Molecular Imprinting Technology (MIT) is a technique to design artificial receptors with a predetermined selectivity and specificity for a given analyte, which can be used as ideal materials in various application fields. Molecularly Imprinted Polymers (MIPs), the polymeric matrices obtained using the imprinting technology, are robust molecular recognition elements able to mimic natural recognition entities, such as antibodies and biological receptors, useful to separate and analyze complicated samples such as biological fluids and environmental samples. The scope of this review is to provide a general overview on MIPs field discussing first general aspects in MIP preparation and then dealing with various application aspects. This review aims to outline the molecularly imprinted process and present a summary of principal application fields of molecularly imprinted polymers, focusing on chemical sensing, separation science, drug delivery and catalysis. Some significant aspects about preparation and application of the molecular imprinting polymers with examples taken from the recent literature will be discussed. Theoretical and experimental parameters for MIPs design in terms of the interaction between template and polymer functionalities will be considered and synthesis methods for the improvement of MIP recognition properties will also be presented.
Naser Hossein Motlagh, Mahsa Mohammadrezaei, J. Hunt
et al.
Integration of renewable energy and optimization of energy use are key enablers of sustainable energy transitions and mitigating climate change. Modern technologies such the Internet of Things (IoT) offer a wide number of applications in the energy sector, i.e, in energy supply, transmission and distribution, and demand. IoT can be employed for improving energy efficiency, increasing the share of renewable energy, and reducing environmental impacts of the energy use. This paper reviews the existing literature on the application of IoT in in energy systems, in general, and in the context of smart grids particularly. Furthermore, we discuss enabling technologies of IoT, including cloud computing and different platforms for data analysis. Furthermore, we review challenges of deploying IoT in the energy sector, including privacy and security, with some solutions to these challenges such as blockchain technology. This survey provides energy policy-makers, energy economists, and managers with an overview of the role of IoT in optimization of energy systems.
Abstract Much of the attention surrounding blockchain today is focused on financial services, with very little discussion about nonfinancial services firms and how blockchain technology may affect organizations, their business models, and how they create and deliver value. In addition, some confusion remains between the blockchain (with definite article) and blockchain (no article), distributed ledger technologies, and their applications. Our article offers a primer on blockchain technology aimed at general managers and executives. The key contributions of this article lie in providing an explanation of blockchain, including how a blockchain transaction works and a clarification of terms, and outlining different types of blockchain technologies. We also discuss how different types of blockchain impact business models. Building on the well-established business model framework by Osterwalder and Pigneur, we outline the effect that blockchain technologies can have on each element of the business model, along with illustrations from firms developing blockchain technology.
Eleftherios Fyntikakis, Spyridon Plakias, Themistoklis Tsatalas
et al.
Background: Strength training (ST) is essential for enhancing athletic performance and reducing injury risk, yet traditional monitoring relies heavily on subjective assessment, limiting objective and individualized evaluation. Objective: This scoping review critically synthesizes current applications of artificial intelligence (AI) and wearable technologies (WT) in ST, with emphasis on methodological approaches, data characteristics, explainability, and practical readiness. Methods: Searches of PubMed and Scopus identified 13 peer-reviewed studies (2015–2025). Evidence was charted and synthesized to compare AI models, wearable sensor configurations, validation strategies, and translational potential. Results: Studies employed classical machine learning, deep learning, and hybrid approaches alongside inertial, force, strain, and physiological sensors to support exercise classification, load estimation, fatigue detection, and performance monitoring. Deep learning models dominated movement recognition tasks, whereas simpler models often aligned better with small datasets and interpretability requirements. However, most studies relied on limited, homogeneous samples and internal validation, restricting generalizability and real-world applicability. Explainability was inconsistently addressed, particularly in higher-risk applications such as injury prediction. Conclusions: AI-enhanced wearables provide objective and individualized ST monitoring, but current evidence remains largely experimental. To ensure a practical application is implemented, standardized datasets, robust external validation, and greater integration of explainable AI are required to support and deliver trustworthy decision-making.
Abstract Leveraging on the mature processing infrastructure of silicon microelectronics, silicon photonic integrated circuits may be readily scaled to large volume production for low-cost high-volume applications such as optical transceivers for data centers. Driven by the rapid growth of generative artificial intelligence and the resultant rapid increase in data traffic in data centers, new integrated optical transceivers will be needed to support multichannel high-capacity communications beyond 1.6Tb/s. In this paper, we review some of the recent advances in high performance optical waveguide grating couplers (WGC) as a key enabling technology for future high capacity communications. We describe the novel use of shifted-polysilicon overlay gratings on top of the silicon grating that enabled foundry manufactured chips to have fiber-chip coupling losses of under 1 dB. The use of mirror symmetry and resonant cavity enhancement in the design of gratings can increase the 1-dB optical bandwidths of grating couplers to over 100 nm. Multimode waveguide grating couplers (MWGC) may be designed for the selective launch of different modes channels in multimode fibers for mode-division-multiplexing (MDM) communications. The use of different modes or polarizations in optical fibers for high capacity communications requires the unscrambling of data lanes which are mixed together during the optical fiber transmission. We describe how silicon photonic circuits can be used to perform unitary matrix operations and unscramble the different data lanes in multichannel optical communication systems. We also describe recent advances on high-speed silicon modulators for enabling data rates of individual data lanes in an integrated optical transceiver beyond 300 Gb/s.
In this paper, the spatiotemporal evolution characteristics of spike and modal-deep stall cells in a centrifugal compressor are studied at a small mass-flow rate. Three operating points are selected at different stall stages, and the internal unstable flow mechanism is deeply explored based on the downtrend of pressure ratio and isentropic efficiency on the external characteristic curve. The dimensionless mass flow rate (M*) is proposed to reflect the centrifugal compression rate of the whole machine under various stall conditions. A hump-like pulse is captured at deep stall conditions, and its fluctuation amplitude is an order of magnitude different from the near-stall to stall conditions. The fluctuation characteristics at different operating conditions are quantitatively analyzed from the perspective of the time domain. The fluctuation and spectral characteristics of flow field parameters at different stall conditions are obtained by collecting dynamic data of the flow field at different positions and performing a fast Fourier transform. The characteristic frequency of the flow field is closely correlated with the spatiotemporal flow structure. The fitting curve of static pressure distribution along the blade profile at deep stall conditions is increasingly divergent. The stall signals of two different modes are captured by the frequency-domain analysis of the pressure fluctuation on the inner flow surface of the impeller. The spatiotemporal correlation between the evolution of stall clusters in the impeller domain and the existence of vaneless diffusers and volutes is explored, from the axial flow of the impeller domain to circumferential flow. The stall mechanism of circumferential flow in the vaneless zone of the centrifugal compressor is deeply explored from the time domain and frequency domain based on the pressure fluctuation and its root mean square error value.
Self-centering structures have emerged as a promising seismic design solution, offering advantages in structural safety, rapid post-earthquake functionality recovery, and life-cycle economy. This paper introduces a self-centering beam–column joint that integrates superelastic shape memory alloys (SMAs) and prestressed steel tendons as restoring components. A numerical model was developed in OpenSees and validated against experimental results, with discrepancies in residual deformation within 10%. The validated model was used for parametric studies on strand area, prestress, and SMA configuration. The results show that the proposed joint sustains a maximum drift of 6% while maintaining nearly zero residual drift (less than 0.2%), and its hysteresis curve exhibits a stable flag-shaped pattern. The equivalent viscous damping ratio exceeds 0.1, confirming excellent deformation and energy dissipation capacities. These findings highlight the joint’s potential for application in seismic-resilient steel frames.
Boniphace Kutela, Frank Ngeni, Cuthbert Ruseruka
et al.
Over-speeding is a pivotal factor in fatal traffic crashes globally, necessitating robust speed management strategies to augment road safety. In 2021, the National Highway Traffic Safety Administration reported over 12 000 speed-related fatalities in the United States alone. Previous studies aggregated over-speeding tendencies; however, the extent of over-speeding has a significant implication on the crash outcome. This study delves into the prevalence and magnitude of over-speeding in various scenarios, utilizing data from traffic cameras in Edmonton, Canada, and employing a negative binomial statistical model for analysis. The model elucidates the significance and likelihood of over-speeding tendencies by incorporating temporal and built environment variables, i.e., year, month, number of lanes, dwelling unit types, school-related, and open green space. Study results indicated that the aggregation of the over-speeding data tends to underestimate the influence of various factors. Notably, the estimated impact of the posted speed limit for the disaggregated models is up to over two times that for the aggregated model. Further, the summer months exhibit a roughly 25% uptick in speed limit violations for aggregated models while about a 40% uptick in the speed limit violations for disaggregated approaches. Conversely, a discernible decline in over-speeding tendencies is observed with camera enforcement, showcasing a 25% reduction over four years. Built environment variables presented mixed results, with one-unit dwellings associated with a 12% increase in over-speeding, while proximity to schools indicated a 10% decrease. These pivotal findings provide policymakers and practitioners with valuable insights to formulate targeted interventions and countermeasures to curtail speed limit violations and bolster overall road safety conditions.
Cancer is a disorder characterized by the abnormal growth of cells that increases uncontrollably over an extended period of time. Treating cancerous brain tumors remains among the most challenging tasks for researchers, as brain tumors are among the hardest cancers to treat. Additionally, the condition often worsens because of the delayed diagnosis caused by the absence of early symptoms. The use of conventional treatment methods, such as radiation, chemotherapy, and surgery, continues to be highly limited. The low solubility, narrow therapeutic index, and limited ability to traverse the blood–brain barrier of most anticancer drugs result in limited therapeutic efficacy. In an attempt to overcome these predicaments, formulation scientists have been considering nanotechnology-based therapeutic solutions, particularly given the increasing rates of brain cancers that have low survivability and the drawbacks of the existing treatment methods. Different nanoplatforms, such as polymeric nanoparticles, nanoliposomes, dendrimers, carbon nanotubes, and magnetic nanoparticles, have been explored. Research has indicated that such nanocarriers can increase the delivery of drugs to cells in brain tumors with a minimal off-target distribution, resulting in minimal adverse effects and optimal treatment. This review presents a summary of nanocarrier-based drug delivery systems that have been reported in recent years for the treatment of brain tumors. In addition, it explains the existing difficulties with the clinical implementation of nanodrug carriers and the perspectives of this field.