Hasil untuk "Technology (General)"

Yao Zou, Mian Zhong, Shichen Li et al.

Flexible wearable strain sensors based on laser-induced graphene (LIG) have attracted significant interest due to their simple preparation process, three-dimensional porous structure, excellent electromechanical characteristics, and remarkable mechanical robustness. In this study, we demonstrated that LIG with various defects could be prepared on the surface of polyimide (PI) film, patterned in a single step by adjusting the scanning speed while maintaining a constant laser power of 12.4 W, and subjected to two repeated scans under ambient air conditions. The results indicated that LIG produced at a scanning speed of 70 mm/s exhibited an obvious stacked honeycomb micropore structure, and the flexible strain sensor fabricated with this material demonstrated stable resistance. The sensor exhibited high sensitivity within a low strain range of 0.4–8.0%, with the gauge factor (GF) reaching 107.8. The sensor demonstrated excellent stability and repeatable response at a strain of 2% after approximately 1000 repetitions. The flexible wearable LIG-based sensor with a serpentine bending structure could be used to detect various physiological signals, including pulse, finger bending, back of the hand relaxation and gripping, blinking eyes, smiling, drinking water, and speaking. The results of this study may serve as a reference for future applications in health monitoring, medical rehabilitation, and human–computer interactions.

Ruizhi Chu, Jie Zhang, Shuo Li et al.

Feng-Chi Chang, Chia-Jen Wu, Kan Ling et al.

Dwi Ratna Puspita Sari, Sirli Fahriah, Kurnianingsih et al.

Gold is one of the most favored investment instruments due to its stability and its ability to preserve value against inflation. However, its price movements are volatile and influenced by various global economic factors, currency exchange rates, and geopolitical conditions, making gold price forecasting a significant challenge. This study aims to develop a gold price forecasting system using the Long Short-Term Memory (LSTM) algorithm, a variant of the Recurrent Neural Network (RNN) that excels in processing time-series data. The dataset consists of historical daily gold buying and selling prices from 2015 to 2025, collected from Yahoo Finance, Logam Mulia, and the official website of Bank Indonesia. The modeling process follows the CRISP-DM methodology, which includes business understanding, data preparation and exploration, modeling, and evaluation stages. Time Series Cross Validation (TSCV) is used to validate the model. LSTM performance is compared with other models such as GRU, CNN-1D, and Simple RNN to identify the best-performing architecture. Evaluation results indicate that LSTM achieved the highest performance with an R² score of 0.99 for selling prices and 0.98 for buying prices on the final test dataset. The system is deployed online, making it accessible in real-time. This research is expected to assist investors, financial analysts, and the general public in making smarter investment decisions based on valid historical data and advanced forecasting technology.

Marcos G. Alberti, Alejandro Enfedaque, Duarte M. V. Faria et al.

Material optimization was one of the challenges for achieving cost-competitive solutions when concrete was introduced in construction, leading to new structural shapes for both civil works and buildings. As concrete construction became dominant, saving material was given less significance, and the selection of the structural typology was mostly influenced by construction or architectural considerations. Simple and non-time-consuming methods for building thus arose as the dominant criteria for design, and this led to the construction of less efficient structures. Currently, the awareness of the environmental footprint in concrete construction has brought the focus again to the topic of structural efficiency and material optimization. In addition, knowledge of material technology is pushing the use of cements and binders with lower environmental impact. Within this framework, Fiber-Reinforced Concrete (FRC) has been identified as a promising evolution of ordinary concrete construction. In this paper, a discussion is presented on the structural properties required for efficient design, focusing on the toughness and deformation capacity of the material. By means of several examples, the benefits and potential application of limit analysis to design at the Ultimate Limit State with FRC are shown. On this basis, the environmental impact of a tailored mix design and structural typology is investigated for the case of slabs in buildings, showing the significant impact that might be expected (potentially reducing CO₂-eq emissions to half or even less in slabs when compared to ordinary solutions).

Zhimin Han, Hongyu Zhang, Chang Wang et al.

With the development and advancements of modern industry, the application of enhanced tubes is becoming more and more extensive in various engineering domains and technological applications. When water is used as the working fluid for thermal energy utilization, there is a large chance of accumulating a large amount of fouling on the surface of the enhanced tubes. This paper establishes a mathematical model of the local fouling deposition of calcium carbonate in enhanced tubes. The model is used to simulate and compare the local fouling characteristics of CaCO3 in different tubes. In addition, a study is conducted on the effects of different inlet flow rates, water temperature, and calcium carbonate concentrations on the local fouling resistance of bellows tubes. The results showed that the local fouling resistance (average) and local fouling resistance of enhanced tubes is smaller than that of circular tubes. Additionally, the bellows tubes yielded the best scale inhibition effect. The obtained local fouling resistance is also found to change periodically along the length of the tube. The local fouling resistance decrease with an increase in the inlet flow rate and wall temperature, and increases with an increase in the calcium carbonate solution concentration.

Hajar Ebrahimi, Fahimeh Babalhavaeji, Dariush Matlabi et al.

Objective: This study aims to investigate the factors influencing professional publishing and propose a model for professional book publishing in Iran. Method: The research was conducted using a survey method with a researcher-developed questionnaire. The statistical population consisted of professional publishing managers in the country, totaling 581 publishers, from which 231 were selected as a sample based on Cochran's formula. Ultimately, 211 questionnaires were completed and analyzed. Data processing was carried out using SPSS software, employing exploratory the factor analysis and multiple regression test. Findings: Based on exploratory factor analysis, nine factors have been identified as influential in professional publishing: the economics of publishing, the supply and display of publishing products, government support and backing, adherence to copyright, publishing evaluation and auditing, advertising, marketing and branding, publishing management, and the creation of publishing content. Additionally, five factors have been recognized as dimensions of professional publishing, which include technical elements, cultural and literary circles and centers, authors and audiences, electronic systems, and distribution and marketing elements. Ultimately, in the regression model, five independent variables were included in the equation due to their significance level being below .05. Conclusion: The findings of this research contribute to enhancing the awareness and understanding of audiences regarding publishing processes. They also assist publishers and industry managers in recognizing successful trends and existing challenges within the field, as well as in formulating supportive policies and strategies for publishing by relevant authorities.

Bareqa Salah, Diana Shahin, Momen Sarhan et al.

Pengsheng Hui, Yu Jiang, Jie Wang et al.

BackgroundParkinson’s disease (PD) is a prevalent neurodegenerative disorder that significantly benefits from early diagnosis for effective disease management and intervention. Despite advancements in medical technology, there remains a critical gap in the early and non-invasive detection of PD. Current diagnostic methods are often invasive, expensive, or late in identifying the disease, leading to missed opportunities for early intervention.ObjectiveThe goal of this study is to explore the efficiency and accuracy of combining fNIRS technology with machine learning algorithms in diagnosing early-stage PD patients and to evaluate the feasibility of this approach in clinical practice.MethodsUsing an ETG-4000 type near-infrared brain function imaging instrument, data was collected from 120 PD patients and 60 healthy controls. This cross-sectional study employed a multi-channel mode to monitor cerebral blood oxygen changes. The collected data were processed using a general linear model and β values were extracted. Subsequently, four types of machine learning models were developed for analysis: Support vector machine (SVM), K-nearest neighbors (K-NN), random forest (RF), and logistic regression (LR). Additionally, SHapley Additive exPlanations (SHAP) technology was applied to enhance model interpretability.ResultsThe SVM model demonstrated higher accuracy in differentiating between PD patients and control group (accuracy of 85%, f1 score of 0.85, and an area under the ROC curve of 0.95). SHAP analysis identified the four most contributory channels (CH) as CH01, CH04, CH05, and CH08.ConclusionThe model based on the SVM algorithm exhibited good diagnostic performance in the early detection of PD patients. Future early diagnosis of PD should focus on the Frontopolar Cortex (FPC) region.

Aykut Şener, Muharrem Kaya

Bu araştırma, Isparta koşullarında 11 kırmızı mercimek çeşidinin (Çiftçi, Özbek, Kafkas, Tigris, Fırat-87, Evirgen, Seyran-96, Çağıl, Altıntoprak, Şakar ve Kırmızı-51) verim ve bazı verim öğeleri bakımından değerlendirilmesi amacıyla yürütülmüştür. Çalışma 2018-2021 yılları arasında iki yıl süreyle, tesadüf bloklarında bölünmüş parseller deneme desenine göre 3 tekrarlamalı olarak kurulmuş, varyans analizleri yapılmış, önemlilik olduğu belirlenen faktörlerde elde edilen ortalamalar Tukey testi ile karşılaştırılmıştır. İki yıllık ortalamalara göre bitki boyu 21,75-28,01 cm, ilk bakla yüksekliği 10,21-15,91 cm, bitkide bakla sayısı 31,31-49,48 adet, tane sayısı 36,06-64,76 adet, bitki verimi 2,41-5,17 g, tane verimi 139,61-257,73 kg da-1, bin tane ağırlığı 29,50-38,32 g, hasat indeksi %30,03-49,31, su alma kapasitesi 0,019-0,033 g tane-1 ve protein oranı %26,83-29,75 arasında değişim göstermiştir. Sonuç olarak tane verimi ve verime katkısı yüksek özellikler bakımından Evirgen, Çağıl ve Seyran-96 çeşitlerinin ön plana çıktığı belirlenmiştir.

Yingqiang Gao, Lukas Fischer, Alexa Lintner et al.

Audio descriptions (ADs) function as acoustic commentaries designed to assist blind persons and persons with visual impairments in accessing digital media content on television and in movies, among other settings. As an accessibility service typically provided by trained AD professionals, the generation of ADs demands significant human effort, making the process both time-consuming and costly. Recent advancements in natural language processing (NLP) and computer vision (CV), particularly in large language models (LLMs) and vision-language models (VLMs), have allowed for getting a step closer to automatic AD generation. This paper reviews the technologies pertinent to AD generation in the era of LLMs and VLMs: we discuss how state-of-the-art NLP and CV technologies can be applied to generate ADs and identify essential research directions for the future.

Kyrylo Malakhov, Vadislav Kaverinskiy, Liliia Ivanova et al.

The monograph summarizes and analyzes the current state of scientific research in the field of interactive artificial intelligence systems, text generation systems, diagnostics of the competitiveness of specialists, in the areas of correct color rendering in image formation, informatization of the work of graduate students, accessible technology for creating three-dimensional 3D models. The monograph will be useful both to specialists and employees of companies working in the IT field, as well as teachers, masters, students and graduate students of higher educational institutions, as well as anyone interested in issues related to information technology. The monograph was compiled based on the results of the 16-th international scientific and practical conference Information technologies and automation - 2023, which took place in October 2023 at Odessa National University of Technology.

Lewis E. MacKenzie, Peter Kirton

Superfluorescence (SF) in lanthanide doped upconversion nanoparticles (UCNPs) is a room-temperature quantum phenomenon, first discovered in 2022. In a SF process, the many emissive lanthanide ions within a single UCNP are coherently coupled by an ultra-short (ns or fs) high-power excitation laser pulse. This leads to a superposition of excited emissive states which decrease the emissive lifetime of the UCNP by a factor proportional to the square of the number of lanthanide ions which are coherently coupled. This results in a dramatic decrease in UCNP emission lifetime from the microsecond regime to the nanosecond regime. Thus SF offers a tantalizing prospect to achieving superior upconversion photon flux in upconversion materials, with potential applications such as imaging and sensing. This perspective article contextualizes how SF-UCNPs can be regarded as a second generation quantum technology, and notes several challenges, opportunities, and open questions for the development of SF-UCNPs.

S. Jacobsen, E. Iversen, D. Knutti et al.

Xue-Li Wang, Shu-Yi Qiu, Shao-Qi Zhou et al.

Phosphorus-solubilizing microorganisms release organic acids that can chelate mineral ions or reduce the pH to solubilize insoluble phosphates for use by plants; it is important to study potential phosphorus-solubilizing microorganisms for use in agriculture. In this study, PSF7 was isolated from the soil of the Wengfu Phosphorus Tailings Dump in Fuquan City, Guizhou Province, China. PSF7 was identified as Paecilomyces lilacinus, based on morphological characterization and ITS sequencing analysis. The relationship between the phosphorus-solubilizing capacity and pH variation of PSF7 under liquid fermentation was studied. The results showed that there was a significant negative correlation (−0.784) between the soluble phosphorus content of PSF7 and the pH value. When PSF7 was placed under low phosphorus stress, eight organic acids were determined from fermentation broth using HPLC, of which tartaric acid and formic acid were the main organic acids. Different optimization parameters of medium components were analyzed using response surface methodology. The optimized medium components were 23.50 g/L sucrose, 1.64 g/L ammonium sulfate and soybean residue, 1.07 g/L inorganic salts, and 9.16 g/L tricalcium phosphate, with a predicted soluble phosphorus content of 123.89 mg/L. Under the optimum medium composition, the actual phosphorus-solubilizing content of PSF7 reached 122.17 mg/L. Moreover, scanning electron microscopy analysis of the sample was carried out to characterize the phosphate-solubilizing efficiency of PSF7 on mineral phosphate. The results provide useful information for the future application of PSF7 as a biological fertilizer.

Dae-yeon Lee

This study explored the significance of promoting college general physical education in the era of COVID-19. Due to the impact of the COVID-19 pandemic, college general physical education classes and various activities have faced numerous challenges and changes. It has become necessary to approach the intrinsic value and importance of college general physical education from a new perspective, considering the disrupted opportunities for students to engage in diverse interactions and experiences. This situation presents an opportunity to develop a new educational paradigm. To address this, previous studies were analyzed and synthesized, leading to the proposal of enhancing college physical education through the active utilization of digital technologies such as new teaching methods, remote learning tools, AR, AI, VR, and big data, which are familiar to students with positive digital experiences. Additionally, an emphasis was placed on the development of new college general physical education programs that apply specialized digital platforms, content, and devices.General physical education, which is based on bodily movement more than any other subject, is highly suitable for integration with various digital devices. Therefore, through various attempts and efforts, it is expected that positive synergies can be created, and that the utilization of such digital technologies can enhance student participation and learning outcomes, thereby elevating the value of physical education as a part of general education.

Eketa Devi, Ranjitha Gracy T. Kalaivendan, Gunaseelan Eazhumalai et al.

The present study focused to modify the functionality of arrowroot starch (ARS) by a novel atmospheric pressure pin-to-plate cold plasma. The top electrode consists of multiple pins arranged in such a way to shower corona discharge of electrons to provide effective modification. Arrowroot starch (10 g) was exposed to the cold plasma processed at three input voltages (190, 210, 230 V) for 5–15 min and studied for the changes in intrinsic viscosity average molecular weight (MWv), powder flow properties (bulk and tapped density, Hausner's ratio, Carr's index), functional (water and oil binding capacity, pH, gel hydration, turbidity), rheological (pasting and steady shear flow), thermal (DSC) and structural (FTIR, XRD, SEM) properties. With cold plasma treatment, MWv of the ARS was increased evincing the cross-linking phenomenon which has also shown in increase in peak viscosity of the starch pastes (4.33%–11.98%). The steady shear viscosity at 50 s−1 of the plasma-treated starch also increased remarkably (15.44%–223.83%) than the untreated. Inclusion of acidic and hydrophilic functionalities along with surface etching of starch observed under SEM have resulted in the pH reduction (from 5.41 ± 0.03 to 4.01 ± 0.01), Increase in water (22.5% rise in 230–15) and oil binding (8.46% in 230–15), swelling volume (50% increase) and solubility index (240% increase), reduction in paste turbidity. The increase in % of crystallinity in the plasma-treated arrowroot starch was associated with the increase in gelatinization enthalpy showing the thermal stability of plasma-indued crosslinking of arrowroot starch. This proves that cold plasma can be a potential green modification technology to produce clear, highly viscous, more hydrating, shear, and thermally stable starches.

Tan Yan Lin, Pang Ying Han, Ooi Shih Yin et al.

In the telecom industry, predicting customer churn is crucial for improving customer retention. In literature, the use of single classifiers is predominantly focused. Customer data is complex data due to class imbalance and contain multiple factors that exhibit nonlinear dependencies. In these complex scenarios, single classifiers may be unable to fully utilize the available information to capture the underlying interactions effectively. In contrast, ensemble learning that combines various base classifiers empowers a more thorough data analysis, leading to improved prediction performance. In this paper, a heterogeneous ensemble model is proposed for churn prediction in the telecom industry. The model involves exploratory data analysis, data pre-processing and data resampling to handle class imbalance. In this proposed model, multiple trained base classifiers with different characteristics are integrated through a stacking ensemble technique. Specifically, convolutional-based neural network, logistic regression, decision tree and Support Vector Machine (SVM) are considered as the base classifiers in this work. The proposed stacking ensemble model utilizes the unique strengths of each base classifier and leverages collective knowledge to improve prediction performance with a meta-learner. The efficacy of the proposed model is assessed on a real-world dataset, i.e., Cell2Cell. The empirical results demonstrate the superiority of the proposed model in churn prediction with 62.4% f1-score and 60.62% recall.

Wen-Yen Huang, Yaoh-Shiang Lin, Yu-Chun Lin et al.

The tumor microenvironment (TME) plays a crucial role in tumor progression. One of its key stromal components, cancer-associated fibroblasts (CAFs), may crosstalk with cancer cells by secreting certain cytokines or chemokines. However, which important mediator(s) are released by CAFs, and the underlying molecular mechanism, remain largely unknown. In the present study, we isolated patient-derived CAFs and normal fibroblasts (NFs). Using microarray analysis, we detected chemokine ligand 11 (CCL11) overexpression in CAFs compared to NFs. CCL11 administration promoted the migration and invasion of head and neck cancer (HNC) cells with enhanced cancer stem cell-like properties and induction of epithelial-to-mesenchymal transition. Furthermore, neutralization of CCL11 activity reversed the aggressive phenotype of CAF-induced cancer cells. Confocal microscopy showed colocalization of CCL11 and CC chemokine receptor 3 (CCR3) on HNC cells. Moreover, immunohistochemical analysis of clinical samples from 104 patients with HNC showed that expression of CCL11 and CCR3 were significantly correlated with poor overall survival (p = 0.003 and 0.044, respectively). Collectively, CCL11 expressed on CAFs promotes HNC invasiveness, and neutralization of CCL11 reverses this effect. We propose that the CCL11/CCR3 signaling circuit is a potential target for optimizing therapeutic strategies against HNC.

Haibing Xiao, Chenlin Du, Songling Zhang et al.

With a focus on the particle pollutants on the surface of silicon carbide crystal materials, this paper establishes a laser cleaning model for the fine particles found in silicon carbide crystal materials and proposes a new nanosecond green laser cleaning method assisted by airflow, which can effectively remove microparticles and the oxide layer on the substrate surface. Abaqus software and ANSYS Fluent software were used to simulate changes in the cleaning temperature field and the distribution of particles and dust during cleaning simulation, respectively. Based on the experimental research, and by using a nanosecond green laser to produce a wavelength of 532 nm, the direct irradiation of a nanosecond green laser on the surface of the element, and the particle contaminants on the surface of the silicon carbide material, optimized the process parameters to achieve a better cleaning efficiency. A green laser was used as a light source to conduct experiments to control the wind force of the gas chamber. The influence of the laser energy, scanning speed, and other parameters on the final cleaning efficiency was studied. The parameters of the silicon carbide before and after cleaning were characterized. The research shows that laser cleaning assisted with airflow is an efficient cleaning method that can be used to clean microparticles without damaging silicon carbide crystal substrate and to reduce the surface roughness of silicon carbide material from 1.63 to 0.34 μm, with an airflow of 0.2 Mpa.