Advanced Process Technology Research Articles

Phase analysis of biphoton joint spectra by interference between different SPDC sources

In spontaneous parametric down-conversion, the spectral correlations between the signal and the idler are expressed by the joint spectral amplitude (JSA) function. However, in the standard coincidence measurements, the phase information of the JSA is lost, and only the square of the absolute value of the JSA is recorded, thus preventing full characterization of the biphoton state. Here, we present an experimental technique to investigate the interference of biphoton joint spectral amplitudes, unlocking new avenues in quantum photonics research. Our method explores phase-dependent phenomena within entangled biphoton spectra. This is achieved by simultaneously pumping two structured nonlinear photonic crystals and observing their interference, which reveals previously inaccessible effects with direct intensity measurements. We demonstrate the versatility of our technique by analyzing two types of joint spectra: one exhibiting a two-lobe shape and the other a three-lobe shape. Additionally, we reconstruct the joint spectral amplitudes for both scenarios and observe good agreement with theoretical predictions. These results pave the way for developing advanced quantum communication and information processing technologies using biphoton spectra.

Adapting the GPT engine for proactive customer insight extraction in product development

Understanding the perceptions of consumers is widely recognized as a critical element in the innovation of new products. Traditional techniques used by companies to collect essential consumer insights have largely remained unchanged. Common practices like interviews and surveys have distinct limitations. Interviews may not always capture the precise needs of consumers due to communication barriers, and surveys tend to encourage incremental changes rather than radical innovations. Service industries face further complexity as they navigate customer feedback on the more intangible aspects of service quality. This study highlights the pioneering use of GPT-3.5 Turbo, a tool known for its exceptional ability to delve into the nuances of conversational context and process data in a chat-centric manner, thereby enhancing the extraction of Voice of the Customer (VoC). Its capability to handle large volumes of data in multiple languages leads to a more thorough and inclusive VoC analysis. The study links these technological advancements with Lean Six Sigma 4.0, suggesting that incorporating GPT-3.5 Turbo could significantly improve the customer-focused strategies in the current industrial landscape. This breakthrough in VoC analysis suggests possibilities for more perceptive, immediate data-driven approaches in customer service, and lays a stronger groundwork for decisions in product evolution and process enhancement. The paper concludes by urging further investigation to confirm these initial results and to explore the ethical aspects of employing such advanced natural language processing technologies.

Influences of friction stir processing on the microstructure and properties of TC4 titanium alloy by laser metal deposition

To enhance the properties of TC4 titanium alloy samples prepared by Laser Metal Deposition (LMD), while minimizing metallurgical defects and increasing relative density of the samples, the advanced Friction Stir Processing (FSP) technology was employed to conduct a refined secondary modification on the LMD-deposited TC4 titanium alloy samples. The comprehensive influences of FSP on the microstructure and properties of the as-deposited TC4 titanium alloy were emphatically investigated. The results reveal that the as-deposited TC4 titanium alloy exhibits a microstructure characterized primarily by columnar grains interspersed with a minor amount of equiaxed grains in the vertical section. After FSP treatment, the microstructure transforms into a new morphology where more refined equiaxed grains coexist with bimodal structures. Meanwhile, on the horizontal plane, the existing equiaxed grains are further refined, and the microstructure gradually transitions from the original Widmanstätten and basketweave structures to a more uniform laminar structure. Accordingly, the transformation of the microstructure leads to enhanced mechanical properties of the TC4 titanium alloy. Compared to the untreated as-deposited titanium alloy, the hardness of the FSP-processed samples significantly increases by 46.9 HV1 on the horizontal plane and 33.8 HV1 on the vertical plane. Additionally, the reduction in friction coefficient (from 0.47 to 0.41), a substantial decrease in wear mass (3.7 mg), and a slight improvement in the corrosion resistance of the titanium alloy collectively demonstrate the effectiveness of the FSP process in enhancing the performance of as-deposited TC4 titanium alloy.

Research on a New Model of Cultivating Creative Thinking in the Language Discipline Based on ChatGPT--Taking Project-based Learning of Senior Secondary Language as an Example

This paper This paper discusses a new model of cultivating creative thinking in the language discipline based on ChatGPT, aiming to provide new ideas and methods for language teaching reform. ChatGPT, an advanced natural language processing technology with powerful language comprehension and generation capabilities, provides new means and methods for language teaching. The study presents a case study of the teaching practice of the new model of creative thinking cultivation in language discipline based on ChatGPT through the casestudy method, which provides guidance for the further improvement and optimization of the new model. The model takes ChatGPT as the core, combines the characteristics of language teaching and students' needs, and designs teaching processes and activities suitable for cultivating students' creative thinking. The study emphasizes the importance of changing teaching concepts when using the Humanistic Version of the senior secondary language textbook, as well as incorporating practical language activities to improve students' core literacy through personalized participation and practice.

Extracting lung cancer staging descriptors from pathology reports: A generative language model approach

BackgroundIn oncology, electronic health records contain textual key information for the diagnosis, staging, and treatment planning of patients with cancer. However, text data processing requires a lot of time and effort, which limits the utilization of these data. Recent advances in natural language processing (NLP) technology, including large language models, can be applied to cancer research. Particularly, extracting the information required for the pathological stage from surgical pathology reports can be utilized to update cancer staging according to the latest cancer staging guidelines. ObjectivesThis study has two main objectives. The first objective is to evaluate the performance of extracting information from text-based surgical pathology reports and determining pathological stages based on the extracted information using fine-tuned generative language models (GLMs) for patients with lung cancer. The second objective is to determine the feasibility of utilizing relatively small GLMs for information extraction in a resource-constrained computing environment. MethodsLung cancer surgical pathology reports were collected from the Common Data Model database of Seoul National University Bundang Hospital (SNUBH), a tertiary hospital in Korea. We selected 42 descriptors necessary for tumor-node (TN) classification based on these reports and created a gold standard with validation by two clinical experts. The pathology reports and gold standard were used to generate prompt-response pairs for training and evaluating GLMs which then were used to extract information required for staging from pathology reports. ResultsWe evaluated the information extraction performance of six trained models as well as their performance in TN classification using the extracted information. The Deductive Mistral-7B model, which was pre-trained with the deductive dataset, showed the best performance overall, with an exact match ratio of 92.24% in the information extraction problem and an accuracy of 0.9876 (predicting T and N classification concurrently) in classification. ConclusionThis study demonstrated that training GLMs with deductive datasets can improve information extraction performance, and GLMs with a relatively small number of parameters at approximately seven billion can achieve high performance in this problem. The proposed GLM-based information extraction method is expected to be useful in clinical decision-making support, lung cancer staging and research.

Influence of Laser Power and Rotational Speed on the Surface Characteristics of Rotational Line Spot Nanosecond Laser Ablation of TC4 Titanium Alloy.

The TC4 titanium alloy is widely used in medical, aerospace, automotive, shipbuilding, and other fields due to its excellent comprehensive properties. As an advanced processing technology, laser processing can be used to improve the surface quality of TC4 titanium alloy. In the present research, a new type of rotational laser processing method was adopted, by using a beam shaper to modulate the Gaussian spot into a line spot, with uniform energy distribution. The effects of the laser power and rotational speed on the laser ablation surface of the TC4 titanium alloy were analyzed. The results reveal that the melting mechanism of the material surface gradually changes from surface over melt to surface shallow melt with the increase in the measurement radius and the surface roughness increases first, then decreases and, finally, tends to be stable. By changing the laser power, the surface roughness changes significantly with the variation in the measurement radius. Because low laser power cannot provide sufficient laser energy, the measurement radius corresponding to the surface roughness peak of the microcrack area is reduced. Under a laser power of 11 W, the surface roughness reaches its peak when the measurement radius is 600 μm, which is 200 μm lower than that of a laser power of 12 W, 13 W, and 14 W. By changing the rotational speed, the centrifugal force generated by the rotation of the specimen affects the distribution and re-condensation of the molten pool of the surface. As the rotational speed increases, the shallow pit around the pit is made shallower by the filling of the pit with molten material and the height of the bulge decreases, until it disappears. The surface oxygen content of the material increases first and then decreases with the increase in the measurement radius and gradually approaches the initial surface state. Compared with a traditional laser processing spot, the rotational line spot covers a larger processing area of 22.05 mm2. This work can be used as the research basis for rotational modulation laser polishing and has significance for guiding the innovative development of high-quality and high-efficiency laser processing technology.

A hybrid approach for vision-based structural displacement measurement using transforming model prediction and KLT

As civil infrastructures age, monitoring their health conditions has become increasingly critical. Dynamic displacement measurement is a prevalent method for assessing structural health. Traditional techniques, which often involve installing instruments and scaffolding, can interfere with the response of the structures. To address the challenge, non-contact measurement methods have been developed; however, these are typically costly and require expert operation. Advances in high-speed industrial cameras and image processing technology now enable vision-based displacement measurement. Despite their effectiveness, existing vision-based methods face significant limitations, including their inability to maintain tracking when line-of-sight is obstructed, sensitivity to lighting variations, and the need for manual intervention when feature points are lost. This study introduces a novel hybrid approach, termed ToMP-KLT, which combines the KLT tracker with a deep learning-based model. This method harnesses the precision of the KLT tracker under favorable conditions and the robustness of the deep learning-based tracker under adverse conditions. Its effectiveness is validated through simulation-based tests, lab-scale experiments, and field testing on Cheonsa Bridge, demonstrating substantial improvements in tracking robustness against occlusions and varying light conditions.

Revitalize Hearing Skills with Advanced Speech Processing Technology

Revitalizing listening skills with advanced sound processing technology is an interesting concept, especially in today's digital era where technology is advancing rapidly. Listening skill is the ability to understand, analyze, and respond well to information conveyed through sound, and this is a key skill in various contexts, such as interpersonal communication, business, education, and others. Here are some ways revitalizing hearing skills with advanced sound processing technologies can be accomplished, Introduction to Sound Processing Technologies, It is important to understand what advanced sound processing technologies are. This includes using speech processing algorithms, speech recognition techniques, and even artificial intelligence (AI) to optimize speech understanding and interpretation. Hearing Skills Training Speech processing technology can be used in hearing skills training. For example, an application that can record, analyze and provide feedback on a person's hearing ability. Such applications can be used in the context of business education or training. Automatic Transcription and Translation, Voice processing technology can be used to convert conversations in foreign languages ??into text or translate voice content in real time. This can help someone understand a foreign language or content displayed in a language they are not familiar with. Improved Business Communications, In business, voice processing technology can be used to record, analyze and provide feedback on presentations, meetings or telephone conversations. This can help improve business communication skills. Providing Better Customer Service Many companies use voice processing technology to improve their customer service. Advanced voice interactive systems (IVR) can be used to route customer calls more efficiently and provide necessary information. Sentiment Understanding Advanced voice processing technology can be used to analyze sentiment in conversations or customer reviews. This can help companies understand the views and feelings of their customers. Educational Applications Speech processing technology can also be used in education to create more interactive teaching materials.

Direct Ink Writing of Highly Conductive and Strongly Adhesive PEDOT:PSS-EP Coatings for Antistatic Applications

As the information age progresses, the electronics industry is evolving towards smaller and more sophisticated products. However, electrostatic potentials easily penetrate these components, causing damage. This underscores the urgent need for materials with superior antistatic properties to safeguard electronic devices from such damage. Antistatic coatings typically rely on polymers as the primary material, enhanced with conductive fillers and additives to improve performance. Despite significant progress, these coatings still face challenges related to advanced processing technologies and the integration of electrical and mechanical properties. Among various conductive fillers, the conducting polymer PEDOT:PSS stands out for its exceptional conductivity, environmental stability, and long cycle life. Additionally, epoxy resin (EP) is widely utilized in polymer coatings due to its strong adhesion to diverse substrates during curing. Here, we develop highly conductive and strongly adhesive PEDOT:PSS inks by combining PEDOT:PSS with EP using a composite engineering approach. These inks are used to fabricate PEDOT:PSS coatings by direct ink writing (DIW). We systematically evaluate the DIW of PEDOT:PSS-EP coatings, which show high electrical conductivity (ranging from 0.59 ± 0.07 to 41.50 ± 3.26 S cm−1), strong adhesion (ranging from 15.84 ± 2.18 to 99.3 ± 9.06 kPa), and robust mechanical strength (8 MPa). Additionally, we examine the surface morphology, wettability, and hardness of the coatings with varying PEDOT:PSS content. The resultant coatings demonstrate significant potential for applications in antistatic protection, electromagnetic shielding, and other flexible electronic technologies.

Drone Technology for Precision Agriculture: Advancements and Optimization Strategies

This paper delves into the development and optimization of an intelligent agricultural monitoring system that makes use of drone technology to enhance agricultural productivity and sustainability. With the world's population on the rise and the scarcity of arable land, precision agriculture becomes essential in guaranteeing food security. By harnessing state-of-the-art sensors and imaging technologies, drone technology offers a new and innovative approach to agricultural monitoring. Efficiently collecting vital data on vegetation indices, soil moisture, and crop health. This study seeks to create a state-of-the-art drone-based monitoring system that uses advanced machine learning algorithms and data processing technologies to analyze agricultural data with exceptional precision. It explores the most efficient methods for operating drones, including flight planning and data collection protocols, with the aim of creating a comprehensive agricultural monitoring system. This platform provides the convenience of automating monitoring processes, leading to lower labor costs, improved resource allocation, and a beneficial impact on agricultural modernization. The article delves into the effects of the latest developments in drone technology and the subsequent decrease in costs on the agriculture industry worldwide. It highlights the ways in which these advancements are transforming conventional farming methods.

High-efficiency removal of microcystis aeruginosa using Z-scheme AgBr/NH2-MIL-125(Ti) photocatalyst with superior visible-light absorption: Performance insights and mechanisms

Algal blooms have become a widespread concern for drinking water production, threatening ecosystems and human health. Photocatalysis, a promising advanced oxidation process (AOP) technology for wastewater treatment, is considered a potential measure for in situ remediation of algal blooms. However, conventional photocatalysts often suffer from limited visible-light response and rapid recombination of photogenerated electron-hole pairs. In this study, we prepared a Z-scheme AgBr/NH2-MIL-125(Ti) composite with excellent visible light absorption performance using co-precipitation to efficiently inactivate Microcystis aeruginosa. The degradation efficiency of AgBr/NH2-MIL-125(Ti) for chlorophyll a was 98.7 % after 180 min of visible light irradiation, significantly surpassing the degradation rate efficiency of AgBr and NH2-MIL-125(Ti) by factors of 3.20 and 36.75, respectively. Moreover, the removal rate was maintained at 91.1 % even after five times of repeated use. The experimental results indicated that superoxide radicals (•O2-) were the dominant reactive oxygen species involved. The photocatalytic reaction altered the morphology and surface charge of algal cells, inhibited their metabolism, and disrupted their photosynthetic and antioxidant systems. In conclusion, this study presents a promising material for the application of photocatalytic technology in algal bloom remediation.

¬Survey on Organizational Chat Conversation Analysis: Exploring Dialogue Summarization from a Knowledge Discovery Perspective

With the latest advances in natural language processing technologies, multi-participant conversation summarization features are now embedded in the most widely used collaboration platforms offered by industry leaders such as Microsoft, Google, and Zoom. This allows employees to streamline their work and increase efficiency by summarizing long chat threads. In this study, an attempt has been made to perceive summarized chat conversations as a tool for knowledge discovery and reusable information extraction within an organization in general or during projects. To this end, recent scientific articles have been reviewed to identify the most effective techniques and approaches for summarizing chat threads and conversations alongside the challenges and peculiarities of collaborative text-based communication. In addition, significant attention has been paid to the further utilization of the extracted information to represent the knowledge for further reuse.

Special Issue “Recent Advances in Processing Technologies for Substance Extraction, Separation, and Enrichment”

Special Issue “Recent Advances in Processing Technologies for Substance Extraction, Separation, and Enrichment”

Wireless Body Sensor Networks for Real-Time Healthcare Monitoring: A Cost-Effective and Energy-Efficient Approach

Background: The continuous and accurate monitoring of physiological signals is critical for timely medical interventions, particularly in the context of chronic disease management and elderly care. The advent of Wireless Body Sensor Networks (WBSN) and Patient Health Monitoring Systems (PHMS) promises to revolutionize healthcare by integrating real-time data collection with advanced processing and communication technologies, enabling proactive medical responses through the Internet of Things (IoT). Methods: In this study, a WBSN-PHMS framework is proposed, leveraging a network of biomedical sensors to continuously monitor vital signs such as temperature and heart rate. The system includes a gateway node that wirelessly transmits sensor data to a cloud-based storage and processing system. Physiological data are processed in real-time using advanced algorithms to detect anomalies and trigger alerts. The health status is modeled using a function g(v,w(v),∅−1(v(w))−∂i(v,∂))g(v, w(v), \varnothing^{-1}(v(w)) - \partial_i(v, \partial))g(v,w(v),∅−1(v(w))−∂i?(v,∂)), which integrates patient-specific factors and real-time physiological states. The processed data are communicated efficiently through IoT platforms, ensuring that healthcare professionals receive timely alerts via mobile applications and specialized dashboards. Results: The WBSN-PHMS demonstrated high accuracy in detecting abnormal temperature and heart rate patterns, achieving detection rates of 98.61% and 99.43%, respectively. Additionally, the system improved overall patient health monitoring by 98.42%, offering a significant advancement in patient safety and healthcare efficiency. The cost-effectiveness of the system was established with a 96.21% improvement, while energy consumption was reduced by 23%, underscoring the sustainability of the technology. Conclusion: The WBSN-PHMS proves to be a reliable and efficient healthcare monitoring system that enhances patient outcomes through real-time data analysis and rapid medical responses. The system's ability to continuously monitor and analyze physiological signals in a non-intrusive manner positions it as a vital tool in modern medical practice, with the potential to revolutionize remote patient care and monitoring.

Neural Network Signal Integration from Thermogas-Dynamic Parameter Sensors for Helicopters Turboshaft Engines at Flight Operation Conditions.

The article's main provisions are the development and application of a neural network method for helicopter turboshaft engine thermogas-dynamic parameter integrating signals. This allows you to effectively correct sensor data in real time, ensuring high accuracy and reliability of readings. A neural network has been developed that integrates closed loops for the helicopter turboshaft engine parameters, which are regulated based on the filtering method. This made achieving almost 100% (0.995 or 99.5%) accuracy possible and reduced the loss function to 0.005 (0.5%) after 280 training epochs. An algorithm has been developed for neural network training based on the errors in backpropagation for closed loops, integrating the helicopter turboshaft engine parameters regulated based on the filtering method. It combines increasing the validation set accuracy and controlling overfitting, considering error dynamics, which preserves the model generalization ability. The adaptive training rate improves adaptation to the data changes and training conditions, improving performance. It has been mathematically proven that the helicopter turboshaft engine parameters regulating neural network closed-loop integration using the filtering method, in comparison with traditional filters (median-recursive, recursive and median), significantly improve efficiency. Moreover, that enables reduction of the errors of the 1st and 2nd types: 2.11 times compared to the median-recursive filter, 2.89 times compared to the recursive filter, and 4.18 times compared to the median filter. The achieved results significantly increase the helicopter turboshaft engine sensor readings accuracy (up to 99.5%) and reliability, ensuring aircraft efficient and safe operations thanks to improved filtering methods and neural network data integration. These advances open up new prospects for the aviation industry, improving operational efficiency and overall helicopter flight safety through advanced data processing technologies.

Recent advances in robust and ultra‐thin Li metal anode

AbstractLi metal batteries have been widely expected to break the energy‐density limits of current Li‐ion batteries, showing impressive prospects for the next‐generation electrochemical energy storage system. Although much progress has been achieved in stabilizing the Li metal anode, the current Li electrode still lacks efficiency and safety. Moreover, a practical Li metal battery requires a thickness‐controllable Li electrode to maximally balance the energy density and stability. However, due to the stickiness and fragile nature of Li metal, manufacturing Li ingot into thin electrodes from conventional approaches has historically remained challenging, limiting the sufficient utilization of energy density in Li metal batteries. Aiming at the practical application of Li metal anode, the current issues and their initiation mechanism are comprehensively summarized from the stability and processability perspectives. Recent advances in robust and ultra‐thin Li metal anode are outlined from methodology innovation to provide an overall insight. Finally, challenges and prospective developments regarding this burgeoning field are critically discussed to afford future outlooks. With the development of advanced processing and modification technology, we are optimistic that a truly great leap will be achieved in the foreseeable future toward the industrial application of Li metal batteries.

Multi-energy field simulation and experimental research on laser composite machining of micro-holes

Thin-walled micro-holes are frequently used in aerospace components to achieve specific functions, such as heat dissipation and filtration. However, traditional manufacturing technologies face difficulties in achieving precision machining of these holes due to deformation caused by cutting force or heat. Laser machining is a highly flexible and efficient advanced processing technology that aims to achieve precise machining of thin-walled holes. However, it is important to note that the thermal energy generated by the laser can cause deformation of the thin walls. To address these issues, this paper proposes a process that combines laser and backside electrochemical composite machining. The model for laser electrochemical composite processing after through-hole formation suggests that the laser's temperature rise effect on the electrolyte can significantly enhance the efficiency of electrochemical processing. Furthermore, the laser exerts a micro-zone stirring effect on the electrolyte in the processed micro-zone, which promotes the liquid-phase mass transfer process during the electrochemical reaction. Furthermore, a one-way experiment was conducted to investigate the effects of the main laser parameters on the processing results. The results indicated that higher laser power, as well as lower laser frequency and scanning speed, significantly reduced the edge damage and pore taper of the processed micro-holes. The language used is clear, concise, and objective, adhering to a formal register and avoiding biased or ornamental language. Technical terms are consistently used and explained when first introduced. The text is grammatically correct and free from spelling and punctuation errors. Furthermore, this process has significantly reduced the oxygen content and surface roughness of the sidewalls of the micro-holes.

Microstructural design characteristics of high-density polyethylene for thick-walled pressure pipe applications

Advancements in process technology and structure-property relationship-based design of high-density polyethylene has led to development of high-performance bimodal polyethylene grades suitable for large diameter thick-walled pressure pipe applications. These high-density bimodal products have enhanced design life, high pressure rating and sag resistance for fabrication of large diameter thick-walled pipe. Achieving optimal balance of these performance properties require controlled microstructural characteristics. The design characteristics are intricately influenced by catalyst type and process technology employed in resin manufacturing. In this study, we systematically establish correlations between microstructural characteristics of seven commercially available bimodal PE resins, produced using dual slurry or gas phase reactors, and their most relevant performance properties. We conducted comprehensive microstructural characterization of all samples using GPC-IR (high-temperature gel permeation chromatography coupled with an infrared composition detector), 13C NMR (nuclear magnetic resonance), TREF (temperature rising elution fractionation) and DSC (differential scanning calorimetry) techniques. Additionally, we measured mechanical, thermal, and rheological properties, focusing on crystallization kinetics, density, slow crack growth resistance (PENT and strain hardening modulus SHM) and low-frequency shear rheology of these products. Comparisons were made between key properties of interest, including slow crack growth resistance and low-frequency rheology (relevant for sag resistance), and microstructural characteristics. Theoretical and empirical correlations based on phenomenological models were employed for this analysis. The detailed study contributes to determining the critical levels of short and long chain branching, as well as molecular weight distribution characteristics essential to produce bimodal PE-4710 certifiable high-density polyethylene resins with low sag resistance. These design principles may serve as a foundation for the efficient design of reactor recipes and post-reactor modification processes, contributing to the evolution of pressure pipe polyethylene resin development and paving the way for design of next-generation pressure pipe grades.

Recent Advances in Low-Power Digital Signal Processing Technologies for Data Center Applications

Recent Advances in Low-Power Digital Signal Processing Technologies for Data Center Applications

Sentiment Analysis Based on Transformer

This article aims to explore the sentiment analysis of Transformer comments on Weibo to understand the attitudes and emotional tendencies of social media users regarding the movie The boy and the heron. This paper uses natural language processing technology and sentiment analysis model Transformer to crawl the Weibo comment data set, analyzes the anti-crawling mechanism of Weibo, adds HEADERS and REFERER directly in the code to bypass the inspection, and preprocesses it. Sentiment analysis was conducted on a large number of Weibo comments related to the boy and the heron. The results of the study showed that the vast majority of the comments expressed positive emotional attitudes and held positive and appreciative attitudes towards the boy and the heron. This study reveals that social media users have positive attitudes and emotional identification with the boy and the heron, and the audience really likes the film, which is of great help for the filmmakers to further understand the advantages and disadvantages of the film. Performing Weibo sentiment analysis based on advanced natural language processing technologies such as transformers can not only improve the accuracy and efficiency of sentiment analysis, but also help promote the progress and application of artificial intelligence technology. This is important for the development of more intelligent social media analysis tools and systems.