Application Of Technology Research Articles

Bacteriocins sourced from traditional fermented foods for ensuring food safety: the microbial guards.

Concerns about food safety have consistently driven the exploration of potent antimicrobials with probiotic origins. Identification of probiotic-derived bacteriocins as robust alternatives to antibiotics has gained traction following the COVID-19 pandemic. Additionally, the global market is witnessing an increasing preference for minimally processed food products free from chemical additives. Another contributing factor to the search for potent antimicrobials is the escalating number of infections caused by antibiotic-resistant bacteria and the need to mitigate the significant damage inflicted on the commensal human microbiota by broad-spectrum antibiotics. As an alternative bio-preservation strategy, there is substantial enthusiasm for the use of bacteriocins or starter cultures producing bacteriocins in preserving a variety of food items. This review specifically focuses on bacteriocins originating from lactic acid bacteria associated with fermented foods and explores their technological applications as nanobiotics. The food-grade antibiotic alternatives, whether utilized independently or in combination with other antimicrobials and administered directly or encapsulated, are anticipated to possess qualities of safety, stability and non-toxicity suitable for application in the food sector. © 2024 Society of Chemical Industry.

Application of genome tagging technology in elucidating the function of sperm-specific protein 411 (Ssp411).

The genome tagging project (GTP) plays a pivotal role in addressing a critical gap in the understanding of protein functions. Within this framework, we successfully generated a human influenza hemagglutinin-tagged sperm-specific protein 411 (HA-tagged Ssp411) mouse model. This model is instrumental in probing the expression and function of Ssp411. Our research revealed that Ssp411 is expressed in the round spermatids, elongating spermatids, elongated spermatids, and epididymal spermatozoa. The comprehensive examination of the distribution of Ssp411 in these germ cells offers new perspectives on its involvement in spermiogenesis. Nevertheless, rigorous further inquiry is imperative to elucidate the precise mechanistic underpinnings of these functions. Ssp411 is not detectable in metaphase II (MII) oocytes, zygotes, or 2-cell stage embryos, highlighting its intricate role in early embryonic development. These findings not only advance our understanding of the role of Ssp411 in reproductive physiology but also significantly contribute to the overarching goals of the GTP, fostering groundbreaking advancements in the fields of spermiogenesis and reproductive biology.

Does Scaling Up Pig Farming Promote Carbon Neutrality Among Pig Farmers?

Pig farming is a major source of carbon emissions in China's livestock industry, and the promotion and application of carbon neutral technologies dedicated to carbon reduction and sequestration is a key measure to promote the realization of carbon neutrality in pig farming industry. In the absence of a system for carbon emission reduction in agriculture, it is important to explore whether large-scale pig farming can spontaneously promote the application of carbon neutral technologies by pig farmers. Combined with the technical background of carbon neutrality in pig farming industry and using the field survey data of 468 pig farmers in Shandong Province, this paper empirically analyzed the effect of large-scale pig farming on the carbon neutral behavior of pig farmers. The findings are threefold. Firstly, because of the effect of economies of scale in the application of carbon neutral technologies, large-scale pig farming can promote the carbon neutral behavior of pig farmers. However, the effect of economies of scale in the application of carbon neutral technologies has a boundary, and the annual output of pigs should be maintained at 3000 to 4999. Secondly, among the effects of pig breeding scale on the application and its degree of composting project, state of planting and breeding cycle has no regulating effect. Thirdly, among the effects of pig breeding scale on the application and its degree of biogas engineering, both state of planting and breeding cycle and subsidy for construction of biogas digester have negative regulating effects. In this paper, the pig farming industry is taken as a typical example to reveal the endogenous incentive mechanism of promoting agricultural carbon emission reduction, which provides theoretical reference and experience enlightenment for the formulation of effective policies to promote agricultural carbon neutrality, which are different from industrial industries.

CRISPR-Cpf1 system and its applications in animal genome editing.

The clustered regularly interspaced short palindromic repeats (CRISPR) and their associated protein (Cas) system is a gene editing technology guided by RNA endonuclease. The CRISPR-Cas12a (also known as CRISPR-Cpf1) system is extensively utilized in genome editing research due to its accuracy and high efficiency. In this paper, we primarily focus on the application of CRISPR-Cpf1 technology in the construction of disease models and gene therapy. Firstly, the structure and mechanism of the CRISPR-Cas system are introduced. Secondly, the similarities and differences between CRISPR-Cpf1 and CRISPR-Cas9 technologies are compared. Thirdly, the main focus is on the application of the CRISPR-Cpf1 system in cell and animal genome editing. Finally, the challenges faced by CRISPR-Cpf1 technology and corresponding strategies are analyzed. Although CRISPR-Cpf1 technology has certain off-target effects, it can effectively and accurately edit cell and animal genomes, and has significant advantages in the preclinical research.

Non-destructive electroluminescence inspection for LED epitaxial wafers based on soft single-contact operation

Non-destructive and accurate inspection of gallium nitride light-emitting diode (GaN-LED) epitaxial wafers is important to GaN-LED technology. However, the conventional electroluminescence inspection, the photoluminescence inspection, and the automated optical inspection cannot fulfill the complex technical requirements. In this work, an inspection method and an operation system based on soft single-contact operation, namely, single-contact electroluminescence (SC-EL) inspection, are proposed. The key component of the SC-EL inspection system is a soft conductive probe with an optical fiber inside, and an AC voltage (70V pp , 100 kHz) is applied between the probe and the ITO electrode under the LED epitaxial wafer. The proposed SC-EL inspection can measure both the electrical and optical parameters of the LED epitaxial wafer at the same time, while not causing mechanical damage to the LED epitaxial wafer. Moreover, it is demonstrated that the SC-EL inspection has a higher electroluminescence wavelength accuracy than photoluminescence inspection. The results show that the non-uniformity of SC-EL inspection is 444.64%, which is much lower than that of photoluminescence inspection. In addition, the obtained electrical parameters from SC-EL can reflect the reverse leakage current (I s ) level of the LED epitaxial wafer. The proposed SC-EL inspection can ensure high inspection accuracy without causing damage to the LED epitaxial wafer, which holds promising application in LED technology.

The impact of zwitterionic surfactants on optode-based nanosensors via different fabrication approaches and sensing mechanisms.

In this work, we explored the impact of zwitterionic surfactants, sulfobetaine 16 (SB-16) and a PEG-phospholipid conjugate (DSPE-PEG), on nanosensor performance. We fabricated four sensors (for Na+, K+, Al3+, and O2) and examined how these surfactants influenced various aspects, including fabrication methods, sensing mechanisms, and the incorporation of nanomaterials. Our results highlighted SB-16's role in enhancing selectivity in ion-exchange sensors (Na+ and K+) while maintaining sensitivity akin to its PEG counterpart. The liquid-liquid extraction based sensors (Al3+) were unaffected by surfactant choice, while the O2 sensors that operate via collisional quenching exhibited reduced sensitivity with SB-16 when compared to its PEG-based counterpart. Additionally, the SB-16 sensors proved adaptable to different fabrication approaches (SESE - single emulsion solvent evaporation and FNP - flash nanoprecipitation), showcased good cell viability and maintained a functional lifetime of at least five days. Furthermore, via the use of quantum dots, we showed that it is possible to incorporate other nanomaterials into the sensing phase of SB-16 sensors. Future investigations could target enhancing the pH stability of zwitterionic surfactants to further advance their applicability in sensor technologies.

AI-generated text detection and classification based on BERT deep learning algorithm

With the rapid development and wide application of deep learning technology, AI-generated text detection plays an increasingly important role in various fields. In this study, we developed an efficient AI-generated text detection model based on the BERT algorithm, which provides new ideas and methods for solving related problems. In the data preprocessing stage, a series of steps were taken to process the text, including operations such as converting to lowercase, word splitting, removing stop words, stemming extraction, removing digits, and eliminating redundant spaces, to ensure data quality and accuracy. By dividing the dataset into a training set and a test set in the ratio of 60% and 40%, and observing the changes in the accuracy and loss values during the training process, we found that the model performed well during the training process. The accuracy increases steadily from the initial 94.78% to 99.72%, while the loss value decreases from 0.261 to 0.021 and converges gradually, which indicates that the BERT model is able to detect AI-generated text with high accuracy and the prediction results are gradually approaching the real classification results. Further analysis of the results of the training and test sets reveals that in terms of loss value, the average loss of the training set is 0.0565, while the average loss of the test set is 0.0917, showing a slightly higher loss value. As for the accuracy, the average accuracy of the training set reaches 98.1%, while the average accuracy of the test set is 97.71%, which is not much different from each other, indicating that the model has good generalisation ability. In conclusion, the AI-generated text detection model based on the BERT algorithm proposed in this study shows high accuracy and stability in experiments, providing an effective solution for related fields. In the future, the model performance can be further optimised and its potential for application in a wider range of fields can be explored to promote the development and application of AI technology in the field of text detection.

Exploration of the application of artificial intelligence in modern agricultural production Take orchard management as an example

In the context of increasing global agricultural challenges, the application of artificial intelligence technology in the agricultural field is increasingly a trend, especially in the production of agricultural products, intelligent identification technology has shown the potential to significantly improve production efficiency and optimize yield and quality. By the mid-21st century, demand for food production is expected to reach 50 percent, and there will be enormous pressure to achieve this goal with traditional agricultural technologies, which could be achieved through the application of artificial intelligence. The application of artificial intelligence technology in modern agricultural production will be analyzed in detail in this paper, the guidance of future research fields will be proposed, and the existing challenges and technical problems will be identified and discussed in order to promote the deepening and wide application of intelligent agriculture. This paper specifically discusses examples of applications in orchard management, pest detection, and automated harvesting and summarizes the effectiveness and obstacles of these techniques.

Enhancement Petrophysical Properties of Carbonate Reservoirs Using Plasma Channel Technology, Case Study: Yamama Formation, South of Iraq

Carbonate reservoirs are significant hydrocarbon productions, but their complex and heterogeneous nature often poses challenges in maximizing oil and gas recovery. Traditional methods for enhancing petrophysical properties have shown limited success in carbonate formations. However, new advancements in Plasma Channel Technology have shown promising results in improving the reservoir's petrophysical properties. The application of Plasma Channel Technology is considered as a novel approach to enhance the petrophysical properties of carbonate reservoirs. Plasma channel technology involves the controlled application of high-voltage electrical discharges to create conductive channels (new porosity) within the reservoir units. These channels serve as preferential pathways for fluid flow (permeability) and facilitate enhanced oil and gas recovery. The main objective of this study is to show the key aspects of the enhancement of the Yamama reservoir properties using Plasma Channel Technology minimizes the need for chemical additives to reduce operational costs and environmental impact. Therefore, Yamama Formation is studied in two oil wells Snd-1 and Snd-2, six core samples from reservoir units within the Yamama succession were studied to determine porosity using a Scanning Electron Microscope which provides valuable insights into their internal structure and porosity characteristics in these samples. A comparison had been done between porosities within rock samples before and after application of Plasma channel technology, where the porosity was ranging from micropores (10 nm) to mesopores (10-50 nm) in some samples before voltage electrical discharges, while it reached mesopores (20-50 nm) to macropores (more than 500 nm) after voltage electrical discharges.These pores have been increased in size if there was higher voltage which means enhancement in the petrophysical properties in the reservoir.

Tingkat Kepuasan Petani Terhadap Penggunaan Pupuk Hayati Boc-Srf dan Pupuk Hayati X dalam Meningkatkan Produksi Cabai Merah

Red chilli is a plant that has always been the subject of discussion because it can cause inflation in Indonesia. Even though demand is high, the lack of goods causes prices to soar. One effort to increase red chilli productivity is the use of fertilizer. The Center for the Study and Application of Technology (BPPT) has produced superior biological fertilizers, namely BOC-SRF Biological Fertilizers, which farmers use on red chilli plants. Farmers as consumers certainly feel satisfied or not after using this fertilizer or other biological fertilizers; therefore, it is necessary to research the level of satisfaction of farmers regarding the use of BOC-SRF Biofertilizer and Biological Fertilizer Hayati BOC-SRF with Hayati X in Sukatani Village, Pacet District, Cianjur Regency with 40 farmers as respondents. The analysis method uses the Customers Satisfaction Index (CSI), Important Performance Analysis (IPA) and Diagonal Analysis (Suharjo Split). As a result, farmers were very satisfied with the performance of BOC-SRF biofertilizer (CSI: 81%), while farmers using X biofertilizer were satisfied with an index value of 74.4%. The attribute that is the main priority but performs poorly when using BOC-SRF biological fertilizer is the "productive period". In contrast, for X biological fertilizer, it is the "productive period and production yield". The analysis of service efficiency levels showed that over-service occurred in the HPT resistance attribute of BOC-SRF biofertilizer, while other attributes experienced under-service. At the level of biofertilizer service efficiency X, all attributes are experienced under service.

Machine Learning-Based Prediction Approach for COVID-19 Detection via Chest X-Ray Scans

Objectives: In medical research, the application of AI technologies has become a desirable approach for disease diagnosis due to their ability to increase diagnosis speed and accuracy. One major application of these technologies is in diagnosing diseases using medical imagery data such as X-ray and CT scan images. The objective of this study is to implement a COVID-19 diagnosis approach based on ML and compare it with laboratory testing approaches. Methods: The study utilizes the VGG16 architecture to extract features from X-ray and CT scan images, which are then used to build an ANN classifier to predict images as either COVID-positive or normal. Real-world datasets of varying difficulty were employed to evaluate the system's performance under different stressors, while comparative analysis with other classifiers was conducted to assess the model's improvement in accuracy and time complexity. Findings: The proposed architecture correctly classified 98.41% of the testing images. Among the images from infected patients in the testing set, 97.54% were accurately identified, while 98.72% of the normal cases were correctly classified. The model demonstrated good precision, with 96.49% of predicted COVID cases truly being from COVID-19 patients. Additionally, the F1 score for the model is 98.41%, indicating a good balance in predicting both categories. Showcasing its ability to handle multiple predictions quickly. The model's execution time for classifying a single case was computed as 0.0039 seconds, showcasing its ability to handle multiple predictions quickly. The proposed approach exhibits superior performance on datasets with balanced outcomes, surpassing the accuracy of SVM, DT, Logistic Regression, and Random Forest models. The approach outperforms laboratory-based testing approaches in terms of accuracy cost and assay times. Novelty: This study not only implements a robust ML-based approach for COVID-19 diagnosis but also provides a unique comparative analysis with traditional laboratory-based testing methods, such as Reverse Transcription Polymerase Chain Reaction (RT-PCR), reported in other literature. This dual focus on ML performance and its comparison with established testing methods highlights the practical advantages of ML in terms of diagnosis speed, accuracy, and cost-efficiency, offering a comprehensive perspective not commonly addressed in existing research. Keywords: COVID-19, Machine Learning techniques, F1 score, Classifier, Specificity, Accuracy, Sensitivity, Precision

Properties, characterization and biomedical applications of polyvinylidene fluoride (PVDF): a review

AbstractPolyvinylidene fluoride (PVDF) boosted its technological applications because of its piezoelectric and pyroelectric properties, together with mechanical, chemical, and thermal resistance. In this review, we address the PVDF chemical structure and its synthesis procedure, also describing the occurrence of polymorphism (alpha (α), beta (β), gamma (γ), and delta (δ) of this semicrystalline polymer, and the processes involved in phase transformation that govern the adaptability of the PVDF crystalline phases and properties for several industrial uses. It can be used as a component of implants and sutures, as engineering plastic to play a structural role in aggressive environments, acting in the field of process control as a component of transducers and sensors, as well as in the synthesis of separation membranes, fuel cells, and a long list of new applications that include an important role in robotics. The main physicochemical techniques used for PVDF characterization, i.e., differential scanning calorimetry, Fourier transform infrared spectroscopy, X-ray diffraction, and scanning electron microscopy, that highlight its unique properties, are also discussed, showing the versatility of PVDF for a range of biomedical and pharmaceutical applications.

Practice and Research on Blockchain Technology Empowering Digital Art Economy

Blockchain technology, with its characteristics of decentralization, security, transparency, and traceability, brings new opportunities and challenges to the development of the digital economy and the digital art economy. Based on existing literature research, this paper deeply analyzes the core concepts and working principles of blockchain technology and explains its dual role in the digital economy and digital art economy from a theoretical perspective. On this basis, this paper conducts an empirical study on the application of blockchain technology in the digital economy and digital art economy through typical cases and explores future development trends of blockchain technology in this field.

Application of optimizing advanced encryption standard encryption algorithm in secure communication of vehicle controller area network bus

Introduction: As the main means of information exchange within vehicles, the safety of the controller area network bus directly affects the safe operation of the vehicle and the safety of passengers’ lives and property.Methods: To enhance its secure communication function, this study utilizes advanced encryption standard algorithms and improves the S-box of the algorithm to solve problems such as extended processing time. A secure communication system for the local area network bus of the vehicle controller is designed based on optimized advanced encryption standard algorithms.Results and Discussion: The results showed that when the file size was 200MB, the encryption and decryption time spent by the research method was 469.8 s and 528.5 s, respectively, which are significantly lower than traditional methods. In the simulation results, under both non-encrypted and encrypted transmission, the information remained intact throughout the entire transmission process. This indicated that the optimization algorithm effectively reduced encryption processing time and system resource consumption while ensuring data confidentiality and integrity. The new system meets the security requirements of the local area network bus of vehicle-mounted controllers.Conclusion: This study not only enhances the security of in-vehicle networks but also promotes the application and development of related encryption technologies in the field of vehicle networking. It provides strong technical support for the further development of vehicle networking and the safe operation of intelligent vehicles.

South Africa’s food system: An industry perspective on past, present and future applications of science and technology

The South African food system is facing severe challenges as increasing hunger, rising food costs, lack of dietary diversity, child stunting, foodborne illnesses, food waste and an obesity epidemic coupled with malnutrition are observed. In this study, we aimed to establish the application of science and technology advances in the food and beverage industry in South Africa in response to food consumption pattern changes since 1994 and how they could be used to address food security challenges. We found that food consumption shifts have been towards sugar-sweetened beverages, processed and packaged food, animal-source foods and added caloric sweeteners, and away from vegetables. These dietary shifts are concerning as they relate to public health. Most commitments to improve the nutritional status of South Africans have been limited to corporate social investment strategies and should be extended into core business strategies. Furthermore, although the South African food and beverage industry has kept pace with developments in food manufacturing practices, there has been little experimentation with non-commercial novel technologies. The expert survey revealed that indigenous African crops and food waste recovery are the two most promising emerging food sources that could be available to South Africans in the shortest time frame. South Africa has many enabling drivers to become a global leader in food technology advances. However, many barriers need to be overcome for industry, academia and government to collaborate to advance novel food science and technologies to reach commercialisation.

TeaPoseNet: A deep neural network for tea leaf pose recognition

The estimation of tea leaf pose is an emerging research topic. Recognising the morphological features of tea leaves can help accurately categorise, grade, and determine their level of maturity. Therefore, this study proposes a deep neural network, TeaPoseNet, to estimate tea leaf poses. The algorithm was trained and validated using a dataset of one-bud-one-leaf images of Yinghong No.9 tea leaves and was compared with four other pose estimation networks. At the same time, the contribution of TKS_NMS to the algorithm was validated through ablation experiments. The results indicate that TKS_NMS improved the EPE accuracy of pose recognition by 16.33 %. More specifically, the algorithm achieved a good overall performance, with PCK, AUC, EPE, and NME reaching 0.9800, 0.8147, 9.0955, and 0.0644, respectively. The average running speed for detecting the pose of a single tea leaf image was 40.01 ms. To the best of our knowledge, this is the first application of pose estimation technology to the detection and analysis of Yinghong No.9 tea leaves. The results show that the proposed algorithm can effectively estimate the pose of tea leaves, thus providing a reference for subsequent tea research.

Application Analysis of Artificial Intelligence Virtual Reality Technology in Fitness Training Teaching

The application of artificial intelligence virtual reality (VR) technology in fitness teaching and training is becoming increasingly widespread, and its significant advantages provide athletes with a more personalized, efficient and safe training experience. By simulating various fitness scenes, virtual reality technology enables athletes to train immersively, not only improving the fun of training, but also enhancing the training effect. At the same time, the introduction of artificial intelligence technology enables real-time monitoring and analysis of athlete performance during the training process, providing accurate data support for coaches and helping them develop more scientific training plans. This paper explores the application of virtual reality technology in physical education teaching and training, introduces the basic principles and characteristics of virtual reality and proposes several typical virtual reality application scenarios for the needs of physical education teaching and training. In football training, through virtual reality technology, athletes can conduct various technical and tactical exercises in simulated stadiums. At the same time, artificial intelligence systems can analyze the actions and decisions of athletes in real time, and provide feedback and suggestions. In track and field training, virtual reality technology can help athletes simulate competition scenes and improve their psychological adaptability during competitions. Through in-depth research, we have found that this technology provides athletes with a more personalized, efficient, and safe training experience. In football training, virtual reality technology can simulate real game scenes, allowing athletes to engage in various technical and tactical exercises in simulated sports stadiums. At the same time, artificial intelligence systems can analyze the actions and decisions of athletes in real time, provide accurate data support for coaches, and help them develop more targeted training plans.

Systematic Review on the Use of 3D-Printed Models for Planning, Training and Simulation in Vascular Surgery

The use of 3D-printed models in simulation-based training and planning for vascular surgery is gaining interest. This study aims to provide an overview of the current applications of 3D-printing technologies in vascular surgery. We performed a systematic review by searching four databases: PubMed, Web of Science, Scopus, and Cochrane Library (last search: 1 March 2024). We included studies considering the treatment of vascular stenotic/occlusive or aneurysmal diseases. We included papers that reported the outcome of applications of 3D-printed models, excluding case reports or very limited case series (≤5 printed models or tests/simulations). Finally, 22 studies were included and analyzed. Computed tomography angiography (CTA) was the primary diagnostic method used to obtain the images serving as the basis for generating the 3D-printed models. Processing the CTA data involved the use of medical imaging software; 3DSlicer (Brigham and Women’s Hospital, Harvard University, Boston, MA), ITK-Snap, and Mimics (Materialise NV, Leuven, Belgium) were the most frequently used. Autodesk Meshmixer (San Francisco, CA, USA) and 3-matic (Materialise NV, Leuven, Belgium) were the most frequently employed mesh-editing software during the post-processing phase. PolyJet™, fused deposition modeling (FDM), and stereolithography (SLA) were the most frequently employed 3D-printing technologies. Planning and training with 3D-printed models seem to enhance physicians’ confidence and performance levels by up to 40% and lead to a reduction in the procedure time and contrast volume usage to varying extents.

Application of 3D and 4D Printing in Electronics

Nowadays, additive manufacturing technologies have impacted different engineering sectors. Three- and four-dimensional printing techniques are increasingly used in soft and flexible electronics thanks to the possibility of working contemporarily with several materials on various substrates. The materials portfolio is wide, as well as printing processes. Shape memory polymers, together with composites, have gained great success in the electronic field and are becoming increasingly popular for fabricating pH, temperature, humidity, and stress sensors that are integrated into wearable, stretchable, and flexible devices, as well as for the fabrication of communication devices, such as antennas. Here, we report an overview of the state of the art about the application of 4D printing technologies and smart materials in electronics.

A Human Feedback Strategy for Photoresponsive Molecules in Drug Delivery: Utilizing GPT-2 and Time-Dependent Density Functional Theory Calculations

Photoresponsive drug delivery stands as a pivotal frontier in smart drug administration, leveraging the non-invasive, stable, and finely tunable nature of light-triggered methodologies. The generative pre-trained transformer (GPT) has been employed to generate molecular structures. In our study, we harnessed GPT-2 on the QM7b dataset to refine a UV-GPT model with adapters, enabling the generation of molecules responsive to UV light excitation. Utilizing the Coulomb matrix as a molecular descriptor, we predicted the excitation wavelengths of these molecules. Furthermore, we validated the excited state properties through quantum chemical simulations. Based on the results of these calculations, we summarized some tips for chemical structures and integrated them into the alignment of large-scale language models within the reinforcement learning from human feedback (RLHF) framework. The synergy of these findings underscores the successful application of GPT technology in this critical domain.