Mode Of Interaction Research Articles

The impact of digital communication and offline social interactions on depressive symptoms in Chinese older people

BackgroundIn a special period of lack of offline social interaction (pandemic), the mentality of older people is changing quietly. This study aims to dissect the impact of these changes on their mental health.MethodUtilizing data from the China Health and Retirement Longitudinal Study (CHARLS 2020), this research included 7,784 participants aged over 60 years. It evaluated the prevalence of depressive symptoms, and assessed the relative effects of these interactions on depressive symptoms.Results(1) a depressive symptom prevalence of 40.65%; (2) a modest engagement in digital communication, with only 20.39% of the older participating; (3) varying prevalences of depressive symptoms across groups, with notable differences depending on the type and combination of social interactions. Specifically, the prevalence was 21.7% among those engaging only in digital communication, and varied from 21.7 to 32.0% among other groups, highlighting the significant impact of social interaction patterns on depressive symptoms. Statistical analysis confirmed the significance of these findings (χ2 = 42.415, p < 0.001). (4) In the first model, digital communication was associated with a lower likelihood of depressive symptoms (OR = 0.820, 95%CI: 0.707–0.950, p < 0.05). The second model showed no significant effect of offline social interactions on depressive symptoms (OR = 0.124, 95%CI: 0.917–1.143, p = 0.678). The third model demonstrated that the inclusion of offline social interaction variables did not significantly alter the beneficial effect of digital communication (OR = 0.820, 95%CI: 0.707–0.952, p < 0.05). (5) The results of the 1:1 matching analysis indicated that digital communication significantly alleviated depressive symptoms in older people (ATT = −0.054, p < 0.05). Conversely, offline social interactions did not significantly affect the depressive symptoms of this demographic (ATT = −0.028, p > 0.05).ConclusionThe depressive symptoms among older people in China has greatly increased during 2020 which year the COVID-19 pandemic was in the early stage, accompanied by considerable changes in their modes of social interaction. Our findings suggest that the influences of digital communication and offline social interactions on depressive symptoms may have operated independently during the pandemic. The potential of digital communication engagements in enhancing mental health, particularly in exceptional circumstances such as during a pandemic, underscores the need for further exploration.

Impact of Peer Interaction on the Improvement of English Speaking for Students of Non-English Majors

This paper explores the impact of peer interaction on the improvement of English speaking for students of non-English majors. Peer interaction is always seen as the most important factor affecting students’ English speaking performance. The purpose of this paper is to discover the essence of peer interaction, then try to apply the concept of peer interaction to the actual university class. At last, this paper advocates that we should adopt the interactional teaching mode to help students improve their English speaking proficiency.

Synthesis, Biological Evaluation, and Molecular Docking Studies of Indole-Based Heterocyclic Scaffolds as Potential Antibacterial Agents.

Indole-based heterocyclic scaffolds have become increasingly important in medicinal chemistry due to their notable pharmacological and biological properties. Their role in the discovery and development of innovative drugs for treating various diseases highlights their value. This study aimed to synthesize C3-indole derivatives linked to various heterocyclic scaffolds, including thiophenes, thiazolidine-4-ones, and 1,3,4-thiadiazoles, via the reaction of ethylthioacetanilide 2 with different α-haloketones.The structures of the target compounds were established using 1H and 13C nuclear magnetic resonance spectroscopy, mass spectrometry, infrared spectroscopy, and elemental analysis. The synthesized compounds were tested for antimicrobial activity against different microbes: S. aureus ATCC 6538 (Gram-positive bacteria), E. coli ATCC 25933 (Gram-negative bacteria), C. albicans ATCC 10231 (yeast), and fungi (A. niger NRRL-A326). Thiophene 6a, thiazolidine-4-one 8, and compound 10d exhibited the highest antimicrobial activities. The molecular docking study showed that compounds 2, 4, 6a, and 6c had good binding energy and favorable binding modes of interactions with the DNA gyrase B enzymes (PDB: 3 U2D) and (PDB: 1S14). The results showed that the NH group of the indole in compounds 2 and 4, together with the nitrile group (CN), played an important role in inhibiting DNA gyrase B of S. aureus, PDB: 3 U2D. Furthermore, the NH of the indole ring, together with the ethylamino group of compound 2, was crucial in inhibiting DNA gyrase B of E. coli, PDB: 1S14. These findings may encourage researchers to develop more effective C3-indole derivatives in their search for antimicrobial drugs.

Objects, affordances, and material histories in Roman Palestine: a case study on Northern Collar-Neck Lamps and their uses in early Jewish Sources

Abstract This article seeks to contribute to the growing scholarship on object-focused Roman histories by expanding the conversation to previously overlooked archaeological finds from Roman Palestine. This case study focuses on “Northern Collar-Neck Lamps,” which have been found throughout Roman Galilee and date to the first two centuries CE. I argue that their distinctive high collar, perhaps designed to reduce spillage, also served as an affordance that invited additional modes of interaction, namely placing a supplemental reservoir for oil – such as a pierced eggshell – over the filling hole. Once set up, this would allow for a slow drip of oil to prolong illumination time without human intervention. This usage is suggested from chronologically and geographically proximate sources, namely early rabbinic literature: Hebrew and Aramaic writings from the first centuries that reference physical details and uses of hundreds of objects and could prove helpful for future material histories of the Roman era.

Unlocking Neuraminidase Inhibitors: Insights from Natural Products through Pharmacophore Modeling, Virtual Screening, and Molecular Docking

Background: Neuraminidase enzyme plays a major role in the life cycle of influenza viruses. Targeting neuraminidases is a key strategy in preventing and treating influenza A and B spread by inhibiting the release of new viral particles from infected cells. Developing new neuraminidase inhibitors with enhanced efficacy and reduced risk of resistance is the ultimate of ongoing research. Objective: In this study, we tried to shed light on molecules of natural origin that inhibit neuraminidase enzyme by utilizing different aspects of in silico studies. Methods: The work started by generating structure-based pharmacophore, later used for the virtual screening of electronic libraries constructed from chemical and natural compounds. Hits raised from virtual screening were subjected to molecular docking to assess and explain different modes of interactions with the neuraminidase enzyme. Drug likeness and ADME filters were applied to choose compounds that may be taken effectively by oral route with good gastrointestinal absorption and acceptable pharmacokinetics with respect to Lipinski and Ghose rules. The hits were also inspected for toxicity risks, including mutagenic, tumorigenic, irritant, and reproductive effects. Results: 7 features of the generated pharmacophore from the potent inhibitor Oselatamiver were the base for virtual screening. The results obtained by database screening highlighted 4 natural compounds listed in the COCONUT library (CNP 0125691 (Penicitrinol K), CNP0256196 (Frenolicin D), CNP 0138184, and CNP 0206296) as potential neuraminidase inhibitors. The compounds showed a similar interaction profile to Oseltamivir by molecular docking with high binding affinity. The key residue TYR 406 hydrogen bonded to Penicitrinol and Frenolicin D as well as to Oseltamevir. The 4 natural compounds exerted drug-like properties and promising pharmacokinetic characters. Toxicity risks estimations put Penicitrinol K and Frenolicin as devoted to mutagenic, tumorigenic, irritant and reproductive potentials. Penicitrinol K and Frenolicin are assigned for further in vitro and in vivo studies as promising neuraminidase inhibitors. Conclusion: Penicitrinol K, and Frenolicin D are promising natural neuraminidase inhibitors, exhibiting favourable drug-like properties and low toxicity risks in addition to showing the pattern of interaction profiles with neuraminidase enzymes similar to the known drug Oseltamivir contributing to the development of effective antiviral therapies against influenza, suggesting further in vitro and in vivo evaluation.

Technical Feasibility Analysis of Digital Twin Technology in the Field of Urban Disaster Prevention

Abstract. In recent years, disasters have occurred frequently around the world, causing great harm to national finances and people's safety. There is still a lack of research on digital disaster prevention technology, but with the development of technologies such as artificial intelligence and computer vision, it has gradually become possible to use digital twin technology in urban disaster prevention. This paper is mainly based on digital twin related technologies, briefly introduces the concept and importance of digital twins, and the applicable fields, and then discusses the mode of data interaction between real entities and twins and the feasibility of digital disaster prevention, so as to prevent and simulate urban disasters and derive strategies for dealing with disasters. This paper reviews the existing research on digital twin technology through the method of literature retrieval and analysis. By analyzing technologies such as the Internet of Things, sensors, three-dimensional laser technology, urban monitoring technology, satellite remote sensing technology, drone remote sensing technology, mobile device technology, lidar technology, and oblique photography, a systematic review of critical analysis is obtained, and a macro method of data interaction and modeling for digital twin technology for disaster prevention is obtained, thereby deriving the feasibility of disaster prevention based on digital twin systems.

Cell shape affects bacterial colony growth under physical confinement

Evidence from homogeneous liquid or flat-plate cultures indicates that biochemical cues are the primary modes of bacterial interaction with their microenvironment. However, these systems fail to capture the effect of physical confinement on bacteria in their natural habitats. Bacterial niches like the pores of soil, mucus, and infected tissues are disordered microenvironments with material properties defined by their internal pore sizes and shear moduli. Here, we use three-dimensional matrices that match the viscoelastic properties of gut mucus to test how altering the physical properties of their microenvironment influences the growth of bacteria under confinement. We find that low aspect ratio (spherical) bacteria form compact, spherical colonies under confinement while high aspect ratio (rod-shaped) bacteria push their progenies further outwards to create elongated colonies with a higher surface area, enabling increased access to nutrients. As a result, the population growth of high aspect ratio bacteria is, under the tested conditions, more robust to increased physical confinement compared to that of low aspect ratio bacteria. Thus, our experimental evidence supports that environmental physical constraints can play a selective role in bacterial growth based on cell shape.

Rare Sesterterpenoids from the Soil Derived Fungus, Aspergillus variecolor Strain SDG, Therapeutic Potential of Stellatic Acid and Docking Studies.

Ethyl acetate extract of the cultures of the soil-derived filamentous fungus, Aspergillus variecolor SDG strain from Nallamala forest resulted in the isolation of extremely rare sesterterpenoids, stellatic acid (1) and andilesin C (2). We report a thorough chemical characterization of these compounds using various spectroscopic techniques and evaluation of their in vitro preclinical therapeutic potential. Stellatic acid exhibits potent antioxidant activity with an IC50 of 38 µg/mL and significant anticancer activity against HeLa, HepG2, MCF7, and A549 cancer cell lines with an IC50 of 7-12 µM. On the other hand, andilesin C displayed moderate cytotoxicity against DU145 and B16F10 cancer cell lines but lacked antioxidant activity. Furthermore, the potential hypoglycemic property of stellatic acid was evaluated by measuring its inhibitory effect against α-glucosidase. It exhibited tenfold potency against yeast α-glucosidase (IC50 101.73 µg/mL) than mammalian α-glucosidase (IC50 1000.00 µg/mL). Docking studies were also performed to suggest the interaction mode of stellatic acid in the α-glucosidase enzyme active site. Notably, yeast α-glucosidase shows a higher affinity towards stellatic acid than mammalian α-glucosidase (3TOP). Thus, the in vitro preclinical study of stellatic acid suggests its potential efficacy in therapeutic drug development.

A systematic review on robot-assisted language learning for adults

In the 21st-century era of globalization, language proficiency is a pivotal connector across cultures, with artificial intelligence (AI) revolutionizing educational paradigms through Robot-Assisted Language Learning (RALL). This systematic review examines the role of RALL in adult second language acquisition, focusing on its pedagogical strategies and learner engagement. Unlike the previous systematic reviews that explore the multifaceted roles of robots in language learning, including as teachers, tutors, assistants, and peer learners, we identify explicit and implicit instructional strategies within RALL, highlighting the unique learning landscape of adult learners characterized by self-regulation and self-direction. We assess the latest advancements in RALL for adult learners through three research questions, compare the effectiveness of explicit versus implicit instructions, and investigate affective factors enhancing RALL performance. Our review contributes a comprehensive status analysis, in-depth exploration of interaction modes, and insights for future research directions, providing a roadmap for academic research and practical guidance for educators and robot developers. This study aims to optimize RALL strategies to better meet the needs of adult learners, fostering a more efficient and engaging language learning experience.

Constructing Flexible Crystalline Porous Organic Salts via a Zwitterionic Strategy.

The unique ionic channels and highly polar pore structures have distinguished crystalline porous organic salts (CPOSs) from conventional porous frameworks in the past decade. Up to now, CPOSs were all constructed by a monoionic strategy, in which two types of building units individually bearing anionic or cationic groups were introduced, thus increasing complexity in the synthesis of CPOSs. In this study, by utilizing stereoisomeric compounds of TPE-NS-Z or TPE-NS-E bearing both anionic and cationic groups as a single building unit, the zwitterionic strategy was proven feasible in constructing CPOSs. Benefiting from the single building unit, the zwitterionic strategy simplified the preparation process and reduced the difficulty in studying the aggregation behavior of building units into CPOSs. And also, this novel strategy enabled precise control of the finally obtained CPOSs through fine-tuning of the initial building units. Surprisingly, the special parallel/vertical alternated stacking mode and unique ionic interaction networks in the crystal structure provided the flexible pore characteristic of CPOS-E, which further guaranteed the multitime controllable release of highly polar chemicals in different solvents.

Higher-order structure based node importance evaluation in directed networks

Evaluating the significance of objects with possible relevant information is a crucial topic in information science. Due to the fact that objects related to each other can often be described using complex networks, this topic also forms a fundamental theme in network science. Most traditional methods for characterizing the importance of nodes in complex networks only utilize the binary relationships between node pairs, neglecting the influence brought by higher-order structures. Considering the specific interaction modes between local nodes in the network, this paper associates the higher-order structural characteristics of the network with the importance of the nodes. It constructs an evaluation framework for the importance of nodes in directed networks based on higher-order structures. Experimental analysis on both artificial data and scientific citation data from the APS dataset has validated the effectiveness of the proposed algorithms. Compared with PageRank and eigenvector centrality, the proposed algorithms demonstrated higher accuracy, revealing the role of higher-order structures in node importance evaluation. Finally, a robustness analysis of several algorithms indicated that the proposed algorithms exhibited good robustness.

The Trail Making Test in Virtual Reality (TMT-VR): The Effects of Interaction Modes and Gaming Skills on Cognitive Performance of Young Adults

Virtual Reality (VR) is increasingly used in neuropsychological assessments due to its ability to simulate real-world environments. This study aimed to develop and evaluate the Trail Making Test in VR (TMT-VR) and investigate the effects of different interaction modes and gaming skills on cognitive performance. A total of 71 young female and male adults (aged 18–35) with high and low gaming skills participated in this study. Participants completed the TMT-VR using three interaction modes as follows: eye-tracking, head movement, and controller. Performance metrics included task completion time and accuracy. User experience, usability, and acceptability of TMT-VR were also examined. Results showed that both eye tracking and head movement modes significantly outperformed the controller in terms of task completion time and accuracy. No significant differences were found between eye tracking and head movement modes. Gaming skills did not significantly influence task performance using any interaction mode. The TMT-VR demonstrates high usability, acceptability, and user experience among participants. The findings suggest that VR-based assessments can effectively measure cognitive performance without being influenced by prior gaming skills, indicating potential applicability for diverse populations.

Animal-Assisted Interventions: Factors Affecting Donkey Behaviours and Attitude Toward Humans

The relationship between humans and donkeys has ancient origins. In recent years, donkeys’ involvement in Animal-Assisted Intervention (AAI) has grown, raising major concerns about their welfare during these activities. We investigated the factors affecting donkeys’ behaviour during an AAI programme for migrants. We observed an effect of human distance from the animal, the interaction mode, and food presence on the animals’ behaviours and attitudes toward humans. Moreover, we provide preliminary evidence of the effect of AAI on migrants’ welfare and prosocial behaviours and offer insights into the issues to address in future studies.

A Study of the Bioactive Compounds, Antioxidant Capabilities, Antibacterial Effectiveness, and Cytotoxic Effects on Breast Cancer Cell Lines Using an Ethanolic Extract from the Aerial Parts of the Indigenous Plant Anabasis aretioïdes Coss. & Moq.

Anabasis aretioïdes contain numerous bioactive compounds that provide several advantages, including antioxidant, antibacterial, anticancer, neuroprotective, anti-inflammatory, and antidiabetic characteristics. This study aimed to make a hydroethanolic extract from the aerial part of the plant, analyze its biochemical compounds, and test its biological activities. From HPLC-DAD analysis, cinnamic acid, sinapic acid, and vanillin bioactives were found to be the main compounds in the extract. The spectrometric tests revealed that the extract was rich in flavonoids (8.52 ± 0.32 mg RE/100 g DW), polyphenols (159.32 ± 0.63 mg GAE/100 g DW), and condensed tannins (8.73 ± 0.23 mg CE/100 g DW). The extract showed significant antioxidant activity. There were strong correlations between the amount of flavonoid or polyphenol and the antioxidant assays, including ABTS, DPPH, β-carotene, and TAC. The extract also showed highly effective results against Gram-positive bacteria Staphylococcus aureus and Enterococcus faecalis as well as against Gram-negative bacteria Escherichia coli and Pseudomonas aeruginosa, and showed promising cytotoxicity against breast cancer cell lines MCF-7 and MDA-MB-231. The in silico modeling of the bioactive compounds contained in the extract illustrated their interaction mode with the active sites of particular target proteins, and it showed that rutin had the strongest effect on stopping NADPH oxidase enzyme, with a glide score of −6.889 Kcal/mol. Sinapic acid inhibited E. coli beta-ketoacyl-[acyl carrier protein] synthase (−7.517 kcal/mol), and apigenin showed high binding affinity to S. aureus nucleoside di-phosphate kinase, with −8.656 kcal/mol. Succinic acid has the strongest anticancer effect for caspase-3, with a glide score of −8.102 kcal/mol. These bioactive components may be beneficial as antioxidant and antibacterial applications in medicine, foods, natural cosmetics, and breast cancer prevention in the future. As a result, the use of this indigenous plant must be considered to maximize its value and preservation.

Space Weather Forecasts of Ground Level Space Weather in the UK: Evaluating Performance and Limitations

AbstractGeomagnetically Induced Currents (GICs) are a severe space weather hazard, driven through coupling between the solar wind and magnetosphere. GICs are rarely measured directly, instead the ground magnetic field variability is often used as a proxy. Recently space weather models have been developed to forecast whether the magnetic field variability (R) will exceed specific, extreme thresholds. We test an example machine learning‐based model developed for the northern United Kingdom. We evaluate its performance (discriminative skill and calibration) as a function of magnetospheric state, solar wind input and magnetic local time. We find that the model's performance is highest during active conditions, for example, geomagnetic storms, and lowest during isolated substorms and “quiet” intervals, despite these conditions dominating the training data set. Correspondingly, the performance is high when the solar wind conditions are elevated (i.e., high velocity, large total magnetic field strength, and the interplanetary magnetic field oriented southward), and at a minimum when the north‐south component of the magnetic field is highly variable or around zero. Regarding magnetic local time, performance is highest within the dusk and night sectors, and lowest during the day. The model appears to capture multiple modes of magnetospheric activity, including substorms and viscous interactions, but poorly predicts impulsive phenomena (i.e., storm sudden commencements) and longer timescale coupling processes. Future models of mid‐latitude magnetic field variability will need to effectively use longer time intervals of unpropagated (i.e., observations from L1) solar wind to more completely describe the magnetospheric conditions and response.

Polydopamine-incorporated MOF membrane with hydrophilicity for effective oil/water separation and fouling-resistant model analysis and prediction

A large number of oil spills and oily wastewater are discharged, in the modern industrial production process, resulting in serious water pollution. Oily wastewater can be extremely harmful to ecosystems and human health. Oil-water separation has become a major challenge at present, and membrane separation has aroused more and more concern in recent years due to its high economic efficiency. This paper fabricated PES filtration membranes using the novel nanoparticlesbased on both metal–organic frameworks-5 (MOF-5) and polydopamine (PDA) layers as dopants, and then adequately explored the porosity, morphology, separation, hydrophilicity, and fouling-resistant performance of the resultant membranes. In addition, the prepared membranes were used for oil–water separation, including soybean-in-water, petroleum ether-in-water, and gasoline-in-water emulsions. MOF@PDA membranes exhibit high separation efficiency of up to 99.8%. Subsequently, membrane fouling mechanisms were investigated using the Derjaguin-Landau-Verwey-Overbeek (XDLVO) theory, and the molecular mode of interaction with the three oil–water emulsions with the membrane was compared. Newly prepared MOF@PDA membranes have better stability, fouling-resistant, and self-cleaning properties. Finally, an optimal machine learning model for oil–water separation efficiency was developed with a high prediction accuracy of 98%. The obtained results indicate that mixed matrix membranes exhibit excellent oil–water separation performance, demonstrating great application prospects in the remediation of oily wastewater.

Gesture2Text: A Generalizable Decoder for Word-Gesture Keyboards in XR Through Trajectory Coarse Discretization and Pre-Training.

Text entry with word-gesture keyboards (WGK) is emerging as a popular method and becoming a key interaction for Extended Reality (XR). However, the diversity of interaction modes, keyboard sizes, and visual feedback in these environments introduces divergent word-gesture trajectory data patterns, thus leading to complexity in decoding trajectories into text. Template-matching decoding methods, such as SHARK2 [32], are commonly used for these WGK systems because they are easy to implement and configure. However, these methods are susceptible to decoding inaccuracies for noisy trajectories. While conventional neural-network-based decoders (neural decoders) trained on word-gesture trajectory data have been proposed to improve accuracy, they have their own limitations: they require extensive data for training and deep-learning expertise for implementation. To address these challenges, we propose a novel solution that combines ease of implementation with high decoding accuracy: a generalizable neural decoder enabled by pre-training on large-scale coarsely discretized word-gesture trajectories. This approach produces a ready-to-use WGK decoder that is generalizable across mid-air and on-surface WGK systems in augmented reality (AR) and virtual reality (VR), which is evident by a robust average Top-4 accuracy of 90.4% on four diverse datasets. It significantly outperforms SHARK2 with a 37.2% enhancement and surpasses the conventional neural decoder by 7.4%. Moreover, the Pre-trained Neural Decoder's size is only 4 MB after quantization, without sacrificing accuracy, and it can operate in real-time, executing in just 97 milliseconds on Quest 3.

Drug Repurposing and Screening for Multiple Sclerosis Targeting Microglia and Macrophages.

Microglia/macrophages (MG/Mφ) play a central role in the pathogenesis of multiple sclerosis (MS). However, the intricacies of the immunomodulatory microenvironment in MS, particularly the heterogeneity and regulatory mechanisms of MG/Mφ subpopulations, remain elusive. The commonly used treatment options for MS have several drawbacks, such as significant side effects and uncertain efficacy. The exploration of developing new drugs targeting MG/Mφ for the treatment of MS remains to be investigated. We identified three distinct subpopulations of MG/Mφ, among which MG/Mφ_3 significantly increased as the experimental autoimmune encephalomyelitis (EAE) progressed. Ifenprodil and RO-25-6981 demonstrated notable inhibition of inflammatory factor expression, accompanied by reduced cytotoxicity. The interaction modes of these compounds with the common binding pocket in the GluN1b-GluN2B amino terminal domain heterodimer were elucidated. Virtual docking, based on the N-methyl-D-aspartate (NMDA) receptor, showed that homo-skeleton compounds of ifenprodil potentially exhibit low binding free energy with the receptor, including eliprodil and volinanserin. In vitro cell models corroborated the effective inhibition of inflammatory factor expression and minimal cytotoxicity of eliprodil and volinanserin. CoMFA (standard error of estimate = 0.378, R2 = 0.928, F values = 241.255, Prob. of R2 = 0) and topomer CoMFA (q2 = 0.553, q2 stderr = 0.77, intercept = - 1.48, r2 = 0.908, r2 stderr = 0.35) were established based on the inhibitors of NMDA receptor. The contour maps of CoMFA and topomer CoMFA models give structural information to improve the inhibitory function. This study underscores the involvement of MG/Mφ in inflammatory pathways during MS progression and offers promising compound candidates for MS therapy targeting MG/Mφ.

Experimental characterization and dual-temperature molecular dynamics simulation on the intervention of tea saponin in starch chain dynamic behavior

In this work, the typical properties of starchy products were innovatively described as six types of chain dynamic behaviors. Dual-temperature molecular dynamic simulations, alongside multi-experimental methods, were employed to tandemly explore the intervention effect and mechanism of tea saponin (TS, 0 %–40 % w/w) on these behaviors. The findings reveal that the hydrophilic and hydrophobic ends of TS provide numerous sites for hydrogen bonding and steric hindrance, respectively, which hinder the formation of hydrogen bonds between starch chains. This interaction mode facilitated the chain unwinding (pasting temperature: 79.8 → 76.4 °C) and movement (viscosity: 267.67 → 38.92 Pa.s), and also retarded chain short/long-term reassociation (elastic modulus: 0.41 → 0.14 Pa/min; hardening rate: 2.72 → 0.07 gf/d) and rearrangement (hardness: 15.50 → 10.00 gf). Notably, a critical TS content was observed between 10 % and 20 % w/w, beyond which textural collapse (hardness: 15.50 → 10.00 gf) occurred. This research offers a new strategy and relevant theoretical backing for the property regulation of starch products.

Construction of Smart Clothing Service System for the Health and Well-Being of the Aging Community in a Sustainable Society

Objective: The phenomenon of global aging is becoming more and more severe, and security services such as nursing and guardianship for the elderly have become the focus of social attention. According to the data released by the World Health Organization, by 2030, one out of every six people will be over 60 years old. By 2050, the world population will be 2.1 billion over 60 years old, and the number of people over 80 years old will reach 460 million. This paper aims to monitor the health and services of aging clothing intelligently for the health and well-being of the aging community in a sustainable society, starting from the physiological and psychological needs of the new elderly. Theoretical Framework: In the age-appropriate smart clothing service system, the critical link is to construct the human-computer interaction mode of clothing and wearable devices, establish a design paradigm from single interaction to multi-interaction, and form a service terminal based on smart wearable. Method: This paper designs an aging smart clothing design system using field research, questionnaires, interviews, etc., analyzes the aging smart wearable clothing evaluation system, and constructs aging smart clothing that meets the actual needs of health and well-being for the elderly in a sustainable society. Results and Discussion: In this research, The results obtained sustainable design theories and methods combined with interdisciplinary research tools as the basis of data analysis from the aging intelligent clothing product design system, age-appropriate smart clothing service system, and age-appropriate smart clothing evaluation system. Research Implications: Population aging has been the basic national condition of our country for quite a long time in the future, and "healthy aging" is a strategic measure put forward by the government around the intelligent care service system for the elderly, communities, and institutions. Important direction. Among them, how to build an age-appropriate smart wearable product service system has become the main development content. Therefore, its intelligence and synergistic combination with terminals such as communities, caregivers, and hospitals will become the trend of smart elderly care. Originality/Value: This study contributes to Construct a systematic design of a new intelligent wearable clothing service system for the elderly, which is a great of significance for improving the service quality index of the health and well-being of the elderly. At the same time, it is of great value to expand and deepen the concept of sustainable design.