Search Results (3,043)

Presidential elections are an important political event that often trigger intense debate. With more than 139 million users, YouTube serves as a significant platform for understanding public opinion through sentiment analysis. This study aimed to implement deep learning techniques for a multi-label sentiment analysis of comments on YouTube videos related to the 2024 Indonesian presidential election. Offering a fresh perspective compared to previous research that primarily employed traditional classification methods, this study classifies comments into eight emotional labels: anger, anticipation, disgust, joy, fear, sadness, surprise, and trust. By focusing on the emotional spectrum, this study provides a more nuanced understanding of public sentiment towards presidential candidates. The CRISP-DM method is applied, encompassing stages of business understanding, data understanding, data preparation, modeling, evaluation, and deployment, ensuring a systematic and comprehensive approach. This study employs a dataset comprising 32,000 comments, obtained via YouTube Data API, from the KPU and Najwa Shihab channels. The analysis is specifically centered on comments related to presidential candidate debates. Three deep learning models—Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (Bi-LSTM), and a hybrid model combining CNN and Bi-LSTM—are assessed using confusion matrix, Area Under the Curve (AUC), and Hamming loss metrics. The evaluation results demonstrate that the Bi-LSTM model achieved the highest accuracy with an AUC value of 0.91 and a Hamming loss of 0.08, indicating an excellent ability to classify sentiment with high precision and a low error rate. This innovative approach to multi-label sentiment analysis in the context of the 2024 Indonesian presidential election expands the insights into public sentiment towards candidates, offering valuable implications for political campaign strategies. Additionally, this research contributes to the fields of natural language processing and data mining by addressing the challenges associated with multi-label sentiment analysis. Full article

Honeybee larvae and pupae form the cornerstone of colony survival, development, and reproduction. Azoxystrobin is an effective strobilurin fungicide that is applied during the flowering stage for controlling plant pathogens. The contaminated nectar and pollen resulting from its application are collected by forager bees and impact the health of honeybee larvae and pupae. The current study evaluated the survival, development, and physiological effects of azoxystrobin exposure on the larvae and pupae of Apis mellifera worker bees. The field-recommended concentrations of azoxystrobin were found to suppress the survival indices and lifespan in the larval as well as pupal stages; moreover, the rates of the survival and pupation of larvae as well as the body weights of the pupae and newly-emerged adult bees were significantly reduced upon long-term exposure to azoxystrobin. In addition, azoxystrobin ingestion induced changes in the expression of genes critical for the development, immunity, and nutrient metabolism of larvae and pupae, although the expression profile of these genes differed between the larval and pupal stages. Results indicated the chronic toxicity of azoxystrobin on the growth and development of honeybee larvae and pupae, which would affect their sensitivity to pathogens and other external stresses during the development stage and the study will provide vital information regarding the pollination safety and rational use of pesticides. Full article

Background/Objectives: The importance of fixed-dose combinations (FDCs) for the treatment of hypertension is well established. However, from a stability perspective, FDCs present a challenge since the degradation of one active pharmaceutical ingredient (API) can be affected by the presence of another API. The aim of this study was to compare the degradation behaviors and evaluate the degradation kinetics of three antihypertensive drugs, perindopril tert-butylamine (PER), amlodipine besylate (AML), and indapamide (IND). Methods: The degradation processes were studied using the previously developed reverse phase high-performance liquid chromatographic (RP-HPLC) method after exposing each drug individually, as well as the combinations of two/three drugs, to different stress factors, such as light, oxidation, acidic, basic, or neutral pH values at different temperatures. Results: The results show that PER is most unstable under basic conditions and that AML displays a negative, while IND displays a positive effect, on PER stability when combined. AML is most affected by basic conditions and oxidation, and its stability is affected by both drugs positively; IND undergoes extreme photolysis, which is positively affected by AML but negatively by PER. Conclusions: Great care must be taken when formulating FDCs with these three drugs, as well as solutions or oral suspensions adjusted for geriatric or pediatric populations, since the stability of all three drugs is greatly affected by pH conditions, as well as light or oxidation factors and their interactions. Full article

Heterogeneous federated learning, as an innovative variant of federated learning, aims to break through the constraints of vanilla federated learning on the consistency of model architectures to better accommodate the heterogeneity in mobile computing scenarios. It introduces heterogeneous and personalized local models, which effectively accommodates the heterogeneous data distributions and hardware resource constraints of individual clients, and thus improves computation and communication efficiency. However, it poses a challenge to model ownership protection, as watermarks embedded in the global model are corrupted to varying degrees when they are migrated to a user’s heterogeneous model and cannot continue to provide complete ownership protection in the local models. To tackle these issues, we propose a dynamic black-box model watermarking method for heterogeneous federated learning, PWFed. Specifically, we design an innovative dynamic watermark generation method which is based on generative adversarial network technology and is capable of generating watermark samples that are virtually indistinguishable from the original carriers. This approach effectively solves the limitation of the traditional black-box watermarking technique, which only considers static watermarks, and makes the generated watermarks significantly improved in terms of stealthiness and difficult to detect by potential model thieves, thus enhancing the robustness of the watermarks. In addition, we design two watermark embedding strategies with different granularities in the heterogeneous federated learning environment. During the watermark extraction and validation phase, PWFed accesses watermark samples claiming ownership of the model through an API interface and analyzes the differences between their output and the expected labels. Our experimental results show that PWFed achieves a 99.9% watermark verification rate with only a 0.1–4.8% sacrifice of main task accuracy on the CIFAR10 dataset. Full article

Quality control in pharmaceutical manufacturing necessitates rigorous testing and approval, adhering to Current Good Manufacturing Practices before commercialization. The production of solid drugs presents significant industrial challenges regarding uniformity, homogeneity, and consistency. Traditional quality guidelines rely on classical analytical methods such as liquid chromatography coupled with mass spectrometry. However, the emergence of Process Analytical Technology introduced non-destructive, rapid, and cost-effective methods like UV-Visible Diffuse Reflectance Spectroscopy. The present study aimed to develop a chemometric method for quantifying Active Pharmaceutical Ingredients (APIs) in Neo Nisidine^®, a solid mixture drug, using spectrophotometric data. The Net Analyte Signal (NAS) method, combined with standard additions, allowed the creation of a pseudo-univariate standard addition model, overcoming some challenges in solid-phase analysis. Successful quantifications of APIs in ideal laboratory samples and real pharmaceutical tablets were obtained. NAS-based chemometric models showed high precision and reliability, whose results were validated by comparisons with HPLC ones. The study revealed that solid-phase spectrophotometric analyses can be considered a valid alternative to API analyses. Solid-phase analysis offers non-destructive, cost-effective, and environmentally friendly benefits, enabling its integration into pharmaceutical production to improve quality control. Full article

The honey bee Apis cerana cerana (A. c. cerana), a subspecies of Apis cerana, is endemic in China and possesses a valuable ecological niche. Understanding the ways to protect this honey bee’s populations is crucial, but this topic has been understudied. For the efficient utilization of beekeeping and pollination, there is a need to explore its biology and management practices. In light of this, the current study was carried out to investigate the ontogeny and dietary differences in the queen and worker castes of the A. c. cerana honey bee. This article presents, supplemented by reference images, a detailed description of the life history of A. c. cerana queens and workers. Additionally, this study investigated the nutritional differences between royal jelly (RJ) and worker jelly (WJ) at various larval ages. The contents of the moisture, crude protein, and amino acids in RJ and WJ were determined via freeze drying, Kjeldahl nitrogen determination, and ultra-high performance liquid chromatography. The results highlight significant variations in the moisture content, crude protein concentration, and amino acid concentration between RJ and WJ. The results offer theoretical support for ex situ artificial rearing practices of A. c. cerana. Full article

This work presents a diagnosis of greenhouse gas (GHG) emissions for floating production storage and offloading (FPSO) platforms for oil and gas production offshore, using calculation methodologies from the American Petroleum Institute (API) and U.S. Environmental Protection Agency (EPA). To carry out this analysis, design data of an FPSO platform is used for the GHG emissions estimation, considering operations under steady conditions and oil and gas processing system simulations in the Aspen HYSYS^® software. The main direct emission sources of GHG are identified, including the main combustion processes (gas turbines for electric generation and gas turbine-driven CO₂ compressors), flaring and venting, as well as fugitive emissions. The study assesses a high CO₂ content in molar composition of the associated gas, an important factor that is considered in estimating fugitive emissions during the processes of primary separation and main gas compression. The resulting information indicates that, on average, 95% of total emissions are produced by combustion sources. In the latest production stages of the oil and gas field, it consumes 2 times more energy and emits 2.3 times CO₂ in terms of produced hydrocarbons. This diagnosis provides a baseline and starting point for the implementation of energy efficiency measures and/or carbon capture and storage (CCS) technologies on the FPSO in order to reduce CO₂ and CH₄ emissions, as well as identify the major sources of emissions in the production process. Full article

This study proposes the Pairwise Attention Enhancement (PAE) model to address the limitations of the Vision Transformer (ViT). While the ViT effectively models global relationships between image patches, it encounters challenges in medical image analysis where fine-grained local features are crucial. Although the ViT excels at capturing global interactions within the entire image, it may potentially underperform due to its inadequate representation of local features such as color, texture, and edges. The proposed PAE model enhances local features by calculating cosine similarity between the attention maps of training and reference images and integrating attention maps in regions with high similarity. This approach complements the ViT’s global capture capability, allowing for a more accurate reflection of subtle visual differences. Experiments using Clock Drawing Test data demonstrated that the PAE model achieved a precision of 0.9383, recall of 0.8916, F1-Score of 0.9133, and accuracy of 92.69%, showing a 12% improvement over API-Net and a 1% improvement over the ViT. This study suggests that the PAE model can enhance performance in computer vision fields where local features are crucial by overcoming the limitations of the ViT. Full article

With the depletion of shallow oil and gas resources, wells are being drilled to deeper and deeper depths to find new hydrocarbon reserves. This study presents the selection and optimization process of the cement slurries to be used for the deepest well ever drilled in China, with a planned vertical depth of 11,100 m. The bottomhole circulating and static temperatures of the well were estimated to be 210 °C and 220 °C, respectively, while the bottomhole pressure was estimated to be 130 MPa. Laboratory tests simulating the bottomhole conditions were conducted to evaluate and compare the slurry formulations supplied by four different service providers. Test results indicated that the inappropriate use of a stirred fluid loss testing apparatus could lead to overdesign of the fluid loss properties of the cement slurry, which could, in turn, lead to abnormal gelation of the cement slurry during thickening time tests. The initial formulation given by different service providers could meet most of the design requirements, except for the long-term strength stability. The combined addition of crystalline silica and a reactive aluminum-bearing compound to oil well cement is critical for preventing microstructure coarsening and strength retrogression at 220 °C. Two of the finally optimized cement slurry formulations had thickening times more than 4 h, API fluid loss values less than 50 mL, sedimentation stability better than 0.02 g/cm³, and compressive strengths higher than 30 MPa during the curing period from 1 d to 30 d. Full article

Laser welding was performed using different filler wires, ER70S steel, commercially pure iron, and pure nickel filler, in the context of welding X80 pipeline steel to assess the microstructure and mechanical properties of the weld metal. Introducing an ER70S wire promoted acicular ferrite formation in the fusion zone, compared to a bainitic microstructure in an autogenous laser weld. The use of pure iron wire was considered as a potential strategy for reducing hardenability, as it led to the dilution of alloying elements in the fusion zone, increasing ferrite content and reducing weld metal hardness to a level compliant with API pipeline standards. The addition of pure nickel wire was used to reveal the degree of weld metal mixing imposed by the laser (thus providing an unambiguous tracer element) when it is combined with filler material dilution in the fusion zone, revealing that the upper region contained 38% wire material and the lower region only 12%. This accounts for the differences observed between the upper versus lower portions of the weld metal when other wires are used, and the use of hardness mapping and micro-indentation demonstrates the correlation between the variations in mechanical properties and microstructural differences introduced by incomplete mixing of the filler wire elements. Full article

In the roll bending process, the rolling force acting on the roller shafts is one of the most important parameters since, on the one hand, it determines the process settings including the pre-loading, and, on the other hand, its distribution and size may affect the integrity of both the bending system and the final product. In this study, the three-roller bending process was modeled using a two-dimensional plane–strain finite element method, and the rolling force was determined as a function of plate thickness, upper roller diameter, and yield strength for various API steel grades. Based on the numerical simulation results, a critical bending angle of 41° was identified and the rolling systems were divided into two categories, of less than or equal to, and greater than 41°, and an analytical model for predicting the maximum rolling force was developed for each category. To determine the optimal pre-tensioning force, two optimization formulations were proposed by minimizing the maximum equivalent stress and the absolute maximum displacement. The rolling forces predicted by the analytical models were found to be in good agreement with the numerical simulation results, with relative errors generally less than 10%. The predictive analytical models developed in this study capture well the complex deformation behavior that occurs during the roll bending process of steel plates, providing guidelines and predictions for industrial applications of this process. Full article

Background: This study aimed to optimize the coating process of Omega-3 fatty acid (OM3-FA) mini soft capsules containing the active pharmaceutical ingredients (APIs) pitavastatin and ezetimibe using near-infrared (NIR) spectroscopy for in-process monitoring. Cardiovascular disease treatments benefit from combining OM3-FA with lipid-lowering agents, but formulating such combinations in mini soft capsules presents challenges in maintaining stability and mechanical integrity. Methods: The coating process was developed using a pan coater and real-time NIR monitoring to ensure uniformity and quality. NIR spectroscopy enabled precise control of coating thickness, ensuring consistent drug distribution across the capsule surface. Results: The optimized process minimized OM3-FA oxidation and preserved the mechanical integrity of the capsules, as confirmed by texture analysis and in-vitro dissolution testing. This integration of NIR spectroscopy as a process analytical technology (PAT) significantly improved coating quality control, resulting in a stable and effective combination therapy for pitavastatin and ezetimibe in a mini soft capsule form. Conclusion: This approach offers an efficient solution for enhancing patient adherence in cardiovascular disease management. The application of NIR spectroscopy for real-time monitoring highlights its broader significance in pharmaceutical manufacturing, where it can serve as a versatile tool for ensuring product quality and optimizing production efficiency in diverse formulation processes. By incorporating NIR-based PAT, manufacturers can not only achieve product-specific improvements but also establish a foundation for continuous manufacturing and automated quality assurance systems, ultimately contributing to a more streamlined and robust production environment. Full article

Background/Objectives: Amorphization of an active pharmaceutical ingredient (API) can improve its dissolution and enhance bioavailability. Avoiding solvents for drug amorphization is beneficial due to environmental issues and potential solvent residues in the final product. Methods: Dry amorphization using a twin-screw extruder is presented in this paper. A blend of mesoporous silica particles and crystalline itraconazole was processed using a pharma-grade laboratory scale twin-screw extruder. The influence of different screw configurations and process parameters was tested. Particle size and shape are compared in scanning electron microscopy (SEM) images. Differential scanning calorimetry (DSC) and X-ray diffraction (XRD) are used to determine the residual amount of crystalline itraconazole in the final product. Results: An optimized screw configuration for the process was found which leads to more than 90% amorphous API when processed at room temperature. Full amorphization was reached at 70 °C. The specific mechanic energy (SME) introduced into the material during twin-screw processing is crucial for the dry amorphization. The higher the SME, the lower the residual amount of crystalline API. Two months after processing, however, recrystallization was observed by XRD. Conclusions: Dry processing using a twin-screw extruder is continuous, free of solvents and can be performed at low temperatures. This study proves the concept of twin-screw processing with mesoporous silica for dry amorphization of itraconazole. Full article

Background: High anticholinergic burden is associated with an increased risk of hospitalisation, readmission, and mortality in geriatric patients. The objectives were to develop an updated anticholinergic burden scale for drugs registered in Denmark and to estimate the burden at admission and discharge for hospitalised patients at the Geriatric Ward of Sønderjylland Hospital. Methods: The updated scale was developed through a systematic evaluation of the anticholinergic effect for all active pharmaceutical ingredients (APIs) listed on validated burden scales. APIs registered in 2020 and 2021 were evaluated separately for possible anticholinergic effect. The anticholinergic effect of each API was scored from 1 (low) to 3 (high). The scale was applied to medical records for patients hospitalised between October 2021 and March 2022. Results: The scale comprised 87 APIs with anticholinergic effect. We applied the scale on 196 patients aged (median [IQR]) 84 (78–89) years. Of these patients, 75 (38.3%) had a high burden (≥3) on admission. These patients had significantly higher drug use and higher risk of 30-day readmission but no relationship with length of stay. Overall, the anticholinergic burden was unchanged at discharge for 109 (55.1%) patients. Conclusion: An updated scale for estimation of the anticholinergic burden in geriatric patients was successfully developed, and a high burden among the admitted geriatric patients was found. Full article

The food industry increasingly depends on technological assets to improve the efficiency and accuracy of fruit processing and quality control. This article enhances the application of computer vision with collaborative robotics to create a non-destructive system. The system can automate the detection and handling of fruits, particularly tomatoes, reducing the reliance on manual labor and minimizing damage during processing. This system was developed with a Raspberry Pi 5 to capture images of the fruit using a PiCamera module 3. After detecting the object, a command is sent to a Universal Robotics UR3e robotic arm via Ethernet cable, using Python code that integrates company functions and functions developed specifically for this application. Four object detection models were developed using the TensorFlow Object Detection API, converted to TensorFlow Lite, to detect two types of fruit (tomatoes) using deep learning techniques. Each fruit had two versions of the models. The models obtained 67.54% mAP for four classes and 64.66% mAP for two classes, A rectangular work area was created for the robotic arm and computer vision to work together. After 640 manipulation tests, a reliable area of 262 × 250 mm was determined for operating the system. In fruit sorting facilities, this system can be employed to automatically classify fruits based on size, ripeness, and quality. This ensures consistent product standards and reduces waste by sorting fruits according to pre-defined criteria. The system’s ability to detect multiple fruit types with high accuracy enables it to integrate into existing workflows, thereby increasing productivity and profitability for food processing companies. Additionally, the non-destructive nature of this technology allows for the inspection of fruits without causing any damage, ensuring that only the highest-quality produce is selected for further processing. This application can enhance the speed and precision of quality control processes, leading to improved product quality and customer satisfaction. Full article