdominopelvic CT scan showed a recurrent cervicovaginal mass with extension to the pelvic sidewalls. The patient was initiated with ifosfamide, paclitaxel, and cisplatin (ITP) as second-line chemotherapy for a recurrent germ cell tumor but later died from hydronephrosis, chronic anemia, and sepsis. Conclusion. The uterine cervix is a very unusual site for primary dysgerminoma and can have a very aggressive clinical course. A high index of suspicion and an exhaustive workup are necessary to reach a diagnosis, particularly in a young patient presenting with a cervical lesion.
Graphene Oxide Nanoparticle-Assisted Promotion of Stevioside, Rebaudioside A, and Selected Biochemical Attributes in Stevia rebaudiana Bertoni
Authors: Rafiq Muhammad Talha , Sajid Zahoor Ahmad , Khilji Sheza Ayaz
Published: June 2024

Stevia rebaudiana Bert. is commonly known as candy leaf, sugar leaf, or sweet leaf. It is a natural sweetener that has low calories and is used as a substitute for sucrose. The objective of this research is to evaluate the effects of graphene oxide (GO) on the growth, biochemical activities, and stevioside and rebaudioside A production of Stevia in in vitro-raised plantlets. For this, green nanomaterials of GO (0, 2, 4, 6, 8, and 10 mgL−1) were applied to the in vitro plants to enhance its sweetness by triggering the production of stevioside and rebaudioside A and other growth and biochemical parameters. It was observed that all the growth parameters of Stevia plants significantly increased with all GO treatments tested. Total chlorophyll and protein contents were increased (1.85- and 2.65-fold increase from the control) by applying 8 mgL−1 of GO to the MS medium. The maximum value (4 mg·g−1 of protein) of peroxidase activity (POD) was observed by applying 4 mgL−1 of GO, 28.92-fold increase from the control. In comparison, superoxide dismutase activity (SOD) (0.4 mg·g−1 protein) was observed with 10 mgL−1 of GO (1.56-fold increase from the control). Stevioside (12.9 and 8.9 mg·g−1 DW) and rebaudioside A (3.2 and 0.81 mg·g−1 DW) were observed only at 6 and 8 mg·L−1 treatment of graphene oxide. According to the findings, using graphene oxide (GO) had a significant impact on the growth, biochemical activities, and steviol glycoside production in Stevia. This shows that GO has the potential to be a valuable enhancer of sweetness and overall Stevia leaf quality, providing great prospects for the development of low-calorie natural sweeteners.
An Unconditionally Stable Numerical Method for Space Tempered Fractional Convection-Diffusion Models
Authors: Qiu Zeshan
Published: June 2024

A second-order numerical method for two-sided tempered fractional convection-diffusion equations is studied in this paper, both convection term and diffusion term are approximated by the tempered weighted and shifted Grünwald difference operators, the first time partial derivative is discretized by the Crank–Nicolson method, and then a class of second-order numerical schemes is derived. By means of matrix method, numerical schemes are proved to be unconditionally stable and convergent with order Oτ2+h2. The validity of the proposed numerical scheme is verified by numerical experiments.
Improvement of Apriori Algorithm Using Parallelization Technique on Multi-CPU and GPU Topology
Authors: Bavarsad Salehpour Hooman , Haj Seyyed Javadi Hamid , Asghari Parvaneh , Shiri Ahmad Abadi Mohammad Ebrahim
Published: June 2024

In the domain of data mining, the extraction of frequent patterns from expansive datasets remains a daunting task, compounded by the intricacies of temporal and spatial dimensions. While the Apriori algorithm is seminal in this area, its constraints are accentuated when navigating larger datasets. In response, we introduce an avant-garde solution that leverages parallel network topologies and GPUs. At the heart of our method are two salient features: (1) the use of parallel processing to expedite the realization of optimal results and (2) the integration of the cat and mouse-based optimizer (CMBO) algorithm, an astute algorithm mirroring the instinctual dynamics between predatory cats and evasive mice. This optimizer is structured around a biphasic model: an initial aggressive pursuit by the cats and a subsequent calculated evasion by the mice. This structure is enriched by classifying agents using their objective function scores. Complementing this, our architectural blueprint seamlessly amalgamates dual Nvidia graphics cards in a parallel configuration, establishing a marked ascendancy over conventional CPUs. In amalgamation, our approach not only rectifies the inherent shortfalls of the Apriori algorithm but also accentuates the extraction of association rules, pinpointing frequent patterns with enhanced precision. A comprehensive evaluation across a spectrum of network topologies explains their respective merits and demerits. Set against the benchmark of the Apriori algorithm, our method conspicuously outperforms in terms of speed and effectiveness, heralding a significant stride forward in data mining research.
Characterisation of Luffa cylindrica Fibre from Cameroon for Use in Composites: Effect of Alkaline Treatment
Authors: Mono Jean Aimé , Rachelle Assona Appolince , Takoumbe Claude , Assiene Odette Thérèse Adegono , Nguimjeu Prince Hermann Pokem , Belinga Fabrice Ella
Published: June 2024

The study of Luffa cylindrica is seen as an alternative solution for the development of green materials. However, a lack of knowledge about some of their characteristics can slow down these applications. The present study focuses on the characterisation of the fibre derived from the Luffa cylindrica sponge. In this study, the fibre was extracted using a manual technique, followed by a treatment cycle with 5%, 7.5%, and 10% NaOH at a temperature of 28°C for 60 min. The results obtained show the lightness of the fibre through its low density (0.233‐0.419 g·cm-3), and a hydrophilic nature of the fibre is observed through its water absorption capacity (106.86‐180.53%), its relative humidity (9.86‐15.33%), and its capacity to diffuse water (2.03 × 10−14 m2·s-1 to 4.61 × 10−14 m2·s-1), which is close to that of other plant fibres, which means that it can be classified as porous, with possible applications in insulating and lightweight materials. Its diameter (418.61‐554.42 μm) and linear mass (34‐58 g·km-1) are high, in contrast to other fibres used in the textile industry to produce yarns. The mechanical results, namely, stress at break (9.744‐27.45 MPa), Young’s modulus (307.56‐582.41 MPa), and elongation at break (3.45%‐8.11%), are close to those of other plant fibres used as reinforcement in polymer matrix composites for applications in the automotive, insulation, furniture, and construction industries. Luffa cylindrica fibre could have applications in the same direction. Fibres treated with 5% NaOH effectively improve the properties of raw fibres.
The Effect of Pressure Variations on the Electronic Structure, Phonon, and Superconducting Properties of Yttrium Hydrogen Selenide Compound
Authors: Bekele Aredo Tadesse , Wodajo Shura Megersa , Asfaw Afrassa Mesfin , Tadele Kumneger , Tolessa Maremi Fekadu
Published: June 2024

The electronic, phonon, and superconducting properties of hexagonal yttrium hydrogen selenide (YHSe) are studied using density functional theory (DFT) methods. The DFT analysis revealed that the energy bandgap and density of states near the Fermi energy (ɛF) decrease with increasing pressure. Additionally, the influence of pressure on the vibrational properties of YHSe is also examined. The findings of the vibrational properties indicate a stiffening of lattice dynamics under pressure and the identification of negative Gruneisen parameters at certain high symmetry sites. This enhances and deepens the understanding of the vibrational characteristics of YHSe under extreme pressure conditions. Finally, the electron–phonon coupling (EPC) parameter (λ) is examined under different pressures. The examination of EPCs across varying pressures showed a significant increase from 0.826 (0 GPa) to 2.6287 (200 GPa), where an increase in this EPC is found to increase the superconducting critical temperature (Tc). Furthermore, the nonmonotonic relationship between the superconducting critical temperature (Tc) and external pressure (P) in the YHSe compound is observed. Initially, Tc decreases with increasing pressure and then begins to rise again, reaching its peak value at extreme pressure. These findings provide valuable insights into the pressure-dependent properties of YHSe and have important implications for the field of superconductivity in condensed matter physics.
Predicting students' success level in an examination using advanced linear regression and extreme gradient boosting
Authors: Wahyuningsih Tri , Iriani Ade , Dwi Purnomo Hindriyanto , Sembiring Irwan
Published: June 2024

This research employs a hybrid approach, integrating advanced linear regression and extreme gradient boosting (XGBoost), to forecast student success rates in exams within the dynamic educational landscape. Utilizing Kaggle-sourced data, the study crafts a model amalgamating advanced linear regression and XGBoost, subsequently assessing its performance against the primary dataset. The findings showcase the model's efficacy, yielding an accuracy of 0.680 on the fifth test and underscoring its adeptness in predicting students' exam success. The discussion underscores XGBoost's prowess in managing data intricacies and non-linear features, complemented by advanced linear regression offering valuable coefficient interpretations for linear relationships. This research innovatively contributes by harmonizing two distinct methods to create a predictive model for students' exam success. The conclusion emphasizes the merits of an ensemble approach in refining prediction accuracy, recognizing, however, the study's limitations in terms of dataset constraints and external factors. In essence, this study enhances comprehension of predicting student success, offering educators insights to identify and support potentially struggling students.
Improving the quality of handwritten image segmentation using k-means clustering algorithms with spatial filters
Authors: Munsarif Muhammad , Saman Muhammad
Published: June 2024

One of the ways to predict human characters is by using handwritten patterns. Graphologists have analyzed handwriting to determine a writer's personality by considering several parameters: writing slopes, spacing, inclination, and writing size. The results of the analysis have been widely used as a reference for psychologists to assess an individual's personality. Moreover, researchers have applied techniques to identify human characters using image processing techniques. However, different styles of handwriting require more research to develop. The process of separating objects from backgrounds needs a segmentation process. This research improves the quality of handwritten image segmentation using k-means clustering algorithms with the spatial filter. This spatial filter consisted of the median and mean filters. This research created various k values to gain the best segmentation results. The results showed that the median filter with a kernel size of 3×3 and the k value = 2 was the best segmentation result because the value of silhouette coefficient was the highest compared to the value of filter type and other k values which reach 99.22%.
Fingerprint based smart door lock system using Arduino and smartphone application
Authors: Sutikno Tole , Faqih Ubaidillah Moh Ainur , Arsadiando Watra , Purnama Hendril Satrian
Published: June 2024

In 2023, crime cases in Indonesia reached 105,133. Cases of theft with aggravation dominate the majority of cases. Everyone is concerned about safety, but doors are typically opened and closed using physical keys. This is vulnerable to being tampered with with fake keys, which can lead to house break-ins and theft. In this research, we propose a fingerprint-based wireless door lock design using Arduino and a smartphone. We offer this solution as a preventive measure to reduce the high rate of theft in homes or other buildings. The devices used are Arduino UNO R3, fingerprint sensor, HC-05 Bluetooth module, buzzer, and door lock solenoid. The results of the fingerprint-based wireless door lock using Arduino and a smartphone can function well, with an average response time of 1.20 seconds. Furthermore, testing the HC-05 Bluetooth when sending signals to a smartphone shows that it can read data accurately with an average response time of 1.54 seconds.
Trends in sentiment of Twitter users towards Indonesian tourism: analysis with the k-nearest neighbor method
Authors: Harahap Eka Purnama , Purnomo Hindriyanto Dwi , Iriani Ade , Sembiring Irwan , Nurtino Tio
Published: June 2024

This research analyzes the sentiment of Twitter users regarding tourism in Indonesia using the keyword "wonderful Indonesia" as the tourism promotion identity. This study aims to gain a deeper understanding of the public sentiment towards "wonderful Indonesia" through social media data analysis. The novelty obtained provides new insights into valuable information about Indonesian tourism for the government and relevant stakeholders in promoting Indonesian tourism and enhancing tourist experiences. The method used is tweet analysis and classification using the K-nearest neighbor (KNN) algorithm to determine the positive, neutral, or negative sentiment of the tweets. The classification results show that the majority of tweets (65.1% out of a total of 14,189 tweets) have a neutral sentiment, indicating that most tweets with the "wonderful Indonesia" tagline are related to advertising or promoting Indonesian tourism. However, the percentage of tweets with positive sentiment (33.8%) is higher than those with negative sentiment (1.1%). This study also achieved training results with an accuracy rate of 98.5%, precision of 97.6%, recall of 98.5%, and F1-score of 98.1%. However, reassessment is needed in the future as Twitter users' sentiments can change along with the development of Indonesian tourism itself.
Deep learning technique for plant disease detection
Authors: Adekunle Temitope Samson , Lawrence Morolake Oladayo , Alabi Oluwaseyi Omotayo , Afolorunso Adenrele A. , Ebong Godwin Nse , Oladipupo Matthew Abiola
Published: June 2024

A nation's economy is primarily reliant on agricultural growth. However, several plant diseases seriously impair crop growth, both in terms of quantity and quality. Due to a lack of subject matter specialists and low contrast data, accurate diagnosis of many diseases by hand is highly difficult and time-consuming. The farm management system is therefore looking for a method for automatically detecting early illnesses. To overcome these challenges and correctly classify the different diseases, an efficient and small deep learning-based framework (E-GreenNet) is proposed. A MobileNetV3Small model is used as the foundation of our end-to-end architecture to produce finely tuned, discriminative, and noticeable features. Furthermore, the new plant composite (PC), plantvillage (PV), and data repository of leaf images (DRLI) datasets are used to independently train our proposed model, and test samples are used to evaluate its actual performance. The suggested model achieved accuracy rates of 1.00 percent, 0.96 percent, and 0.99 percent on the given datasets after a rigorous experimental study. Additionally, a comparative investigation of our proposed technique against the state-of-the-art (SOTA) reveals extremely high discriminative scores.
Sentiment analysis of online licensing service quality in the energy and mineral resources sector of the Republic of Indonesia
Authors: Hizqil Ahmad , Ruldeviani Yova
Published: June 2024

The Ministry of Energy and Mineral Resources of the Republic of Indonesia regularly assessed public satisfaction with its online licensing services. User rated their satisfaction at 3.42 on a scale of 4, below the organization's average of 3.53. Evaluating public service performance is crucial for quality improvement. Previous research relied solely on survey data to assess public satisfaction. This study goes further by analyzing user feedback in text form from an online licensing application to identify negative aspects of the service that need enhancement. The dataset spanned September 2019 to February 2023, with 24,112 entries. The choice of classification methods on the highest accuracy values among decision tree, random forest, naive bayes, stochastic gradient descent, logistic regression (LR), and k-nearest neighbor. The text data was converted into numerical form using CountVectorizer and term frequency-inverse document frequency (TF-IDF) techniques, along with unigrams and bigrams for dividing sentences into word segments. LR bigram CountVectorizer ranked highest with 89% for average precision, F1-score, and recall, compared to the other five classification methods. The sentiment analysis polarity level was 36.2% negative. Negative sentiment revealed expectations from the public to the ministry to improve the top three aspects: system, mechanism, and procedure; infrastructure and facilities; and service specification product types.
Implementing lee's model to apply fuzzy time series in forecasting bitcoin price
Authors: Farida Yuniar , Ainiyah Lailatul
Published: June 2024

Over time, cryptocurrencies like Bitcoin have attracted investor's and speculators' interest. Bitcoin's dramatic rise in value in recent years has caught the attention of many who see it as a promising investment asset. After all, Bitcoin investment is inseparable from Bitcoin price volatility that investors must mitigate. This research aims to use Lee's Fuzzy Time Series approach to forecast the price of Bitcoin. A time series analysis method called Lee's Fuzzy Time Series to get around ambiguity and uncertainty in time series data. Ching-Cheng Lee first introduced this approach in his research on time series prediction. This method is a development of several previous fuzzy time series (FTS) models, namely Song and Chissom and Cheng and Chen. According to most previous studies, Lee's model was stated to be able to convey more precise forecasting results than the classic model from the FTS. This study used first and second orders, where researchers obtained error values from the first order of 5.419% and the second order of 4.042%, which means that the forecasting results are excellent. But of both orders, only the first order can be used to predict the next period's Bitcoin price. In the second order, the resulting relations in the next period do not have groups in their fuzzy logical relationship group (FLRG), so they can not predict the price in the next period. This study contributes to considering investors and the general public as a factor in keeping, selling, or purchasing cryptocurrencies.
Hybrid model for detection of brain tumor using convolution neural networks
Authors: K. Bhagyalaxmi , Dwarakanath B.
Published: June 2024

The development of aberrant brain cells, some of which may turn cancerous, is known as a brain tumor. Magnetic resonance imaging (MRI) scans are the most common technique for finding brain tumors. Information about the aberrant tissue growth in the brain is discernible from the MRI scans. In numerous research papers, machine learning, and deep learning algorithms are used to detect brain tumors. It takes extremely little time to forecast a brain tumor when these algorithms are applied to MRI pictures, and better accuracy makes it easier to treat patients. The radiologist can make speedy decisions because of this forecast. The proposed work creates a hybrid convolution neural networks (CNN) model using CNN for feature extraction and logistic regression (LR). The pre-trained model visual geometry group 16 (VGG16) is used for the extraction of features. To reduce the complexity and parameters to train we eliminated the last eight layers of VGG16. From this transformed model the features are extracted in the form of a vector array. These features fed into different machine learning classifiers like support vector machine (SVM), naïve bayes (NB), LR, extreme gradient boosting (XGBoost), AdaBoost, and random forest for training and testing. The performance of different classifiers is compared. The CNN-LR hybrid combination outperformed the remaining classifiers. The evaluation measures such as recall, precision, F1-score, and accuracy of the proposed CNN-LR model are 94%, 94%, 94%, and 91% respectively.
Fingerprint based smart door lock system using Arduino and smartphone application
Authors: Sutikno Tole , Ubaidillah Moh Ainur Faqih , Arsadiando Watra , Purnama Hendril Satrian
Published: June 2024

In 2023, crime cases in Indonesia will reach 105,133. Cases of theft with aggravation dominate the majority of cases. Everyone is concerned about safety, but doors are typically opened and closed using physical keys. This is vulnerable to being tampered with with fake keys, which can lead to house break-ins and theft. In this research, we propose a fingerprint-based wireless door lock design using Arduino and a smartphone. We offer this solution as a preventive measure to reduce the high rate of theft in homes or other buildings. The devices used are Arduino UNO R3, fingerprint sensor, HC-05 Bluetooth module, buzzer, and door lock solenoid. The results of the fingerprint-based wireless door lock using Arduino and a smartphone can function well, with an average response time of 1.20 seconds. Furthermore, testing the HC-05 Bluetooth when sending signals to a smartphone shows that it can read data accurately with an average response time of 1.54 seconds.
Deep convolutional neural network for Lampung character recognition
Authors: Bintoro Panji , Zulkifli Zulkifli , Fitriana Fitriana , Sukarni Sukarni
Published: June 2024

Recognition of document based, and handwritten characters has recently emerged as highly relevant field of study in the field of digital image processing. The ability to read and write Lampung script is a crucial competency as it helps preserve the language, which is a part of Indonesian culture. This research utilizes data obtained from classified documents and handwritten samples, categorized into eight types. To recognize Lampung characters, deep convolutional neural network (DCNN) architecture is proposed. The novelty of this architecture lies in optimizing document-based and handwritten character recognition to achieve the best performance in terms of accuracy and execution time. The proposed architecture will be compared to principal component analysis (PCA) combined with support vector machine (SVM) to evaluate its results. Experimental results using the DCNN architecture show an average accuracy of 99.3% and an execution time of 283 seconds for all data, while PCA and SVM exhibit an average accuracy of 92.9%. Furthermore, the recognition results for all data from documents and handwritten samples yield satisfactory accuracy of 98.6%. These results make the DCNN architecture suitable for use in recognizing Lampung characters and are expected to make it easier for Lampung people to recognize Lampung character.
Shielding privacy: a technique of extenuating composition attacks in various independent data publication
Authors: Faruq Md. Omar , Walid Md. Abul Ala , Baowaly Mrinal Kanti , Devnath Maloy Kumar , Ejaz Md. Sabbir , Barman Pronob Kumar , Sattar A H M Sarowar
Published: June 2024

Protecting personal information from unauthorized access is a critical concern for individuals. However, the accumulation of confidential information by various organizations, such as banks and hospitals, for regular communication creates a potential vulnerability. If an individual visits two hospitals and both facilities independently release the individual's gathered data, a malicious adversary could potentially deduce confidential information through a composition attack. Therefore, developing methods that protect individuals from composition attacks is crucial. According to the size of the dataset and the percentage of overlapping persons, our study examines the effectiveness of composition attacks. We propose a knowledge domain-based design to mitigate successful composition attacks, which has shown promising results in reducing such attacks and compared to existing studies based on the k-anonymity and l-diversity models. Our approach leverages a knowledge domain to reduce the likelihood of data breaches, demonstrating the effectiveness of our method in protecting individuals' privacy and preventing unauthorized access to sensitive information. Finally, the effects of data utility on the diverse data set have been measured.
Security-based low-density parity check encoder for 5G communication
Authors: Rajangam Balamurugan , Alagarsamy Manjunathan , Radhakrishnan Chirakkal Rathish , Assegie Tsehay Admassu , Salau Ayodeji Olalekan , Quansah Andrew , Chowdhury Nur Mohammad , Chowdhury Ismatul Jannat
Published: June 2024

The fifth generation (5G) of mobile telecommunication standards is intended to offer better performance and efficiency. One of the most significant difficulties in delivering safe data transfer through the transmission channel in the emerging 5G technology is channel-coding security. This research primarily focused on offering information transmission that is secure in the place of novel assaults such as side-channel attacks. In this research, we present a low-density parity check (LDPC) encoder that is constructed using the multiplicative masking method to overcome side-channel attacks, one of the most significant security concerns for the upcoming 5G system. As a result, it offers greater security, reduced complexity, and higher performance. Power, area, and delay can all be calculated using LDPC codes. Comparing multiplicative masking implemented LDPC encoders to ordinary channel coding techniques in terms of security seen that multiplicative masking implemented LDPC encoders offer more security. The program Xilinx ISE 14.7 can synthesize the analysis. The advantage of multiplicative masking is that it offers a promising level of security through the principle of randomization, which is the foundation of the procedure. According to the analysis, the secured transmission of the data by the proposed multiplicative masking implemented LDPC encoder is increased.
Predicting the effects of microcredit on women’s empowerment in rural Bangladesh: using machine learning algorithms
Authors: Polin Johora Akter , Sarker Md. Fouad Hossain , Dolon Mst Dilruba Khanom , Hasan Nahid , Rahman Md. Mahafuzur , Vasha Zannatun Nayem
Published: June 2024

This study aimed to predict the impact of microcredit on women’s empowerment in Bangladesh using machine learning (ML) algorithms. In rural Bangladesh, where microcredit programs are not significantly employed, data for the study was gathered through a survey. The study gathered data on a range of socioeconomic, demographic, and women’s empowerment indicators. The Naive Bayes (NB), sequential minimal optimization (SMO), k-nearest neighbor (k-NN), decision tree (DT), and random forest (RF) ML techniques were used in the investigation. In terms of the prediction of women’s empowerment, the findings indicated that all five algorithms performed well, with the DT having the highest level of accuracy (83.72%). The results of this study have significant consequences for Bangladesh’s microcredit programs and those in nations that are developing. Microcredit programs can focus their efforts on women who, based on their socioeconomic and demographic features, are most likely to benefit from the program by employing ML algorithms. This may result in more successful microcredit projects that support the empowerment of women and general socioeconomic growth.
Terapia Ocupacional: Ciência ou Tecnologia?
Authors: Tassara Eda Terezinha de Oliveira
Published: June 2024

The discussion presents here took place in 1992 as a promotion of the Curso de Terapia Ocupacional da Universidade de São Paulo PhD Eda T. Oliveira Tassara introduced some issues about the occupational therapy^ knowledge. The material of the transcription was revised by her. So we decided to keep the presentation form in order to share the richeness of the meeting.
Terapia Ocupacional e reabilitação psicossocial: uma relação possível?
Authors: Benetton Maria José
Published: June 2024

Making a bibliographic briefrevision of the foreign and of the national literature in occupational therapy, including the altemative projects to "hospitalocentro" in mental health, the author opens an important space to debate the relationship between this profession and the psychosocial rehabilitation.
O mito da atividade terapêutica
Authors: Nascimento Beatriz Ambrósio do
Published: June 2024

This paper discusses the most common conceptions among the occupational therapists about the use of activities as a therapeutic aid. The belief in the intrinsic therapeutic proprieties ofthe activities, by itself, is discussed as wçll as its laboriatorial use, as a moment for doing fisics exercices, for learning standarts of motricity, behavior, social and professional habits and for emocional experience. The psicodinamic view add the inconscients emocional factors in the analisy of the activities and in the relationship between therapist-patient-activity. In both cases the patienfs concret condictions oflife and action do not enter in the occupationaltherapistspecific intervention.
Processos criativos e Terapia Ocupacional
Authors: Castro Eliane Dias de , Silva Reinaldo José Gomes da
Published: June 2024

O artigo procura fazer uma correlação entre os processos criativos na obra de Arte e um determinado procedimento de Terapia Ocupacional, apontando a importância dos mesmos para o desenvolvimento do homem inserido em seu contexto cultural.
Terapia Ocupacional: considerações sobre o contexto profissional
Authors: Ferrigno Iracema Serrat Vergotti
Published: June 2024

O artigo apresenta as limitações do trabalho clínico, geralmente explicitadas pelos terapeutas ocupacionais em seu cotidiano profissional. A partir dessa interpretação, aborda o contexto que influenciou a criação, a estruturação e o desenvolvimento da profissão no Brasil. As reflexões são orientadas no sentido de ampliar e aprofundar a análise feita por Kielhofner, à luz de autores críticos, desvelando algumas das contradições das relações profissionais.
Treinamento de autocuidados em pacientes portadores de bronquiectasia
Authors: Caromano Fátima Aparecida , Tanaka Clarice , Kayano Sandra Yumlko , Steinberg Flávia Likler , Lacombe Adriana de Carvalho
Published: June 2024

This report shows an aspect of practising in autocare and physical therapy for patiente with bronchiectasis that had not gone for surgery. The analisys of the programe efficiency had been demonstrated by control of hydratation, expectoration and exercises. We was had satisfying resulte as comportamental well as function and clinical aspects.
Reflexões sobre a influência do fator cultural no processo de atendimento de Terapia Ocupacional
Authors: Brunello Maria Inês Britto
Published: June 2024

The complexity of questione which emerge from the attendance with individuais from different origins and, most of the time, different from the therapis, himself, shows us that a partial vision of the person isn't errough for the undertanding of this action. The aim of this work ia to understand the influence of culture in the way of acting, thinking of each persons, assimilating the meaning of each human action, always relating it to its context It a important to reconstructthe story of each patient, not only conceming the himaelf but also his cultural and social environments.
Informação: maravilha curativa? De como envolver pais, legisladores, cientistas e professores desinformados, sobrecarregados, desmotivados e desgastados com a questão da informação e da cidadania.
Authors: Gil Marta
Published: June 2024

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Corpo deficiente: um desvio da norma?
Authors: Rocha Eucenir Fredini
Published: June 2024

Este artigo como se constrói a representação social do corpo deficiente e qual o lugar que é ocupado pelas pessoas que o possuem em nossa sociedade, o corpo humano a partir de século XX passa a ser coisificado a fim de atender às necessidades da ordem burguesa vigente, ou seja, ele deve corresponder aos padrões específicos de funcionalidade e estética adequados e ao consumo, qualquer transgressão destas expectativas torna o indivíduo portador de uma deficiência física, estigmatizado e marginalizado socialmente. O processo de estigmatização se dá através da generalização das predicações atribuídas ao corpo deficiente a toda a sua individualidade: "incapaz","improdutivo", "assexuado","anti-estético", etc.. Enfim a totalidade da pessoa portadora de deficiência física a ser encarada como desviante da Norma vigente.
Aspectos orgânicos na patogenia da psicose infantil: uma revisão
Authors: Fernandes Fernanda Dreux Miranda
Published: June 2024

This literatura review refers to works about organic aspects on the origin of infantile autism. They are organized, according to its central focus, in: pregnancy and birth factors, specific neurological aspects, significative signs associated (cognition and language), biochemical aspects and genetic aspects.