A sensitive intelligent point-of-care test method for tert-butylhydroquinone in edible oil via a test strip with a smartphone.

Tert-butylhydroquinone (TBHQ) is easily overused or illegally added to edible oil and attracts a growing concern because of its cytotoxic, liver-damaging, and carcinogenic effects. Thus, a sensitive and intelligent point-of-care testing (iPOCT) method is developed to fulfill the on-site monitoring. This iPOCT method depended on a fluorescent immunochromatographic assay within 15 min. Under optimization, the limit of quantification (LOQ) was calculated as 0.03 µg mL-1. The iPOCT method provided a low limit of detection (LOD) of 0.02 µg mL-1, a wide linear range of 0.03-100 µg mL-1, and great selectivity. Recoveries by the spiking experiments ranged from 97.4% to 103.5% with relative standard deviations (RSDs) of 2.4%-4.9% in soybean, peanut, rapeseed, and corn oil samples. The results showed that the iPOCT method is highly consistent with the high-performance liquid chromatography (HPLC) method.

pH-triggered bilayer film based on carboxymethyl cellulose/zein/Eudragit L100 with purple cabbage anthocyanin for monitoring pork freshness.

A novel pH-triggered bilayer film was composed of zein (Z), carboxymethylcellulose (CMC), Eudragit L100 (L100), and purple cabbage anthocyanin (PCA), followed by casting for monitoring pork freshness during storage at 4 °C and 25 °C. This bilayer film was employed to encapsulate anthocyanins, preventing anthocyanins oxidation and photodegradation. Additionally, under pH 6, this film ruptures and releases anthocyanins, inducing a sudden color change in the indicator film, significantly reducing errors in freshness indications. Notably, the ZCLP8% film had excellent stability and pH response properties. The performance of the ZCLP8% film in monitoring pork freshness was evaluated. When the concentration of pork TVB-N reached 15.59 mg/100 g (pH = 6.35), the bilayer film was ruptured, and the release rate of PCA was 85.52 %, which was a significant change in the color of the bilayer film compared with that at pH = 5. Therefore, this work addresses the limitation that anthocyanin-based intelligent films are subject to judgment errors when applied, opening new possibilities for food freshness differentiation monitoring.

Application of nano-ZnO in the food preservation industry: antibacterial mechanisms, influencing factors, intelligent packaging, preservation film and safety.

In recent years, how to improve the functional performance of food packaging materials has received increasing attention. One common inorganic material, nanometer zinc oxide (ZnO-NPs), has garnered significant attention due to its excellent antibacterial properties and sensitivity. Consequently, ZnO-NP-based functional packaging materials are rapidly developing in the food industry. However, there is currently a lack of comprehensive and systematic reviews on the use of ZnO-NPs as functional fillers in food packaging. In this review, we introduced the characteristics and antibacterial mechanism of ZnO-NPs, and paid attention to the factors affecting the antibacterial activity of ZnO-NPs. Furthermore, we systematically analyzed the application of intelligent packaging and antibacterial packaging containing ZnO-NPs in the food industry. At the same time, this paper also thoroughly investigated the impact of ZnO-NPs on various properties including thickness, moisture resistance, water vapor barrier, mechanical properties, optical properties, thermal properties and microstructure of food packaging materials. Finally, we discussed the migration and safety of ZnO-NPs in packaging materials. ZnO-NPs are safe and have negligible migration rates, simultaneously their sensitivity and antibacterial properties can be used to detect the quality changes of food during storage and extend its shelf life.

Enhancing self-regulation and learner engagement in L2 speaking: exploring the potential of intelligent personal assistants within a learning-oriented feedback framework.

INTRODUCTION: Developing L2 speaking proficiency can be challenging for learners, particularly when it comes to fostering self-regulation and maintaining engagement. Intelligent Personal Assistants (IPAs) offer a potential solution by providing accessible, interactive language learning opportunities. METHODS: This mixed-methods study investigated the effectiveness of using Google Assistant within a learning-oriented feedback (LOA) framework to enhance L2 speaking proficiency, self-regulation, and learner engagement among 54 university-level EFL learners in China. Convenience sampling assigned participants to either an experimental group (n = 27) using Google Assistant with tailored activities or a control group (n = 27) using traditional methods. The Oral Proficiency Interview (OPI) assessed speaking performance. Self-reported questionnaires measured L2 motivation and the Scale of Strategic Self-Regulation for Speaking English as a Foreign Language (S2RS-EFL) evaluated speaking self-regulation. Additionally, semi-structured interviews with a subsample of the experimental group provided qualitative insights. RESULTS: The Google Assistant group demonstrated a statistically significant improvement in speaking performance compared to the control group. While no significant difference in motivation was found, thematic analysis of interviews revealed perceived benefits of Google Assistant, including increased accessibility, interactivity, and immediate pronunciation feedback. These features likely contributed to a more engaging learning experience, potentially fostering self-regulation development in line with the core principles of LOA. CONCLUSION: This study suggests Google Assistant as a promising supplementary tool for enhancing L2 speaking proficiency, learner autonomy, and potentially self-regulation within an LOA framework. Further research is needed to explore its impact on motivation and optimize engagement strategies.

Prescription Precision: A Comprehensive Review of Intelligent Prescription Systems.

Intelligent Prescription Systems (IPS) represent a promising frontier in healthcare, offering the potential to optimize medication selection, dosing, and monitoring tailored to individual patient needs. This comprehensive review explores the current landscape of IPS, encompassing various technological approaches, applications, benefits, and challenges. IPS leverages advanced computational algorithms, machine learning techniques, and big data analytics to analyze patient-specific factors, such as medical history, genetic makeup, biomarkers, and lifestyle variables. By integrating this information with evidence-based guidelines, clinical decision support systems, and real-time patient data, IPS generates personalized treatment recommendations that enhance therapeutic outcomes while minimizing adverse effects and drug interactions. Key components of IPS include predictive modeling, Drug-Drug Interaction detection, adverse event prediction, dose optimization, and medication adherence monitoring. These systems offer clinicians invaluable decision-support tools to navigate the complexities of medication management, particularly in the context of polypharmacy and chronic disease management. While IPS holds immense promise for improving patient care and reducing healthcare costs, several challenges must be addressed. These include data privacy and security concerns, interoperability issues, integration with existing electronic health record systems, and clinician adoption barriers. Additionally, the regulatory landscape surrounding IPS requires clarification to ensure compliance with evolving healthcare regulations. Despite these challenges, the rapid advancements in artificial intelligence, data analytics, and digital health technologies are driving the continued evolution and adoption of IPS. As precision medicine gains momentum, IPS is poised to play a central role in revolutionizing medication management, ultimately leading to more effective, personalized, and patient-centric healthcare delivery.

An integrated approach for discrimination of Magnoliae officinalis cortex before and after being processed by ginger juice combining LC/MS, GC/MS, intelligent sensors, and chemometrics.

INTRODUCTION: Magnoliae officinalis cortex (MOC) is an important traditional Chinese medicine (TCM), and both raw and stir-fried MOC were commonly used in clinic. OBJECTIVES: This study aimed to discriminate MOC and MOC stir-fried with ginger juice (MOCG) using an integrated approach combining liquid chromatography/mass spectrometry (LC/MS), gas chromatography/mass spectrometry (GC/MS), intelligent sensors, and chemometrics. METHODS: The sensory characters of the samples were digitalized using intelligent sensors, i.e., colorimeter, electronic nose, and electronic tongue. Meanwhile, the chemical profiles of the samples were analyzed using LC/MS and GC/MS methods. Chemometric models were constructed to discriminate samples of MOC and MOCG based on not only the sensory data but also the chemical data. RESULTS: The differential sensory characters (L* and b* from colorimeter, ANS from electronic tongue, W1S and W2S from electronic nose) and the differential chemical compounds (26 and 11 compounds from LC/MS and GC/MS, respectively) were discovered between MOC and MOCG. Furthermore, twelve differential compounds showed good relations with differential sensory characters. Finally, artificial neural network models were established to discriminate samples of MOC and MOCG, in which W1S, W2S, ANS, b*, and 10 differential compounds were among the top 10 important variables, respectively. CONCLUSION: Samples of MOC and MOCG can be discriminated not only by the digitalized data of color, taste, and scent detected by intelligent sensors but also by chemical information obtained from LC/MS and GC/MS using chemometrics. The variations in sensory characters and chemical compounds between MOC and MOCG partially resulted from the Maillard reaction products and the oxidation of some compounds in the stir-frying process.

Should local governments adopt dynamic subsidy mechanism to promote the development of green intelligent buildings? An evolutionary game analysis.

The promotion of green intelligent buildings (GIBs) is regarded as an effective way to reduce carbon emissions and environmental pollution. How to formulate a reasonable and practical subsidy mechanism is crucial to promote the development of GIBs. However, there is still a lack of research on dynamic subsidy mechanism. To solve the research gap, based on consumer utility maximization theory, combining Hotelling model, the paper constructs an evolutionary game model between local governments and developers, and discusses the decision-making and evolutionary stable strategy (ESS) of both players under the dynamic subsidy mechanism. In addition, the paper defines a symbol event and analyzes in depth the possibility of effective diffusion of GIBs. Finally, the paper provides corresponding policy suggestions and draws the following conclusions: (1) ESS exists only after the introduction of dynamic subsidy mechanism, so it is necessary for local governments to formulate dynamic subsidy policies; (2) Under the dynamic subsidy mechanism, different subsidy adjustment rates will affect the evolutionary efficiency of the system; (3) The sensitivity of influence factors from high to low is as follows: subsidy adjustment rate, financial incentives for consumers, additional taxes for conventional buildings developers, carbon trading income for GIB developers and comprehensive residential benefits for GIB homebuyers. Improving these factors can increase the possibility of effective diffusion of GIBs.

Smart food packaging: Recent advancement and trends.

Food packaging plays an important role in protecting the safety and quality of food products and enables communication with consumers. With the improved consumers' awareness of safety and quality of food products, the changes in consumers' lifestyle, and the growing demand for transparency of food products along the supply chain, food packaging technologies have evolved from only providing the four fundamental functions (i.e., protection and preservation, containment, communication and marketing, and convenience) to possessing additional functions including active modification of the inside microenvironment (i.e., active packaging) and monitoring the safety and quality of products in real-time (i.e., intelligent packaging). A variety of active and intelligent packaging systems have been developed to better protect and monitor the quality and safety of food products during the past several decades. Recently, advanced versions of smart packaging technologies, such as smart active packaging and smart intelligent packaging technologies have also been developed to enhance the effectiveness of conventional smart packaging systems. Additionally, smart packaging systems that harvest the advantages of both active packaging and intelligent packaging have also been developed. In this chapter, a brief overview of smart packaging technologies was provided. Specific technologies being covered include conventional smart packaging technologies and advanced smart packaging technologies, such as smart active packaging, smart intelligent packaging and dual-function smart packaging.

Intelligent food packaging for smart sensing of food safety.

In this contemporary era, with over 8 billion people worldwide, ensuring food safety has become more critical than ever. To address this concern, the introduction of intelligent packaging marks a significant breakthrough. Essentially, this innovation tackles the challenge of rapid deterioration in perishable foods, which is vital to the well-being of communities and food safety. Unlike traditional methods that primarily emphasize shelf-life extension, intelligent packaging goes further by incorporating advanced sensing technologies to detect signs of spoilage and contamination in real-time, such as changes in temperature, oxygen levels, carbon dioxide levels, humidity, and the presence of harmful microorganisms. The innovation can rely on various packaging materials like plastics, metals, papers, or biodegradable polymers, combined with sophisticated sensing techniques such as colorimetric sensors, time-temperature indicators, radio-frequency identification tags, electronic noses, or biosensors. Together, these elements form a dynamic and tailored packaging system. This system not only protects food from spoilage but also offers stakeholders immediate and adequate information about food quality. Moreover, the real-world application on seafood, meat, dairy, fruits, and vegetables demonstrates the feasibility of using intelligent packaging to significantly enhance the safety and shelf life of a wide variety of perishable goods. By adopting intelligent packaging for smart sensing solutions, both the food industry and consumers can significantly reduce health risks linked with contamination and reduce unnecessary food waste. This underscores the crucial role of intelligent packaging in modern food safety and distribution systems, showcasing an effective fusion of technology, safety, and sustainability efforts aimed at nourishing a rapidly growing global population.

Systematic evaluations and integration of Assam indigenous Joha rice starch in intelligent packaging films for monitoring food freshness using beetroot extract.

It is becoming increasingly important to have starch sources with different physicochemical properties to meet the needs of new applications in food, packaging, bioplastic, and pharmaceutical industries. The first part of this study dealt with the isolation of starch from culturally, geographically, nutritionally esteemed, and high-yielding Assam Joha rice. Fine and uniform particle size (6.3 ± 0.09 µm), high amylose content (28 ± 1.03 %), swelling behavior, viscoelastic rheological behavior, moderate gelatinization temperature (66 ± 1.7 °C), thermostable nature, type A crystallographic pattern with high (45 ± 3.3 %) crystallinity, and suitable microbial quality make the Joha rice derived starch physico-chemically and functionally suitable for potential applications in diverse domains. The latter part of the study focuses on one of the applications of derived starch as a suitable matrix for intelligent packaging films with the incorporation of betanin-enriched beetroot extract (BRE) as a bio-based pH sensor. The addition of 1.0 % w/v BRE to the starch film (starch-BRE III) significantly increased its functionality by reducing UV-visible light transmittance and water vapor permeability, along with enhancing flexibility and hydrophobicity due to intermolecular bonding between BRE and the starch film matrix. Moreover, starch-BRE films with different BRE concentrations were successfully used to monitor the real-time freshness of white meat (chicken and fish) and Indian cottage cheese samples. Overall, the results indicated that starch-BRE III has great potential as an intelligent packaging material for monitoring food freshness.

CART-based wide-area damping controller for inter-area oscillations in bulk power system consisting of WAMS data.

The paper proposes a WADC approach using CART technique to dampen inter-area oscillations (IAOs) in bulk power systems. In this case, PMU data are filtered to estimate inter-area dynamics in which using pade approximation, a pole-zero IAO compensation block is designed. An online random decrement technique is also developed to identify the coherent groups and damping ratios to activate the WADC for oscillation damping. An offline process is provided to identify 200 critical IAO contingencies and tunes WADC gains using PSO for training CARTs via a set of 200 input inter-area signals and assigning output controlling gains pre-trained data and evaluating the CART estimations through online operation. The WADC approach is validated for oscillation damping on a 39-bus system and a realistic 561-generator Iranian grid. Simulations show 98 % accuracy in achieving sufficient damping ratios (>0.6) across various operating conditions.

Construction of intelligent response gene vector based on MOF/Fe3O4/AuNRs for tumor-targeted gene delivery.

Metal-organic frameworks (MOFs) have the potential to efficiently carry cargo due to their excellent porosity and high surface area. Nevertheless, conventional MOFs and their derivatives exhibit low efficiency in transporting nucleic acids and other small molecules, as well as having poor colloidal stability. In this study, a ZIF-90 loaded with iron oxide nanoparticles and Au nanorods was prepared, and then surface-functionalized with polyethyleneimine (PEI) to create a multifunctional nanocomposite (AFZP25k) with pH, photothermal, and magnetic responsiveness. AFZP25k can condense plasmid DNA to form AFZP25k/DNA complexes, with a maximum binding efficiency of 92.85 %. DNA release assay showed significant light and pH responsiveness, with over 80 % cumulative release after 6 h of incubation. When an external magnetic field is applied, the cellular uptake efficiency in HeLa cells reached 81.51 %, with low cytotoxicity and specific distribution. In vitro transfection experiments demonstrated a gene transfection efficiency of 44.77 % in HeLa cells. Following near-infrared irradiation, the uptake efficiency and transfection efficiency of AFZP25k in HeLa cells increased by 21.3 % and 13.59 % respectively. The findings indicate the potential of AFZP25k as an efficient and targeted gene delivery vector in cancer gene therapy.

[Application scenario design and prospect of generative artificial intelligence (AI) in intelligent manufacturing and supply chain of traditional Chinese medicine].

Intelligent manufacturing technologies, including databases, mathematical modeling, and information systems have played a significant role in process control, production management, and supply chain management in traditional Chinese medicine(TCM) industry. However, their ability to process and utilize unstructured data, such as research and development reports, batch production records, quality inspection records, and supplier documents, is relatively weak. For text, images, language, and other unstructured data, generative artificial intelligence(AI) technology has shown strong potential for development in extracting information, extracting knowledge, semantic retrieval, and content generation. Generative AI is expected to provide a feasible set of tools for the utilization of unstructured data resources in the TCM industry. Based on years of research and industrial application experience in TCM intelligent manufacturing technology, this study reviewed the current situation of intelligent manufacturing in TCM and the utilization of unstructured data, analyzed the application value of generative AI in the TCM manufacturing process and supply chain, summarized four typical application scenarios, including intelligent pharmaceutical knowledge base/knowledge graph, intelligent on-the-job trai-ning, intelligent production quality control, and intelligent supply chain. Furthermore, this study also explained the data collection and processing, business process design, application potential, and value of each scenario based on industry demands. Finally, based on the integration of generative AI and TCM industrial models, the study proposed a preliminary concept of a smart industrial brain for TCM, aiming to provide a reference for the application of AI technology in the field of TCM manufacturing.

Design and implementation of multi-band reflectarray metasurface for 5G millimeter wave coverage enhancement.

A compact low-profile multi-band millimeter-wave (mm-wave) reflectarray metasurface design is presented for coverage enhancement in 5G and beyond cellular communication. The proposed single-layer metasurface exhibits a stable reflection response under oblique incidence angles of up to 60 ∘ at 24 and 38 GHz, and transmission response at 30 GHz, effectively covering the desired 5G mm-wave frequency bands. The proposed reflectarray metasurface is polarization insensitive and performs equally well under TE and TM polarized incident waves due to the symmetric pattern. In addition, the low profile of the proposed metasurface makes it appropriate for conformal applications. In comparison to the state-of-the-art, the proposed reflectarray metasurface unit cell design is not only compact (3.3  ×  3.3 mm 2 ) but also offers two reflections and one transmission band based on a single-layer structure. It is easy to reconfigure the proposed metasurface unit cell for any other frequency band by adjusting a few design parameters. To validate the concept of coverage enhancement, a 32  ×  x32 unit-cell prototype of the proposed reflectarray metasurface is fabricated and measured under different scenarios. The experimental results demonstrate that a promising signal enhancement of 20-25 dB is obtained over the entire 5G mm-wave n258, n259, and n260 frequency bands. The proposed reflectarray metasurface has a high potential for application in mm-wave 5G networks to improve coverage in dead zones or to overcome obstacles that prevent direct communication linkages.

From Traditional to Intelligent, A Review of Application and Progress of Sensory Analysis in Alcoholic Beverage Industry.

Sensory analysis is an interdisciplinary field that combines multiple disciplines to analyze food qualitatively and quantitatively. At present, this analysis method has been widely used in product development, quality control, marketing, flavor analysis, safety supervision and inspection of alcoholic beverages. Due to the changing needs of analysis, new and more optimized methods are still emerging. Thereinto, intelligent and biometric technologies with growing attention have also been applied to sensory analysis. This work summarized the sensory analysis methods from three aspects, including traditional artificial sensory analysis, intelligent sensory technology, and innovative technologies. Meanwhile, the application sensory analysis in alcoholic beverages and its industrial production was scientifically emphasized. Moreover, the future tendency of sensory analysis in the alcoholic beverage industry is also highlights.

A review on the energy in buildings: Current research focus and future development direction.

Currently, although energy conservation related research in buildings is a matter of great urgency in the context of an ever more serious energy crisis, people seem to pay more attention on the field of civil engineering, such as the design, construction, monitoring and maintenance management of building structures. This is also evidenced by the authors' extensive research and strong practical engineering experience in infrastructure projects such as bridges. This study first presents the general building energy situation. The state of the art of the energy in buildings is then reviewed, followed by pointing out the intelligent monitoring-based future direction, and then the final goal towards the smart city can be expected. Specifically, more than one hundred published papers are selected for sample analysis, taking into account different research topics and different publication dates etc. The research topics, research methods and research conclusions of these published papers are very different, and they have not yet produced results that could be generally accepted. Actually, most of the published papers focus on the analysis and conservation of building energy, including the energy model for analysis and prediction, the energy affected by resident behavior and building forms, the renewable energy utilization and zero energy building. While a small part of the published papers is concerned with the resilient structural energy dissipation and collapse-resistant. Furthermore, the intelligent monitoring of building energy, supported by advanced sensor development and big data analysis technology, is also providing us a more promising future on the way to the smart city. It should be further noted that the design and construction codes or standards related to building energy have not yet been retrieved, and these have a strong guiding significance for engineering practice. Therefore, more research needs to be done to expect a better practical outcome.

A reliable jumping-based classification methodology for environment sector.

Decision-makers have consistently developed a range of classification models, each possessing unique features within the domain of intelligent models. These endeavors are all directed toward achieving the highest levels of accuracy. In recent developments, two notable methodologies-reliable modeling and jumping modeling approaches-offer specific advantages in formulating cost functions and have been recognized for their role in enhancing classifier accuracy. Specifically, the jumping methodology is based on aligning the learning process with the discrete nature of the classification goal, while the reliable methodology integrates the reliability factor into the learning paradigm. However, their innovative combination, leveraging both accuracy and reliability factors in guiding learning processes, leads to the creation of a high-performing classifier. This addresses a research gap in tackling classification challenges, which remains the core focus of the present study. To evaluate the performance of the proposed reliable jumping-based intelligent classifier in environmental decision-making, we considered ten benchmark datasets spanning various application domains. The numerical results demonstrate that the proposed Reliable Jumping-based intelligent classifier consistently outperforms traditional intelligent classifiers across all studied cases. As a result, the proposed approach proves to be a viable and effective alternative to other intelligent methods in environmental applications.

Revolutionary Point-of-Care Wearable Diagnostics for Early Disease Detection and Biomarker Discovery through Intelligent Technologies.

Early-stage disease detection, particularly in Point-Of-Care (POC) wearable formats, assumes pivotal role in advancing healthcare services and precision-medicine. Public benefits of early detection extend beyond cost-effectively promoting healthcare outcomes, to also include reducing the risk of comorbid diseases. Technological advancements enabling POC biomarker recognition empower discovery of new markers for various health conditions. Integration of POC wearables for biomarker detection with intelligent frameworks represents ground-breaking innovations enabling automation of operations, conducting advanced large-scale data analysis, generating predictive models, and facilitating remote and guided clinical decision-making. These advancements substantially alleviate socioeconomic burdens, creating a paradigm shift in diagnostics, and revolutionizing medical assessments and technology development. This review explores critical topics and recent progress in development of 1) POC systems and wearable solutions for early disease detection and physiological monitoring, as well as 2) discussing current trends in adoption of smart technologies within clinical settings and in developing biological assays, and ultimately 3) exploring utilities of POC systems and smart platforms for biomarker discovery. Additionally, the review explores technology translation from research labs to broader applications. It also addresses associated risks, biases, and challenges of widespread Artificial Intelligence (AI) integration in diagnostics systems, while systematically outlining potential prospects, current challenges, and opportunities.

Model-agnostic counterfactual reasoning for identifying and mitigating answer bias in knowledge tracing.

Knowledge tracing (KT) aims to monitor students' evolving knowledge states through their learning interactions with concept-related questions, and can be indirectly evaluated by predicting how students will perform on future questions. In this paper, we observe that there is a common phenomenon of answer bias, i.e., a highly unbalanced distribution of correct and incorrect answers for each question. Existing models tend to memorize the answer bias as a shortcut for achieving high prediction performance in KT, thereby failing to fully understand students' knowledge states. To address this issue, we approach the KT task from a causality perspective. A causal graph of KT is first established, from which we identify that the impact of answer bias lies in the direct causal effect of questions on students' responses. A novel COunterfactual REasoning (CORE) framework for KT is further proposed, which separately captures the total causal effect and direct causal effect during training, and mitigates answer bias by subtracting the latter from the former in testing. The CORE framework is applicable to various existing KT models, and we implement it based on the prevailing DKT, DKVMN, and AKT models, respectively. Extensive experiments on three benchmark datasets demonstrate the effectiveness of CORE in making the debiased inference for KT. We have released our code at https://github.com/lucky7-code/CORE.