Smart Behavior Research Articles

Sialylated glycan-modulated biomimetic ion nanochannels driven by carbohydrate–carbohydrate interactions

Ion channels play key roles in regulating the ion environment inside and outside the cell. Sialylated glycans (SGs) at the terminus of voltage-gated ion channels (VGICs) are abundant and directly control the switch of VGICs, while SGs on the cell surface are also closely related to virus infection, tumor growth, and metastasis. Here, we report a biomimetic ion nanochannel device that can be precisely regulated by SG. The nanochannel device is composed of a chemically etched polyethylene terephthalate film featuring conical nanochannels and a polyethyleneimine-g-malcopyranoside (abbreviated to Mal-PEI). Maltose, core-binding units in Mal-PEI, forms multiple hydrogen-bonding interactions with SG, which triggers globule-to-coil transition of the polymer chain and blocks transmembrane ion transport, resulting in a remarkable decrease in the ionic current of the nanochannel. Based on the changes in the ionic current, this device can precisely discriminate α2-3- and α2-6-linked sialyllactose, as well as SGs and neutral saccharides. Importantly, the nanochannel device can monitor the sialylation process of lactose catalyzed by α2,6-sialyltransferase in real time, showing its good potential in enzyme activity determination and in vitro enzyme identification. This work constructs an SG-modulated nanochannel with selective and smart ion-gating behavior, exhibiting unique advantages in SG responsiveness and enzymatic activity monitoring.

IoT-Inspired Monitoring Framework for Real-Time Stereotypic Movement Analysis

Physical activity recognition has become one of the prominent domain of research in the field of smart healthcare. The identification of physical activities through wearable sensors provide significant information about the functional ability and lifestyle of an individual. In this article, a smart physical activity-assisted behavior prediction framework is presented to recognize different stereotypical motor movements of the individual suffering from autism spectrum disorder in real time. This article employs the data acquisition efficiency of Internet of Things (IoT) assisted smart wearable sensors to capture the physical activities of an individual. To predict the physical activities, a deep learning-inspired multilayered convolutional neural network (CNN) + long short-term memory network is proposed. Furthermore, the utility of the framework is increased by presenting an effective fog-assisted alert-based decision-making module. The decision-making module notifies the concerned caretaker or medical specialist about the performed irregular physical activity in real time. Moreover, the process of cloud-based record generation also enhances the significance of the proposed solution. The proposed approach is validated by comparing the prediction performance with traditional handcraft-based and modern deep learning-based state-of-the-art approaches such as <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$k$</tex></formula> -nearest neighbors, hidden Markov model, and CNN, where it outperformed by achieving the overall accuracy of 91.88%.

THE ROLE OF COURSES IN INFLUENCING SMART CONSUMER BEHAVIOUR OF SENIOR HIGH SCHOOL STUDENTS IN RURAL AND URBAN AREAS

Consumer education interventions through formal education can increase knowledge, attitudes, and behaviour to become smart consumers who can enforce their rights, carry out their obligations, and protect themselves from harmful goods or services. This study aims to analyze differences in awareness of consumer education materials, sources of information, attitudes, and behaviour as savvy consumers in high school students in rural and urban Bogor and the factors that influence smart consumer behaviour. This study used a cross-sectional study design and involved two senior high schools located in the rural and urban areas of Bogor. The location selection was performed purposively, considering that the school implemented the Education Unit Level Curriculum (KTSP) and used the Electronic School Book (BSE). The sampling technique used random cluster sampling with a sample size of 129 high school students, 68 students in rural areas and 61 students in urban areas. The results showed significant differences in the material awareness of consumer rights variables and the three attitude dimensions. On the contrary, there was no difference in material awareness of consumer obligations, objective knowledge of rights and obligations and significant smart consumer behaviour on smart consumer behaviour in both regions. Students' age, objective knowledge about consumer obligations, and attitudes (affective and conative dimensions) significantly positively affect smart consumer behaviour in high school students.

Organic crystal-based flexible smart materials

Although the famous brittle characteristics of molecular crystals are unfavorable when they are used as flexible smart materials (FSMs), an increasing number of organic crystal-based FSMs have been reported recently. This breaks the perception of their stiff and brittle properties and promises a bright future for basic research and practical applications. Crystalline smart materials present considerable advantages over polymer materials under certain circumstances, rendering them potential candidates for certain applications, such as rapidly responsive actuators, ON/OFF switching, and microrobots. In this review, we summarize the recent developments in the field of organic crystal-based FSMs, including the derivatives of azobenzene, diarylethene, anthracene, and olefin. These organic crystal-based FSMs can bend, curl, twist, deform, or respond otherwise to external stimuli, such as heat or light. The detailed mechanisms of their smart behaviors are discussed with their potential applications in exciting intelligent fields. We believe this review could provide guidelines toward future fabrication and developments for novel organic crystal-based FSMs and their advanced smart applications.

Smart Mechanically Tunable Surfaces with Shape Memory Behavior and Wetting-Programmable Topography.

This paper reports for the first time the fabrication and investigation of wetting properties of structured surfaces formed by lamellae with an exceptionally high aspect ratio of up to 57:1 and more. The lamellar surfaces were fabricated using a polymer with tunable mechanical properties and shape-memory behavior. It was found that wetting properties of such structured surfaces depend on temperature, and thermal treatment history-structured surfaces are wetted easier at elevated temperature or after cooling to room temperature when the polymer is soft because of the easier deformability of lamellae. The shape of lamellae deformed by droplets can be temporarily fixed at low temperature and remains fixed upon heating to room temperature. Heating above the transition temperature of the shape-memory polymer restores the original shape. The high aspect ratio allows tuning of geometry not only manually, as it is done in most works reported previously but can also be made by a liquid droplet and is controlled by temperature. This behavior opens new opportunities for the design of novel smart elements for microfluidic devices such as smart valves, whose state and behavior can be switched by thermal stimuli: valves that can or cannot be opened that are able to close or can be fixed in an open or closed states.

Charge-controllable mussel-inspired magnetic nanocomposites for selective dye adsorption and separation

Magnetic polydopamine (PDA) nanocomposites were prepared with a facile and sustainable synthetic method. The as-synthesized polymer-based hybrid composites inherited the intrinsic adhesiveness contributed by catechol and amino moieties of PDA as well as the magnetic property of Fe3O4. With the unique properties of PDA, the surface charges of Fe3O4@PDA could be easily tuned by pH for smart adsorption-desorption behaviors. Four commercially available dyestuffs including crystal violet, rhodamine B, direct blue 71 and orange G with different structures and surface charges in solution were selected to investigate the adsorption ability and universality of Fe3O4@PDA in wastewater treatment. It was found that the nanocomposites could successfully adsorb these cationic and anionic dyes under suitable pH conditions. This confirmed the ability of the nanoadsorbents for the removal of common textile dyes. The dispersed magnetic nanoadsorbents also demonstrated the ease of collection from dye mixtures, and the possibility of reusing them for several cycles. Selective dye separation was found to be achievable via simple charge control without large consumption of organic solvent and energy. These bio-inspired nanocomposite adsorbents have shown high potential in wastewater treatment and selective recovery of dye waste, especially for wastewater containing ionic dyes.

Kartini in the Pandemic: Women's Economic Empowerment Through Synergy of Digital Banking, Fintech, and E-Commerce

The role of the Financial Services Authority (OJK) is getting bigger in Indonesia. Various strategies are implemented to increase financial inclusion and literacy in Indonesia. However, the mindset that is built that women are identical to housewives who do not have financial independence creates a gap in inclusion and financial literacy between men and women. As a result, women cannot contribute to managing finances well and cannot help build a strong family economy because they do not have the knowledge and financial skills as well as men. The KARTINI program is a program that provides facilities for the women's community to have financial independence. The integration between digital banking (smart behavior), financial technology (affiliate marketing), and e-commerce (dropship) can be the right program in optimizing financial inclusion and literacy for women. The implementation of the KARTINI program has a positive impact on the women's community such as women's cooperatives, PKK women, Aisyiyah women, recitation groups, dharma Wanita and other women's groups. In addition, this program can be applied by OJK to all regions in Indonesia to increase financial literacy and inclusion. Through the integration of smart behavior, affiliate marketing, and dropship, the goals of OJK will be achieved more quickly.

The Status Quo Problem and the Role of Consumers Against Antimicrobial Resistance

Antimicrobial resistance occurs when microorganisms survive exposure and proliferate in the presence of therapeutic levels of antimicrobial drugs. Because antimicrobial resistance is increasing, it is vital to encourage consumers to change and adopt smarter antibiotic behaviour. Despite World Health Organization's efforts to combat antibiotic resistance and their emphasis on the importance of public involvement, the role of consumers has been overlooked. The manifold responsibility for antibiotic resistance extends across different actors, including food retailers and consumers. Given this shared responsibility, a blame game arises and no action occurs. To overcome this status quo situation, we draw attention to the potential role of individual responsibility and social pressure to encourage consumers to adopt smart antibiotic behaviour but also to empower them. Conditions must be put in place to enable consumers' critical evaluation of the health-related and ethical aspects of their food choices. Such behaviour can be facilitated using digital innovations to support informed choices, in store and online.

Biodegradable polyester platform for extrusion-based bioprinting

3D extrusion-based bioprinting has become an emerging tissue engineering technology to produce complex biomimetic structures for a reconstitution of living tissues. Simultaneous dispensing of two and more (bio)polymer materials affords the fabrication of functional 3D constructions displaying both bioactivity (bioink) and defined architecture and shape (scaffold frame). To fabricate biocompatible and biodegradable 3D scaffolds, aliphatic (co)polyesters are the materials of first choice. Recent progress has shown that the function of the polyester's frame can be expanded from contemporary mechanical support to a composite matrix with smart stimuli-responsive behavior. Consequently, it opens new avenues in the fabrication of multifunctional scaffolds, which can be designed as dynamic systems and/or delivery systems for bioactive molecules in situ. This minireview is focused on utilizing the most attractive representatives of aliphatic polyesters in the areas of bioartificial tissue/organ 3D and 4D bioprinting.

Synthesis of tertiary amine functionalized Multi-Stimuli-Responsive latex nanoparticles by semicontinuous emulsion Polymerization: Investigation of responsivities and antimicrobial activity

Stimuli-responsive core–shell nanoparticles (CSNPs) are an important class of smart polymers, which their physical and chemical properties could be changed in response to induced external stimuli. In this study, the amphiphilic CSNPs were synthesized via semi-continuous seeded emulsion polymerization using dimethylamino ethyl methacrylate (DMAEMA) as a hydrophilic monomer, and styrene as a hydrophobic monomer. Developed CSNPs were obtained in two series, poly(N, N-dimethylamino ethyl methacrylate) (PDMAEMA)-polystyrene (PS) core–shell particles and PS-PDMAEMA core–shell particles. The PDMAEMA chains exhibited different responsivities towards temperature rising, pH change, and CO2-gas, which their smart behaviors were investigated by dynamic light scattering (DLS) analysis. The temperature-responsivity of CSNPs was confirmed by observed particle size variation in the case of PDMAEMA-PS (increase) and PS-PDMAEMA (decrease) core–shell latex particles, when temperature increased to 70 °C, above the lower critical solution temperature (LCST) of PDMAEMA. Protonation of tertiary amine functional groups in the presence of CO2 bubbling led to an increase in the size of CSNPs as a result of water swelling of PDMAEMA chains. On the other hand, the size of CSNPs was changed in acidic (pH = 4) and basic (pH = 10) media by swelling and deswelling of PDMAEMA located in the core or shell. Obtained results indicated that maximum antimicrobial activity was observed for PS-PDMAEMA CSNPs compared to PDMAEMA-PS, which is probably due to presence of the PDMAEMA on the surface and increase of positive charge related to amine groups in this sample. The developed antimicrobial latexes have potential applications for using as antibacterial additives in water-based coatings and adhesives.

Smart behaviour of CNT modified adhesive films for carbon fiber composite repair

Epoxy adhesives films were doped by spraying an aqueous dispersion of carbon nanotubes. Sensing capability of carbon fiber reinforced composite coupons with bonded patch repairs was evaluated; several single lap shear and Mode-I fracture energy tests were conducted and their electrical responses were characterized. It was observed that electrical resistance increases with mechanical strain due to tunnelling effect on CNT percolating networks and increases of electrical resistance associated to crack propagations could be also detected. Once the smart behaviour if the adhesive joints was identified by means of mechanical tests in normalized adhesive joined specimens, panels (400 x 400mm) with square co-bonded patches, made using the same curing cycle based on the repaired material one, were prepared using CNT doped adhesive films. These panels were mechanical tested to determine the effect of the doping treatment on the mechanical behaviour of the repairs, at the same time that electric measurements were carried out to identified local failures during testing. Obtained results showed that structural film adhesives could be surface doped with CNT providing sensing capabilities to be used in structural health monitoring of adhesive repairs in composite structures.

Smart Deep Learning Based Human Behaviour Classification for Video Surveillance

Real-time video surveillance system is commonly employed to aid security professionals in preventing crimes. The use of deep learning (DL) technologies has transformed real-time video surveillance into smart video surveillance systems that automate human behavior classification. The recognition of events in the surveillance videos is considered a hot research topic in the field of computer science and it is gaining significant attention. Human action recognition (HAR) is treated as a crucial issue in several applications areas and smart video surveillance to improve the security level. The advancements of the DL models help to accomplish improved recognition performance. In this view, this paper presents a smart deep-based human behavior classification (SDL-HBC) model for real-time video surveillance. The proposed SDL-HBC model majorly aims to employ an adaptive median filtering (AMF) based pre-processing to reduce the noise content. Also, the capsule network (CapsNet) model is utilized for the extraction of feature vectors and the hyperparameter tuning of the CapsNet model takes place utilizing the Adam optimizer. Finally, the differential evolution (DE) with stacked autoencoder (SAE) model is applied for the classification of human activities in the intelligent video surveillance system. The performance validation of the SDL-HBC technique takes place using two benchmark datasets such as the KTH dataset. The experimental outcomes reported the enhanced recognition performance of the SDL-HBC technique over the recent state of art approaches with maximum accuracy of 0.9922.

Solidity Code Generation From UML State Machines in Model-Driven Smart Contract Development

For the development of blockchain smart contracts, a structured approach based on the principles of the Model Driven Architecture can be beneficial and facilitate the implementation of smart contracts. This paper presents such an approach, which, in combination with Unified Modeling Language (UML) Class and State machine diagrams, allows the smart contract structure and behavior logic to be modeled in several abstraction layers. This paper delves into details on how the model-to-model transformations from the specified Blockchain Platform Independent Model (PIM) with specified state-like behavior can be used to produce a Solidity Platform Specific Model (PSM). Subsequently, we elaborate on how the Solidity PSM is used for Solidity smart contract code generation by employing model-to-text transformations. The paper also demonstrates the process of our proposed transformations and code generation using smart contract code examples from Solidity documentation. Based on the examples, a Blockchain PIM is specified and transformed to Solidity PSM, which is then used for Solidity smart contract code generation. The generated smart contract code is then compiled, deployed on the Ethereum blockchain JavaScript virtual machine, and compared to the original smart contract code in terms of Solidity code metrics, similarity scores, and execution costs. The evaluation results indicate that our approach could be successfully used to model and later generate smart contract code.

Role of chain architecture in the solution phase assembly and thermoreversibility of aqueous PNIPAM/silyl methacrylate copolymers.

Stimuli-responsive polymers functionalized with reactive inorganic groups enable creation of macromolecular structures such as hydrogels, micelles, and coatings that demonstrate smart behavior. Prior studies using poly(N-isopropyl acrylamide-co-3-(trimethoxysilyl)propyl methacrylate) (P(NIPAM-co-TMA)) have stabilized micelles and produced functional nanoscale coatings; however, such systems show limited responsiveness over multiple thermal cycles. Here, polymer architecture and TMA content are connected to the aqueous self-assembly, optical response, and thermo-reversibility of two distinct types of PNIPAM/TMA copolymers: random P(NIPAM-co-TMA), and a 'blocky-functionalized' copolymer where TMA is localized to one portion of the chain, P(NIPAM-b-NIPAM-co-TMA). Aqueous solution behavior characterized via cloud point testing (CPT), dynamic light scattering (DLS), and variable-temperature nuclear magnetic resonance spectroscopy (NMR) demonstrates that thermoresponsiveness and thermoreversibility over multiple cycles is a strong function of polymer configuration and TMA content. Despite low TMA content (≤2% mol), blocky-functionalized copolymers assemble into small, well-ordered structures above the cloud point that lead to distinct transmittance behaviors and stimuli-responsiveness over multiple cycles. Conversely, random copolymers form disordered aggregates at elevated temperatures, and only exhibit thermoreversibility at negligible TMA fractions (0.5% mol); higher TMA content leads to irreversible structure formation. This understanding of the architectural and assembly effects on the thermal cyclability of aqueous PNIPAM-co-TMA can be used to improve the scalability of responsive polymer applications requiring thermoreversible behavior, including sensing, separations, and functional coatings.

Polyaniline coated ZnFe2O4 microsphere and its electrorheological and magnetorheological response

Magnetic zinc ferrite particles synthesized by the solvothermal method were coated with conductive polyaniline, and their electro- and magneto-responsive smart fluid behaviors were investigated. Both size and morphology of the produced particles were identified by SEM and TEM. Their chemical composition and crystal faces were analyzed by FT-IR and X-ray diffraction, respectively. The synthesized particles were suspended in silicone oil at 5 vol%, and their electro- and magneto-responses were scrutinized using a rotation rheometer. In an electric field, the flow curve was fitted smoothly to the CCJ model, and the yield stress showed an exponential value of 1.5. In contrast, an exponential value of 1.0 was observed for the yield stress in a magnetic field. • Core-shell structured ZnFe 2 O 4 /PANI microspheres were synthesized. • Aniline monomer was adsorbed onto ZnFe 2 O 4 surface and polymerized via oxidation polymerization. • ER fluid based on ZF/PANI particles was prepared. • Different yield stresses of EMR fluid were correlated with universal yield stress equation.

Blast Performance Evaluation of Steel Moment-Resisting Frame Equipped with Smart Bolted Connection

This paper deterministically evaluates the blast performance of steel moment-resisting frame (MRF) smart structures equipped with nickel titanium shape-memory alloy (NiTi SMA) bolted connections. The recentering capacity of the NiTi SMA bolts is exploited to limit the structural damage and reduce the residual drift after a near-field explosion event. A simplified blast over-pressure calculation approach is used to draw the blast overpressure profile. Numerically, the NiTi SMA–based connection is designed conforming to current European standards and is modeled with a solid finite element. The effect of high-strain-rate actions is considered. The response of the NiTi SMA–based connection is obtained by the nonlinear transient analysis and compared with the steel bolted connection. The NiTi SMA–based connection provided excellent recentering capacity. Later, the moment-rotation response of the NiTi SMA–based connection is utilized in the MRF structure. A simplified self-centering connection is proposed to produce smart behavior in the MRF structures. The results reveal that using NiTi SMA connections keep interstory drift relatively low and substantially provide reasonable structural lateral resistance under near-field blast loading.

ANALISIS PENERAPAN AGEN46 SEBAGAI PENYEDIA LAYANAN KEUANGAN TANPA KANTOR (BRANCHLESS BANKING) DAN UPAYA PENDORONG FINANCIAL INCLUSION

ABSTRACT&#x0D; The development of the financial sector, especially the banking sector, can increase banking access to the public. The problem that occurred before the implementation of Agen46 was that it was difficult for people to get financial services, especially in rural communities. There are still many people who do not know, use, and get financial services, which is a complex problem. The number of Agen46 in Banjarnegara Regency is 160, while the population in Banjarnegara is 923,192 people. The comparison between the number of Agen46 and the number of residents has not met the needs of the community. Branchless banking /smart behavior, encourages banking institutions to develop financial service innovations to encourage financial inclusion. PT Bank Negara Indonesia (Persero), Tbk develops financial service innovations through Agen46. The purpose of this study was to determine the role of Agen46 in its application as a financial service without offices to meet the financial needs of the community.&#x0D; This research is a type of research using a qualitative approach. The data used in this study are primary data by conducting interviews and observations at Agen46 in the Banjarnegara Regency in accordance with the research criteria. The sampling technique used in this study was purposive sampling technique, where the sampling technique took into account certain parties who were considered to meet the criteria, knew, and understood Agen46.&#x0D; The results of this study indicate the role of Agen46 as a provider ofbranchless banking services for financial inclusion. The existence of agen46 is very beneficial for the people of Banjarnegara to meet their financial service needs

Smart-microstructures of composites for electrical contacts with frameless packing of Cr and W in copper

W45Cu55, Cr65Cu35, and Cr32W14Cu54 alloys were obtained in order to study the mechanism of “smart response” of the structure of these alloys when using them as arc-resistant circuit-breakers. These alloys differ from industrial ones with frameless packing of Cr and W phases in the copper matrix. The alloy production method is based on the infiltration of copper melt into a mixture of non-compacted Cr and W powders under vibration exposure (80 Hz). The research results show an increase in the arc resistance of contacts when changing from “frame” packing of W to “frameless,” as well as the decisive role of Cr in the processes of self-dispersion of arc-resistant phases and passivation of W and Cu. Based on the obtained results, conclusions are drawn about the advantage of frameless packing of arc-resistant phases in copper and the reasons for the “smart behavior” of the structure of Cr-containing contacts in response to functional loads in the presence of oxygen and an inert atmosphere.

Human activity recognition using wearable sensors, discriminant analysis, and long short-term memory-based neural structured learning

Healthcare using body sensor data has been getting huge research attentions by a wide range of researchers because of its good practical applications such as smart health care systems. For instance, smart wearable sensor-based behavior recognition system can observe elderly people in a smart eldercare environment to improve their lifestyle and can also help them by warning about forthcoming unprecedented events such as falls or other health risk, to prolong their independent life. Although there are many ways of using distinguished sensors to observe behavior of people, wearable sensors mostly provide reliable data in this regard to monitor the individual’s functionality and lifestyle. In this paper, we propose a body sensor-based activity modeling and recognition system using time-sequential information-based deep Neural Structured Learning (NSL), a promising deep learning algorithm. First, we obtain data from multiple wearable sensors while the subjects conduct several daily activities. Once the data is collected, the time-sequential information then go through some statistical feature processing. Furthermore, kernel-based discriminant analysis (KDA) is applied to see the better clustering of the features from different activity classes by minimizing inner-class scatterings while maximizing inter-class scatterings of the samples. The robust time-sequential features are then applied with Neural Structured Learning (NSL) based on Long Short-Term Memory (LSTM), for activity modeling. The proposed approach achieved around 99% recall rate on a public dataset. It is also compared to existing different conventional machine learning methods such as typical Deep Belief Network (DBN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) where they yielded the maximum recall rate of 94%. Furthermore, a fast and efficient explainable Artificial Intelligence (XAI) algorithm, Local Interpretable Model-Agnostic Explanations (LIME) is used to explain and check the machine learning decisions. The robust activity recognition system can be adopted for understanding peoples' behavior in their daily life in different environments such as homes, clinics, and offices.

Smart Self-Cleaning Membrane via the Blending of an Upper Critical Solution Temperature Diblock Copolymer with PVDF.

Poly(2,2,2-trifluoroethyl methacrylate)-b-poly(imidazoled glycidyl methacrylate-co-diethylene glycol methyl ether methacrylate) (PTFEMA-b-P(iGMA-co-MEO2MA)) containing an upper critical solution temperature (UCST) polymer chain was prepared and blended with poly(vinylidene fluoride) (PVDF) to produce a thermoresponsive membrane with smart self-cleaning performance. The successful preparation of the membrane was demonstrated by attenuated total reflection-Fourier-transform infrared spectroscopy, X-ray photoelectron spectroscopy, and scanning electron microscopy characterization. The membrane shows UCST performance, and its flux changes with the filtrate temperature as the UCST polymer chain stretches out and contracts in response to various temperatures. In addition, the UCST polymer chain can disrupt the foulant and push it away from the membrane when the temperature is above the UCST and thus enables membranes to exhibit a smart self-cleaning behavior. To the best of our knowledge, this work is the first report of a smart self-cleaning membrane based on the blending of a diblock copolymer containing a UCST polymer chain with PVDF.