Adaptive Applications Research Articles

Global Supply Chain Information Sharing Platforms: Challenges, Opportunities, and Trends

With the deepening process of globalization, the rapid development of information technology and the diversification of logistics methods, supply chain management faces increasingly complex challenges. This study explores the future development trends, challenges and opportunities of information sharing platforms in global supply chains. In global supply chains, information sharing platforms are of great significance. It facilitates real-time data exchange and collaboration among supply chain segments and help improve supply chain transparency, flexibility and efficiency. However, global supply chain information sharing faces many challenges, including problems in information security, technical standards, cultural differences, and laws and regulations. In response to these challenges, this paper proposes future directions for developing a global supply chain information sharing platform, including digitization, automation, cross-platform cross-border collaboration, data security and privacy protection, application of new technologies, and flexibility and adaptability. This study provides useful insights and guidance for future global supply chain management by exploring the global supply chain information sharing platform.

Stuart Stress Adaptation and Nola Pender's Model on Mental Nursing Care of Patients Schizophrenia: Case Study

To describe the application of the Stuart Stress Adaptation and Nola Pender Models in mental nursing care for a patient with schizophrenia and risk of violent behavior. This research method used case studies. The sample is one patient with schizophrenia. Data collection is done through direct observation, interviews, and patient medical records. The results showed that the client showed a major nursing problem in the form of risk of violent behavior. Predisposing factors included passive childhood experiences and socio-cultural issues, while precipitation included feeling disrespected by the husband's family. Nursing interventions provided to the patient included logotherapy, forgiveness therapy, and psychoeducation to the family. Family psychoeducation forms a supportive system in the client's recovery process. The results of this study indicate that there is a reduction in symptoms of violent behavior in schizophrenia patients. The importance of integrating the Stuart Stress Adaptation and Nola Pender Model in the psychiatric nursing care of patients with paranoid schizophrenia.

Calibration approach of non-modulated pyramid wavefront sensors for improving the dynamic range

The pyramid wavefront sensor (PWFS) can provide high sensitivity in demanding adaptive optics applications. However, its exquisite sensitivity has a limited dynamic range, which relies on the use of beam modulation to improve the dynamic range of the PWFS—despite this task being achieved by moving optical components in the PWFS, increasing the complexity of the system. An attractive idea to simplify the optical and mechanical design of a PWFS is to work without any dynamic modulation. This paper proposes a new method, to the best of our knowledge, called the pyramid wavefront sensor with truncated axicon (TA-PWFS), which is used in the non-modulated PWFS in the closed-loop adaptive optics system with high sensitivity and high dynamic range without the need for cumbersome modulation. The new approach uses a diffractive element placed at a conjugated Fourier plane of the pyramid prism to shape part of the incident light in a ring pattern around the pyramid pin. The radius of the circular-shaped image is identical to that of the modulation radius in the sinusoidal modulation, offering a high dynamic range, while the remaining portion of the light continues un-diffracted, producing high sensitivity through a spot on the pyramid pin. Simulation results with different kinds of aberrations containing large global tilts reveal that the use of the TA-PWFS has a noticeable improvement in the aberration estimation performance. With a highly simplified structure, elements without mechanical modulation, and aberration reconstruction without precorrection of global tilt, the TA-PWFS could present an innovative method for the design of non-modulated pyramid wavefront sensors that can better suit the higher requirements of adaptive optics applications.

Recent Developments on Novel 2D Materials for Emerging Neuromorphic Computing Devices

The rapid advancement of artificial intelligent and information technology has led to a critical need for extremely low power consumption and excellent efficiency. The capacity of neuromorphic computing to handle large amounts of data with low power consumption has garnered a lot of interest during the last few decades. For neuromorphic applications, 2D layered semiconductor materials have shown a pivotal role due to their distinctive properties. This comprehensive review provides an extensive study of the recent advancements in 2D materials‐based neuromorphic devices especially in multiterminal synaptic devices, two‐terminal synaptic devices, neuronal devices, and the integration of synaptic and neuronal devices. Herein, a wide range of potential applications of memory, computation, adaptation, and artificial intelligence is incorporated. Finally, the limitations and challenges of neuromorphic devices based on novel 2D materials are discussed. Thus, this review aims to illuminate the design and fabrication of neuromorphic devices based on van der Waals (vdW) heterostructure materials, leveraging promising engineering techniques to excel the applications and potential of neuromorphic computing for hardware implementations.

Adaptive Learning Web Application to Improve CAD Learning in Engineering

Adaptive Learning Web Application to Improve CAD Learning in Engineering

Multifunctional Metamaterial with Reconfigurable Electromagnetic Scattering Properties for Advanced Stealth and Adaptive Applications.

The rapid development of radar detection systems has led to an increased sensitivity to the electromagnetic (EM) scattering properties of detected targets. Flexible and adaptable EM scattering properties significantly enhance the survivability of battlefield weapons. This paper presents the design of a novel multifunctional metamaterial with reconfigurable EM scattering properties based on a bistable curved beam. In addition to the cushioning and energy absorption properties of curved beams, the metamaterial achieves more than 90% EM absorption in the frequency range of 2.17-17.31GHz, with a relative thickness of only 0.09λL. The bistable nature of the metamaterial allows it to switch between different states. Moreover, combined with the digital coding, this metamaterial can continuously adjust the absorbing bandwidth and further enhance the EM absorption rate within a specific frequency band range. If applied to satellite configurations, the developed metamaterial significantly reduces the radar cross section and offers potential applications in reconfiguring EM scattering properties, when applied to satellite configurations. By actively controller and reconstructing the EM scattering properties at certain frequency points, the metamaterial can achieve camouflage, providing innovative solutions for future stealth technology, electronic countermeasures, and deception jamming in radar detection.

Transfer graph feature alignment guided multi-source domain adaptation network for machinery fault diagnosis

In recent years, the application of unsupervised multi-source domain adaptation (MSDA) techniques for fault diagnosis has gained significant traction. Current research typically overlooks or fails to effectively capture critical data structure information during feature extraction. Another challenge is optimising the integration of information from multiple source domains to diagnose the target domain while avoiding negative transfer. To address these challenges, this study proposes a transfer graph feature alignment guided multi-source domain adaptation network (MDTGAL). In the proposed method, a transfer graph sample generator module (GSG) is constructed to model the data structure between source and target domains, and multiple graph feature extractors are employed to learn the data structure information from different domain combinations. A regularisation technique is introduced to extract the domain-invariant features by aligning the parameters across multiple independent graph feature extractors. In addition, a weighted soft-voting mechanism based on the polynomial kernel-induced maximum mean difference metric (PK-MMD) is designed to fuse the outputs from multiple classifiers, to comprehensively account for the influence of each source domain. The proposed method was tested on multi-source domain transfer tasks involving various operating conditions of rotating machinery. The experimental results demonstrate that the MDTGAL exhibits superior cross-domain diagnostic performance, outperforming existing mainstream methods. In addition, this study explored the impact of varying numbers of source domains on the diagnostic accuracy of the target domain, providing insights into selecting the correct number of source domains for specific MSDA tasks.

The Impact of Data Privacy on Users' Smartphone App Adoption Decisions

Rich user information is gained through user tracking and power mobile smartphone applications. Apps thereby become aware of the user and their context, enabling intelligent and adaptive applications. However, such data poses severe privacy risks. Although users are only partially aware of them, awareness increases with the proliferation of privacy-enhancing technologies. How privacy literacy and raising privacy concerns affect app adoption is unclear; however, we hypothesize that it leads to a lower adoption rate of data-heavy smartphone apps, as non-usage often is the user's only option to protect themselves. We conducted a survey (N=100) to investigate the relationship between privacy-relevant app- and publisher characteristics with the users' intention to install and use it. We found that users are especially critical of contentful data types and apps with rights to perform actions on their behalf. On the other hand, the expectation of a productive benefit induced by the app can increase the app-adoption intention. Our findings show which aspects designers of privacy-enhancing technologies should focus on to meet the demand for more user-centered privacy.

Evaluating the Impact of the World Health Organization's Surgical Safety Checklist on Clinical Outcomes and Implementation Strategies: A Systematic Review.

This systematic review evaluates the impact and implementation strategies of the World Health Organization's Surgical Safety Checklist (WHO SSC) across diverse healthcare settings since its introduction in 2008. Our comprehensive analysis synthesizes findings from various study designs, including randomized controlled trials, qualitative studies, and meta-analyses, focusing on the checklist's effectiveness in reducing surgical complications and enhancing safety cultures within surgical teams. Despite its widespread endorsement and documented benefits, the review highlights significant variability in implementation quality and adherence, influenced by cultural, institutional, and procedural factors. The findings suggest that optimized adaptation and contextual application of the WHO SSC are crucial for maximizing its clinical benefits, particularly in low-resource settings. This review not only confirms the checklist's efficacy in improving surgical outcomes but also underscores the need for evidence-based strategies to enhance its global implementation and effectiveness.

MURDA: Multisource Unsupervised Raman Spectroscopy Domain Adaptation Model with Reconstructed Target Domains for Medical Diagnosis Assistance.

Artificial intelligence combined with Raman spectroscopy for disease diagnosis is on the rise. However, these methods require a large amount of annotated spectral data for modeling to achieve high diagnostic accuracy. Annotating labels consumes significant medical resources and time. To reduce dependence on labeled medical data resources, we propose a method called Multisource Unsupervised Raman Spectroscopy Domain Adaptation Model with Reconstructed Target Domains (MURDA). It transfers knowledge learned from source domain data sets of different diseases to an unlabeled target domain data set. Compared to knowledge transfer from a single source domain, knowledge from multiple disease source domains provides more generalized knowledge. Considering the diversity of autoimmune diseases and the limited sample size, we apply MURDA to assist in the medical diagnosis of autoimmune diseases. Additionally, we propose a Double-Branch Multiscale Convolutional Self-Attention (DMCS) feature extractor that is more suitable for spectral data feature extraction. On three sets of serum Raman spectroscopy data sets for autoimmune diseases, the multisource domain adaptation diagnostic accuracy of MURDA was superior to traditional single source and multisource models, with accuracy rates of 73.6%, 83.4%, and 82.9%, respectively. Compared with pure source tasks without domain adaptation, it improved by 15.1%, 36%, and 21.6%, respectively. This demonstrates the effectiveness of Raman spectroscopy combined with MURDA in diagnosing autoimmune diseases. We investigated the important decision dependency peaks in migration tasks, providing assistance for future research on artificial intelligence combined with Raman spectroscopy for diagnosing autoimmune diseases. Furthermore, to validate the effectiveness and generalization performance of MURDA, we conducted experiments on the publicly available RRUFF data set, exploring the application of multisource unsupervised domain adaptation in more Raman spectroscopy scenarios.

Experimental evaluation of municipal solid waste air-gasification in a pilot-scale reciprocating moving-grate furnace

The increasing volume of municipal solid waste (MSW) worldwide presents significant environmental challenges, necessitating the development of efficient waste-to-energy (WtE) solutions. Among various thermochemical methods, gasification offers a promising approach for converting MSW into syngas, which can be utilized for energy generation. This study investigates the gasification characteristics of MSW in a pilot-scale reciprocating moving-grate furnace, focusing on the effect of key operating parameters such as equivalence ratio (ER), gasification temperature, and gasifying agent-staged ratio on gasification characteristics.Seven experimental schemes were tested with varying lower heating values (LHV) of MSW (ranging from 6.98 to 15.1 MJ/kg) and throughputs (ranging from 0.77 to 1.67 tons per day) to assess the adaptability and stability of the moving-grate system under different conditions. The results indicate that an ER between 0.6 and 0.7, a gasification temperature of 760 °C, and a gasifying agent-staged ratio of 7:3 are optimal for achieving a maximum energy conversion efficiency of 71.6 %. It was observed that the LHV of syngas decreases when the gasification temperature exceeds 850 °C due to increased oxidation of light hydrocarbons. Moreover, the study highlights the influence of grate moving speed on residence time and reaction completeness, which are critical for optimizing syngas yield and quality.The findings demonstrate that while the maximum energy conversion efficiency of the moving-grate system is lower than other reactor types, its lower capital and operating costs, due to the lack of dedicated feedstock pretreatment, make it a viable option for small-scale and pilot-scale applications. This study provides valuable insights into optimizing MSW gasification processes and underscores the potential of the moving-grate furnace for adaptable and cost-effective WtE applications. The novelty of this work lies in the comprehensive evaluation of the moving-grate gasification process under varied operating conditions, providing a foundation for future research on improving efficiency and reducing environmental impact in large-scale MSW management.

Hybrid group opportunistic maintenance strategy for offshore wind turbines considering maintenance resource allocation in harsh working environments

The intelligent maintenance of offshore wind turbines (OWTs) is vital for sustainable energy generation and utilization, as harsh working environments generally limit their application. As offshore wind farms expand into deeper waters, operational and maintenance costs continue to increase. Therefore, devising targeted maintenance strategies for offshore wind farms is crucial. Existing strategies often overlook the role of maintenance resources in controlling the operational and maintenance costs. To address this problem, this study proposes a hybrid group opportunistic maintenance strategy to implement resource allocation-based maintenance for OWTs under dynamic conditions. First, a failure rate model based on the life decline and system coupling is proposed to capture the evolution of failures in OWTs in harsh working environments, thereby enhancing the accuracy of fault maintenance. Subsequently, a hybrid linear and nonlinear deterioration model is proposed to identify the different deterioration trends owing to the various sensitivities of diverse components to harsh working environments, which enhances the precision of degradation maintenance. In addition, a maintenance resource allocation model is proposed to allocate resources in advance, thereby increasing resource utilization and reducing maintenance costs. A multi-objective optimization approach is proposed to maximize the mean maintenance level (MML) and minimize the per unit cycle expected maintenance cost rate (PECR) by considering harsh working environments. Finally, the nutcracker optimization algorithm is introduced into the maintenance model to generate the optimal solution in a multi-objective manner. The results show that the maintenance model is feasible and effective for practical adaptive maintenance applications of OWTs in harsh working environments in practical applications.

Cross-domain additive learning of new knowledge rather than replacement.

In medical clinical scenarios for reasons such as patient privacy, information protection and data migration, when domain adaptation is needed for real scenarios, the source-domain data is often inaccessible and only the pre-trained source model on the source-domain is available. Existing solutions for this type of problem tend to forget the rich task experience previously learned on the source domain after adapting, which means that the model simply overfits the target-domain data when adapting and does not learn robust features that facilitate real task decisions. We address this problem by exploring the particular application of source-free domain adaptation in medical image segmentation and propose a two-stage additive source-free adaptation framework. We generalize the domain-invariant features by constraining the core pathological structure and semantic consistency between different perspectives. And we reduce the segmentation generated by locating and filtering elements that may have errors through Monte-Carlo uncertainty estimation. We conduct comparison experiments with some other methods on a cross-device polyp segmentation and a cross-modal brain tumor segmentation dataset, the results in both the target and source domains verify that the proposed method can effectively solve the domain offset problem and the model retains its dominance on the source domain after learning new knowledge of the target domain.This work provides valuable exploration for achieving additive learning on the target and source domains in the absence of source data and offers new ideas and methods for adaptation research in the field of medical image segmentation.

Novel rebreathing adaptation extends dive time in a semi-aquatic lizard.

Bubble use evolved in many small invertebrates to enable underwater respiration, but, until recently, there has been no evidence that vertebrate animals use bubbles in a similar manner. Only one group of vertebrates, semi-aquatic Anolis lizards, may be an exception: these lizards dive underwater when threatened and, while underwater, rebreathe a bubble of air over their nostrils. Although it seems that rebreathing should be adaptive, possibly functioning to extend the time that lizards remain in underwater refugia, this has not been empirically tested. Here, I demonstrate that rebreathing serves to extend dive time in a semi-aquatic anole, Anolis aquaticus. I prevented the formation of normal rebreathing bubbles by applying a commercial emollient on the skin surface where bubbles form to assess the impact of bubbles on rebreathing cycles, gular pumps, and dive times. Lizards that were allowed to rebreathe normally remained underwater an average of 32% longer than those with impaired rebreathing, suggesting a functional role of rebreathing in underwater respiration. Unlike rebreathing, gular pumping was unaffected by treatment and may warrant further research regarding its role in supplementing underwater respiration. This study provides evidence that vertebrates can use bubbles to respire underwater and raises questions about adaptive mechanisms and potential bio-inspired applications.

The Impact of AI on Web Development

Artificial Intelligence (AI) has revolutionized many fields, with web development being a notable example. This paper explores the transformative impact of AI technologies on web development, examining how AI tools enhance design, streamline coding, and improve user experiences. We analyse the application of AI in automating repetitive tasks, personalizing content, and optimizing website performance. The study utilizes a review of current literature and case studies to highlight key advancements and emerging trends. Our findings suggest that AI not only accelerates development processes but also introduces new paradigms for creating dynamic and adaptive web applications. This paper concludes by discussing the implications of AI advancements for the future of web development and potential areas for further research.

A multi-level collaborative self-distillation learning for improving adaptive inference efficiency

A multi-exit network is an important technique for achieving adaptive inference by dynamically allocating computational resources based on different input samples. The existing works mainly treat the final classifier as the teacher, enhancing the classification accuracy by transferring knowledge to the intermediate classifiers. However, this traditional self-distillation training strategy only utilizes the knowledge contained in the final classifier, neglecting potentially distinctive knowledge in the other classifiers. To address this limitation, we propose a novel multi-level collaborative self-distillation learning strategy (MLCSD) that extracts knowledge from all the classifiers. MLCSD dynamically determines the weight coefficients for each classifier’s contribution through a learning process, thus constructing more comprehensive and effective teachers tailored to each classifier. These new teachers transfer the knowledge back to each classifier through a distillation technique, thereby further improving the network’s inference efficiency. We conduct experiments on three datasets, CIFAR10, CIFAR100, and Tiny-ImageNet. Compared with the baseline network that employs traditional self-distillation, our MLCSD-Net based on ResNet18 enhances the average classification accuracy by 1.18%. The experimental results demonstrate that MLCSD-Net improves the inference efficiency of adaptive inference applications, such as anytime prediction and budgeted batch classification. Code is available at https://github.com/deepzlk/MLCSD-Net.

Gendered implications for climate change adaptation among farmers in Madagascar

ABSTRACT Climate change is impacting farmers worldwide; none more so than the millions of smallholder farmers who rely entirely on rain-fed agriculture. Adapting agricultural practices is an important strategy to mitigate climate change’s harmful impacts to food security and livelihoods. Researchers have increasingly sought to understand the factors influencing farmer decision-making on adaptation practices but application of important theories in low-income country contexts is lacking. Therefore, we apply the Protection Motivation Theory (PMT) to examine farmer intention to adopt agricultural practices in response to climate change based on a survey of 328 smallholder farmers in rural Madagascar. Using structural equation modeling (SEM) to describe the relationship between social connectedness (e.g., interactions with others about farming-related issues), PMT constructs (threat and coping appraisal), and intention to adapt agricultural practices to climate change, we find that coping appraisal is a stronger predictor of intention to adapt than threat appraisal. Further, SEM results show a significant negative relationship between social connectedness and threat appraisal – farmers with greater social connectedness perceive climate change to be less of a threat. Additionally, as female farmers are significantly less socially connected than male farmers, this study provides further evidence of the gendered vulnerabilities to climate change among smallholders. This research has implications for the design of policies and interventions that consider farmer social networks, critical for reducing threat and building coping capacity, in supporting adaptation to climate change among vulnerable farmer populations.

Tremendous Stiffness‐Changing Polymer Networks Enabled by Touch‐Induced Crystallization

AbstractStiffness‐changing polymers are critically needed for intelligent and adaptive applications in soft robotics and wearable electronics; however, they suffer from the requirement of persistent external stimulation. Herein, a stiffness‐changing polymer network impregnated with a supercooled ionic liquid is introduced, which undergoes touch‐induced crystallization from a soft ionogel to a stiff plastic. The network exhibits a remarkable tunability in stiffness change, with the highest value exceeding 190 000 times between the soft and stiff states (3.5 kPa vs 691.1 MPa). This tremendous transition is achieved through the first‐order liquid–solid phase transition of the supercooled ionic liquid, which is highly reversible and isochoric via a heating‐cooling process. Unlike traditional stimuli, only a single touch is needed without sustained stimulation to trigger the stiffening from soft to stiff states. Importantly, the touch‐responsive polymer network can be extended to various types of ionic liquids and monomers, indicating excellent strategic versatility. Moreover, this unique ionogel exhibits switchable adhesiveness and ionic conductivity, effectively demonstrating smart adhesives and ionic switches with enhancements of more than 66 and 5800 times, respectively. This work provides a facile and versatile strategy for designing stable stiffness‐changing polymers, unlocking significant potential applications for next‐generation smart devices.

Linking environmental, social and economic sustainability. Case study: basin and reservoir of the Valle de Bravo Dam, Mexico

ABSTRACT Sustainability requires understanding and respecting the ecosystem functioning to plan the use of its resources. Improvised planning plus economic benefits can stimulate unsustainable decisions that lead to complications and obscures the application of appropriate adaptations when a change is required. This paper is a historical review of a reservoir when the dam changes its use from hydroelectric to water supply and then tourism is superimposed on the ecosystem. Data collected from the literature are systematically analyzed to see the effects on basin-reservoir dynamics. The main effect was the increase in the hydraulic retention time of the reservoir that facilitates the accumulation of nutrients and eutrophication, aggravated by the increase in the fixed and floating population stimulated by tourism. The consequent cyanobacteria blooms deteriorate the landscape, become a health risk and cancels the use of the reservoir to tourism. The increase in emerging compounds detected in the treatment plant effluent discharged into the reservoir poses a risk to aquatic species and the water supply. These results are analyzed in light of the objectives and dimensions of Sustainability. Recommendations include modifying Goal 1 of SDO 8, because it is shown that an annual growth of 7% of GDP in developing countries threatens their natural resources.

AI-Assisted Rational Design and Activity Prediction of Biological Elements for Optimizing Transcription-Factor-Based Biosensors.

The rational design, activity prediction, and adaptive application of biological elements (bio-elements) are crucial research fields in synthetic biology. Currently, a major challenge in the field is efficiently designing desired bio-elements and accurately predicting their activity using vast datasets. The advancement of artificial intelligence (AI) technology has enabled machine learning and deep learning algorithms to excel in uncovering patterns in bio-element data and predicting their performance. This review explores the application of AI algorithms in the rational design of bio-elements, activity prediction, and the regulation of transcription-factor-based biosensor response performance using AI-designed elements. We discuss the advantages, adaptability, and biological challenges addressed by the AI algorithms in various applications, highlighting their powerful potential in analyzing biological data. Furthermore, we propose innovative solutions to the challenges faced by AI algorithms in the field and suggest future research directions. By consolidating current research and demonstrating the practical applications and future potential of AI in synthetic biology, this review provides valuable insights for advancing both academic research and practical applications in biotechnology.