In the leading/trailing edge’s adaptive machining of the near-net-shaped blade, a small portion of the theoretical part is retained for securing aerodynamic performance by manual work. However, this procedure is time-consuming and depends on the human experience. In this paper, we defined retained theoretical leading/trailing edge as the reconstruction area. To accelerate the reconstruction process, an anchor-free neural network model based on Transformer was proposed, named LETR (Leading/trailing Edge Transformer). LETR extracts image features from an aspect of mixed frequency and channel domain. We also integrated LETR with the newest meta-Acon activation function. We tested our model on the self-made dataset LDEG2021 on a single GPU and got an mAP of 91.9\%, which surpassed our baseline model, Deformable DETR by 1.1\%. Furthermore, we modified LETR’s convolution layer and named the new model after GLETR (Ghost Leading/trailing Edge Transformer) as a lightweight model for real-time detection. It is proved that GLETR has fewer weight parameters and converges faster than LETR with an acceptable decrease in mAP (0.1\%) by test results.

In this article, the main building of the former Uto City Hall, which was severely damaged in the 2016 Kumamoto earthquake, is investigated as a case study for the retrofitting of an irregular Reinforced Concrete building using the base-isolation technique. Its peak response is predicted via mode-adaptive bidirectional pushover analysis (MABPA), which was originally proposed by the authors. In the prediction step of MABPA, the peaks of the first and second modal responses are predicted considering the energy balance during a half cycle of the structural response. The numerical analysis results show that the peak relative displacement can be properly predicted by MABPA. The results also show that the performance of the retrofitted building models is satisfactory for the ground motion considered in this study, including the recorded motions in the 2016 Kumamoto earthquake.

Modelling errors, robust stabilization/tracking problems under parameter and model uncertainties complicate the control of the flexible underactuated systems. Chattering-free sliding-mode based input-output control law realizes robustness against the structured and unstructured uncertainties in the system dynamics and avoids excitation of unmodeled dynamics. The main purpose is to propose a robust adaptive solution for stabilizing and tracking direct-drive (DD) flexible robot arms under parameter and model uncertainties, as well as external disturbances. A lightweight robot arm subject to external and internal dynamic effects was taken into consideration. The challenges are compensating actuator dynamics with the inverter switching effects and torque ripples, stabilizing the zero dynamics under parameter/model uncertainties and disturbances while precisely track the predefined reference position. The precise control of this kind of system demands an accurate system model and knowledge of all sources that excite unmodeled dynamics. For this purpose, equations of motion for a flexible robot arm were derived and formulated for the large motion via Lagrange’s method. The goals were determined to achieve high-speed, precise position control, and satisfied accuracy by compensating the unwanted torque ripple and friction that degrades performance through an adaptive robust control approach. The actuator dynamics and their effect on the torque output were investigated due to the transmitted torque to the load side. The high-performance goals, precision&robustness issues, and stability concerns were satisfied by using robust-adaptive input-output linearization-based control law combining chattering-free sliding mode control (SMC) while avoiding the excitation of unmodeled dynamics.

Adaptive mechanisms, such as channel hopping and packet replication, are used in low-power wireless networks to deal with the spatial and temporal variations in the link quality, and meet the reliability requirements of industrial applications (i.e., PDR>99%). However, the benefits of such mechanisms are limited and may have a large impact on end-to-end latency and energy consumption. Hence, in this paper we propose using adaptive modulation diversity, which allows to dynamically select different modulations, to improve link reliability. We present three adaptive modulation diversity selection strategies and validate them using the data derived from a real-world deployment using the IEEE 802.15.4g SUN modulations (i.e., SUN-FSK, SUN-OQPSK and SUN-ODFM) in an industrial environment. The results show that by using adaptive modulation diversity it is possible to improve link reliability regardless of node conditions.

One of the most crucial and economically beneficial tasks for energy customer is peak load curtailment. On account of the fast response of renewable energy resources (RERs) such as photovoltaic (PV) units and battery energy storage system (BESS), this task is closer to be efficiently implemented. Depends on the customer peak load demand and energy characteristics, the feasibility of this strategy may warry. When adaptive neuro-fuzzy inference system (ANFIS) is exploited for forecasting, it can provide many benefits to address the above-mentioned issues and facilitate its easy implementation, with short calculating time and re-trainability. This paper introduces a data driven forecasting method based on fuzzy logic for optimized peak load reduction. First, the amount of energy generated by PV is forecasted using ANFIS which conducts output trend, and then, the BESS capacity is calculated according to the forecasted results. The trend of the load power is then decomposed in Cartesian plane into two parts, left and right from load peak, searching for BESS capacity equal. Network switching sequence over consumption is provided by a fuzzy logic controller (FLC) with respect to BESS capacity and PV energy output. Finally, to prove the effectiveness of the proposed ANFIS-based peak shaving method, offline digital time-domain simulations have been performed on a real-life practical test micro grid system in MATLAB/Simulink environment and the results have been experimentally verified by testing on a practical micro grid system with real-life data obtained from smart meter and also, compared with several previously-reported methods.

In this paper, we propose an adaptive duty-cycled hybrid X-MAC (ADX-MAC) protocol for energy-efficient forest fire prediction. The X-MAC protocol acquires the additional environmental status collected by each forest fire monitoring sensor for a certain period. And, based on these values, the length of sleep interval of duty-cycle is changed to efficiently calculate the risk of occurrence of forest fire according to the mountain environment. The performance of the proposed ADX-MAC protocol was verified through experiments the proposed ADX-MAC protocol improves throughput by 19% and was more energy-efficient by 24% compared to X-MAC protocol. As the probability of forest fires increases, the length of the duty cycle is shortened, confirming that the forest fires are detected at a faster cycle.

Land-cover classification that uses very-high-resolution (VHR) remote sensing images is a topic of considerable interest. Although many classification methods have been developed, there is still room for improvements in the accuracy and usability of classification systems. In this paper, a novel post-processing approach based on a dual-adaptive majority voting strategy (D-AMVS) is proposed for improving the performance of initial classification maps. D-AMVS defines a strategy for refining each label of a classified map that is obtained by different classification methods from the same original image and fusing the different refined classification maps to generate a final classification result. The proposed D-AMVS contains three main blocks. 1) An adaptive region is generated by extending gradually the region around a central pixel based on two predefined parameters (T1 and T2) in order to utilize the spatial feature of ground targets in a VHR image. 2) For each classified map, the label of the central pixel is refined according to the majority voting rule within the adaptive region. This is defined as adaptive majority voting (AMV). Each initial classified map is refined in this manner pixel by pixel. 3) Finally, the refined classified maps are used to generate a final classification map, and the label of the central pixel in the final classification map is determined by applying AMV again. Each entire classified map is scanned and refined pixel by pixel based on the proposed D-AMVS. The accuracies of the proposed D-AMVS approach are investigated through two remote sensing images with high spatial resolutions of 1.0 and 1.3 m, respectively. Compared with the classical majority voting method and a relatively new post-processing method called general post-classification framework, the proposed D-AMVS can achieve a land-cover classification map with less noise and higher classification accuracies.

This paper attempts at applying adaptive network-based fuzzy inference system (ANFIS) for analysis of the resonant frequency of a microstrip rectangular patch antenna with two equal size slots which are placed on the patch vertically. The resonant frequency is calculated as the position of slots is shifted to the right and left sides on the patch. As a result , the antenna resonates at more than one frequency . Commonly, machine algorithms based on artificial neural networks are employed to recognize the whole resonant frequencies. However ,they fail to estimate the resonant frequencies correctly as in some cases variations are not very sensible and the resonant frequencies overlap each other . It can be concluded that artificial neural networks could be replaced in such designs by the adaptive network-based fuzzy Inference system due to its high approximation capability and much faster convergence rate.

Multiple studies reported a negative relationship between the Honesty-Humility factor of the HEXACO model and psychopathy. The Durand Adaptive Psychopathic Traits Questionnaire (DAPTQ), which was developed to assess adaptive traits known to be related to psychopathic traits, has previously demonstrate positive relationships with all factors of the Big Five Model, at the exception of a negative relationship with neuroticism. The current study aims to validate the previously reported association between the DAPTQ and the five major components of the personality, while also examining its relationship with the Honesty-Humility factor as defined by the HEXACO model. The results (N = 171) support the good internal consistency, two weeks test-retest validity and inter-correlation of the DAPTQ. A Confirmatory Factor Analysis further supports the nine-factor model of the DAPTQ. When compared to the HEXACO, the DAPTQ did not display any relationship with the Honesty-Humility factor, nor the Agreeableness and Openness factors, but kept its similar association to Emotionality, Extroversion, and Conscientiousness as initially reported in its development phase. Overall, the results support the discriminant validity of the DAPTQ to assess adaptive traits related to the psychopathic personality without overlapping with psychopathic personality traits. Results are discussed in terms of implications and further improvements to validate the DAPTQ.

This paper presents the design and measurement of an IEEE 802.11ad standard compatible RF transceiver for 60GHz wireless communication system. In addition to the traditional half-duplex (HD) mode, this work supports full-duplex (FD) operation to deliver better channel utilization and faster response time for the system. The isolation between the transmitter and receiver from the architecture design to system integration for FD operation has been fully considered. A digital self-interference cancellation (DSIC) is implemented in MATLAB to verify the FD performance. The super-heterodyne architecture with an intermediate frequency (IF) of 12GHz is designed to suppress the image frequencies without using extra filters. A flexible phase-locked loop (PLL) synthesizer provides a local oscillator (LO) frequency with a 2kHz resolution. Other than the time division duplex (TDD) mode used in the conventional 60GHz system, a wide bandwidth baseband digital variable gain amplifier (DVGA) with a 3dB bandwidth of more than 4GHz also supports frequency division duplex (FDD) operation. The transceiver chip is fabricated in Tower Jazz 0.18µm SiGe BiCMOS process. With on-board antenna, the transceiver covers all four channels in 802.11ad standard with MCS-12 (7.04Gbps under 1.76GSym/s and 16-QAM) under 1.5m. In the proposed system design, the RF frontend based self-interference (SI) suppression from the local transmitter to receiver LNA is around 54dB. To achieve a practical FD application, the SI is further suppressed with the help of digital SI compensation. The measured power consumption for transmitter and receiver configuration is 194mW and 231mW, respectively, in HD mode and 398mW for FDD or FD operation mode.

Self-assembling peptides have emerged as promising materials for pharmaceutical and biomedical applications due to their unique biocompatibility, biodegradability, and multifunctionality. These properties make them ideal candidates for addressing the challenges posed by biofilm-associated infections linked to implanted medical devices. Additionally, these peptides can form a variety of nanostructures, including nanotubes, hydrogels, and nanosheets, which facilitate targeted drug delivery and enhance therapeutic efficacy. The successful translation of peptide-based materials into clinical applications is exemplified by products like RADA16 and lanreotide, which improve therapeutic outcomes and enhance patient compliance. Furthermore, the development of peptidomimetics extends the versatility of these materials across various biomedical applications. This review highlights the potential of peptide-based biomaterials as effective tools in combating infections and as innovative platforms for drug delivery, emphasising their significance in modern medical therapies.

Speech emotion recognition (SER) is important in facilitating natural human-computer interactions. In speech sequence modelling, a vital challenge is to learn context-aware sentence expression and temporal dynamics of para-linguistic features to achieve unambiguous emotional semantic understand-ing. In previous studies, the SER method based on the single-scale cascade feature extraction module could not effectively preserve the temporal structure of speech signals in the deep layer, downgrading the sequence modeling performance. In this paper, we propose a novel multi-scale feature pyramid network to mitigate the above limitations. With the aid of the bi-directional feature fusion of the pyramid network, the emotional representation with adequate temporal semantics is obtained. Experiments on the IEMOCAP corpus demonstrate the effectiveness of the proposed methods and achieve competitive results under speaker-independent validation.

The Chinese stock market, marked by rapid growth and significant volatility, presents unique challenges for investors and analysts. A-share stocks, traded on the Shanghai and Shenzhen exchanges, are crucial to China’s financial system and offer opportunities for both domestic and international investors. Accurate stock recommendation tools are vital for informed decision-making, especially given the ongoing regulatory changes and economic reforms in China. Current stock recommendation methods often fall short, as they typically fail to capture the complex inter-company relationships and rely heavily on financial reports, neglecting the potential of unlabeled data and historical price trends. In response, we propose a novel approach that combines graph-based structures with historical price data to develop self-learned stock embeddings for A-share recommendations. Our method leverages self-supervised learning, bypassing the need for human-generated labels and autonomously uncovering latent relationships and patterns within the data. This dual-input strategy enhances the understanding of market dynamics, leading to more accurate stock predictions. Our contributions include a novel framework for label-free stock recommendations with modeling stock connections and pricing information, and empirical evidence demonstrating the robustness and adaptability of our approach in the volatile Chinese stock market.

This study investigates the necessity of particle asymmetry for self-propulsion in nanomotors. While conventional wisdom posits that asymmetric designs are crucial for generating phoretic forces or localized bubble propulsion, recent research suggests that symmetrical particles may also exhibit motility. To address this debate, we developed a robust workflow for synthesizing gold grafted silica nanoparticles with precise control over size and shape, enabling the direct comparison of their motile behavior by dynamic light scattering and particle tracking velocimetry. Our results indicate that the inherent asymmetry generated during isotropic gold nanoparticle deposition onto silica surfaces may enable particle motility.

In Korea, substantial disparities exist in physical environments across regions, exacerbating the polarization between metropolitan and provincial areas, and urban and rural regions. This study examines the differences in physical activity and self-rated health of local communities in relation to satisfaction with the physical environment. By identifying and addressing these underlying causes of health disparities, the study provides foundational data to inform policy efforts. The study utilized data from the 2023 Community Health Survey conducted by the Korea Disease Control and Prevention Agency. It included 231,752 individuals aged 19 and older (105,754 men and 125,998 women) selected through a two-stage probability proportional systematic sampling method. One-on-one online interviews were conducted from May 16 to July 31, 2023. Physical environment was assessed based on public satisfaction with safety, natural and living environments, public transportation, and medical services. Study variables included subjective health status and physical activity, with sub-variables for physical activity encompassing the duration of vigorous and moderate physical activity, walking, and flexibility exercises. For data analysis, MANOVA and ANOVA were conducted, with Bonferroni correction for multiple comparisons. Considerable variations in physical activity were observed based on satisfaction with the local community environment. Regarding self-rated health, significant differences were found in safety levels, living environment, and medical services. Satisfaction with the physical environment positively influenced physical activity and self-rated health. Therefore, it is imperative to reduce disparities in the physical environment between regions and enhance residents' satisfaction.

This study assesses the effectiveness of HCV self-testing (HCVST) distribution models compared to provider-led HCV testing (PL-HCVT), including the standard of care facility-based HCVT (SOC-HCVT) and community-based organization HCVT (CBO-HCVT) among people who inject drugs (PWID) in Vietnam. A cross-sectional study was conducted as part of a larger implementation science study in Hanoi and Ho Chi Minh City from September 2023 to April 2024. We engaged 8 CBOs and 10 public and private clinics in offering choice of HCVST through community-based and facility-based distribution, and secondary distribution), or PL-HCVT via SOC-HCVT and CBO-HCVT. Among 1,165 PWID, 693 opted for HCVST and 472 for PL-HCVT. The proportion of first-time testers was significantly higher in HIVST community-based and secondary distribution as well as CBO-HCVT compared to SOC-HCVT and facility-based HCVST (87.1%, 93.3% and 82.7% vs. 41.9% and 35.7%, respectively; p < 0.001). Overall, HCV seropositivity rate was lower in HCVST than PL-HCVT, however, higher in HCVST facility-based and community-based distribution compared to HCVST secondary distribution (28.1% and 20.4% vs. 5.7%, respectively). Community-based HCVST was most effective, followed by facility-based HCVST and secondary distribution. Our findings indicate that HCVST effectively reaches first-time testers and results in more people diagnosed and treated among PWID.

A low cost, scalable and robust process has been proposed as innovative method for coating solar mirrors with a self-cleaning, transparent in the full solar range and versatile material based on auxetic aluminium nitrides, previously obtained on the laboratory scale. This work presents the scaling-up of fabrication process from laboratory to prototypal scale and pre-liminary results of outdoor self-cleaning solar mirror prototypes testing in the demonstrative CSP plant ENEASHIP located in Casaccia (Rome) ENEA research centre. Prototypes 50x40cm have shown stability in external conditions: no degradation occurs during the test campaign. Their washing restores the initial reflectance affected by soiling and the self-cleaning performance allows the utilization of a reduced quantity of water for cleaning operations with respect to uncoated glass of back surface mirrors. A similar self-cleaning AlN coating could be utilized on other solar components affected by soiling, such as glass envelopes of heat collecting elements, PV panels and everywhere are required self-cleaning performance combined to optical ones.

The paper examines the evolution and crucial role of energy communities in the context of the transition to renewable energy sources. Such communities are presented as an innovative and resilient response to the challenges posed by climate change. Their importance emerges from their ability to decentralise energy production, increasing local security and reducing dependence on non-renewable energy sources and external suppliers. However, despite the obvious benefits, the contribution highlights several challenges, such as the need for significant upfront investment and the presence of regulatory barriers that may hinder the integration of these solutions into existing energy systems. A detailed bibliometric analysis is used, focusing on specific research areas and using tools to map the most relevant keywords and emerging trends. The results of the bibliometric analysis highlight how energy communities are emerging as key players in the sustainability landscape, capable of addressing and overcoming contemporary environmental challenges. The research reaffirms the transformative potential of energy communities in promoting a more sustainable and resilient future.

This study aims to broaden the current understanding of the antecedents and consequences of green purchase intention within the realm of perceived green value (PGV) A mixed-method approach is deployed, integrating PLS-SEM and Necessary Condition Analysis (NCA) to examine the role of influence and interaction as key precursors to perceived responsibility and perceived self-efficacy, and explores how perceived responsibility and perceived self-efficacy impacts green purchasing intention, mainly through the mediation of perceived responsibility (PER) and perceived self-efficacy (PSE) using survey data gathered from consumers participating. The results indicate that PGV positively influences green purchase intention (GPI). Moreover, PER and PSE exhibit significant chain mediation effects, with the mediation effect of perceived environmental responsibility being particularly strong. It is recommended that the government intensify green disseminate and education, promote the establishment of certification and evaluation systems for electric vehicle products, and that electric vehicle factories emphasize the environmental attributes and economic interest of their products to encourage green purchasing behavior.

Improvement of hydrophysical properties and corrosion resistance of self-compacting concrete to the effects of alternate freezing-thawing and aggressive soils of southern and central Kazakhstan is of interest to a wide range of researchers from the side of practical application of the obtained results in construction practice. It is proposed to form a spatially reinforced fine crystalline structure of cement matrix with maximum dense packing by using a complex modifier (Hyperplasticiser + Polymer + Microsilica + Fibro fibers) in the composition of self-compacting concretes (SCC). The introduction of the calculated amount of the above additives increases the operational reliability of the current SCC compositions, increasing the water resistance to W16, frost resistance to F=500, increasing the compressive strength by 20%, reducing the mass loss of samples during corrosion leaching to 50%. It has been experimentally established that the proposed addition of complex modifier (Hyperplasticiser + Polymer + Microsilica + Fiber fibers) to the SCC composition allows obtaining self-compacting concrete of high quality with improved performance characteristics (compressive strength, water resistance, frost resistance and corrosion resistance). Studies have shown that the complex modifier-modified SCC compositions have a high degree of resistance in aggressive environments and leaching corrosion. Based on the results of the conducted tests, it is possible to recommend the obtained SCC compositions for production of building products working in the zone of alternating freezing-thawing and aggressive soils.

Citizen science substantially contributes to large-scale biodiversity monitoring and the enhancement of public literacy. While both of the contributions largely depend on the public’s self- regulated learning. In this study, we employed a mix- method approach, incorporating both a questionnaire survey of 133 participants and interviews with 12 volunteers, aiming to explore the feature of self- regulated learning in citizen science, as well as identify the main factors impacting it. The volunteers are all different ages, from different jobs, with varying educational backgrounds. The findings revealed that the educational background of volunteers did not significantly impact their self-regulated learning. However, an increased frequency of participation was linked to higher levels of self-regulated learning. Furthermore, achievement goal orientation mediates the prediction of self-regulation by scientific epistemology. These insights underscore the importance of enhancing citizen science's impact on scientific literacy by fostering self-regulated learning among participants. This can be achieved through explicit instruction in scientific epistemology and by fostering a sense of achievement to boost achievement goal orientations. Additionally, our study advocates for the promotion of social interaction between students and out- of- school citizen scientists in school-based citizen science projects. The main innovation of this study is to consider volunteers as learners, not merely as contributors of data.

Background: Dental education blends theoretical and practical training to prepare students for independent patient care. This study examines the confidence levels of dental students in Umeå regarding independent performance of general dentistry tasks, and whether this confidence changes after clinical training. Methods: Surveys were conducted before and after a summer clinical internship, with s tudents rating their comfort levels on a scale from 0 to 10 for various procedures. Results: Results from 64 initial surveys and 54 follow-up surveys showed no significant difference in confidence before and after the internship. However, confidence levels varied based on whether students had practiced specific tasks during the internship. Students felt least confident in managing acute dental trauma and treating cavities in young children, while they were most confident in tasks like obtaining radiographic surveys and debriding periodontally compromised dentition. Tooth extraction and dental fillings were the most practiced procedures during the internship. Conclusions: The study concludes that the internship between the ninth and tenth semesters does not notably boost students' confidence in specific dental procedures, possibly due to the short duration of the training. Further exploration, including input from clinical supervisors, is suggested to optimize clinical training in dental education.

Introduction The placement of implants is often hindered by a severely atrophic mandibular edentulous ridge. A sufficient volume of bone graft is crucial for successful implant restoration. The required bone graft volume directly correlates with the deficiency present in the edentulous ridge. If the residual crestal bone height is so low that the distance between the crestal ridge and the inferior alveolar nerve is less than 2 mm, a significant bone augmentation procedure is necessary. This procedure must ensure the implants are surrounded by a sufficient margin of bone, aiming for a final height of more than 8 mm and a width of more than 7 mm. Achieving primary closure with adequate soft tissue is vital for the success of the bone graft procedure. The key to successful bone grafting lies in the effective management of the soft tissue covering. Case Report This case report details the treatment of a patient with severely atrophic edentulous ridges on both sides, addressed using Self Inflating Tissue Expanders (SITEs) to facilitate bone grafting and implant placement. Following soft tissue expansion, bone grafting and implant placement were successfully performed. The implants supported a fixed restoration, which remained stable and complication-free over a seven-year observation period. Conclusion The SITE (Self-Inflating Tissue Expander) proved effective in obtaining a substantial amount of bone graft needed for implant installation. A seven-year follow-up confirmed the implants were well-functioning with stable bone support, healthy soft tissue coverage, and adequate vestibular depth.

In this essay, I revisit Neural Networks (NNs) through the lens of evolutionary dynamics. From the two most important features of NNs, I recover the two most general equations of evolutionary dynamics. These two equations may thus serve as a connection between NNs and a body of knowledge that has been built over multiple centuries and has found application across a vast range of disciplines. This connection may therefore also help explain why NNs with the two features in question are applicable across a much broader range of domains than initially envisioned.

It is known that breastfeeding difficulties are often seen in the first weeks of the postpartum period and that various factors are effective in the mother's early cessation of breastfeeding. Mothers' self-efficacy and perception of insufficient milk are among these factors. This study aimed to examine the effect of hot application applied to the breast with the help of Thera Pearl in the postpartum period on milk perception and postpartum breastfeeding self-efficacy. This is a randomized controlled study. The study was completed with a total of 64 puerperal mothers, 31 of whom were control and 33 were experimental. Personal Information Form, Breastfeeding Observation Form, Breastfeeding Self-Efficacy Scale and Insufficient Milk Perception Scale were used to collect data. As a result, self-efficacy and milk perception are important factors affecting breastfeeding. In this study, we found that Thera pearl application increased breastfeeding self-efficacy, but did not affect milk perception.

Background: Recent research suggests a concerning trend of non-suicidal self-injury (NSSI) and suicidal behaviors emerging at younger ages (as early as age 12). Early onset of NSSI is linked to more severe outcomes. While universal school-based prevention programs have shown promise in addressing suicidal behaviors, there's limited research on their effectiveness in preventing NSSI onset among adolescents. This study aims to evaluate the efficacy of a universal prevention program in schools for NSSI and mental complaints while enhancing resilience and mental health in 11-14-year-old adolescents. Methods: In total, 329 Flemish secondary school students (55.6% female), aged 11 to 14 years, participated in a 4-hour classroom universal prevention, with a focus on emotion regulation, mental health, and specific strategies to prevent NSSI and reduce stigma. For both the intervention and control group (N = 124), a pre-, post- and one-month follow-up questionnaire was administered, containing reliable and valid measures for NSSI and suicidality, emotion regulation, help-seeking behaviors, well-being, and psychological distress. Results: The prevention program effectively reduced NSSI and psychological distress, particularly for adolescents with a history of NSSI. Conclusions: These findings support previous research on the effectiveness of school-based programs in reducing mental complaints and suggest promising outcomes for NSSI prevention.

Background: Breast cancer remains a significant health concern among women globally. Despite advancements in awareness and diagnostic techniques, it persists as a leading cause of death, with profound impacts on affected individuals' quality of life. Primary and secondary prevention, including regular screenings and practices like Breast Self-Examination (BSE), are pivotal in ensuring early diagnosis. The NHS in Italy offers screenings for women aged 50-69 every two years, managed by the Local Health Authority. However, the participation rates, especially among the Chinese female population residing in Italy, are not well-understood. Methods: Using a snowball method, we electronically disseminated a survey to investigate how Chinese women living in Italy engage with available NHS screening programs. The survey also explores their practice of BSE, the use and impact of technological tools on prevention, and aims to understand their depth of knowledge and misconceptions about breast cancer. Results: The data revealed a significant gap in breast cancer screening adherence and knowledge among Chinese women in Italy, with a notable discrepancy between the general population and those who have previously encountered cancer. Conclusions: The results highlight the urgent need for interventions that are culturally sensitive, stressing that these actions are not only desirable but essential.

Walnut (Juglans regia L.) is a monoecious species and although it exhibits self-compatibility, presents incomplete overlap of pollen shed and female receptivity. Thus, cross-pollination is prerequisite for optimal fruit production. Cross-pollination can occur naturally by the wind, the insects, artificially, or by hand. Pollen has been recognized as one possible pathway for Xanthomonas arboricola pv. juglandis infection, a pathogenic bacterium responsible for walnut blight disease. Other than the well-known cultural and chemical control practices, artificial pollination technologies with the use of drones could be a successful tool for walnut blight disease management of orchards. Drones may carry pollen and release it over crops or mimic the action of bees or other pollinators. Although this new pollination technology could be regarded as a promising tool, pollen germination and knowledge of pollen as a possible pathway for the dissemination of bacterial diseases remain crucial information for design and manufacture of aerial pollinator robot for walnut trees. Thus, our purpose was to describe a pollination model with fundamental components, the identification of the “core” pollen microbiota, specify an appropriate flower pollination algorithm, design an autonomous precision pollination robot, and minimize the average errors of flower pollination algorithm parameters through machine learning and meta-heuristic algorithms.

This article introduces a new physically based Lyapunov function difinition, which can be used as acoustic energy for finite amplitude pressure oscillations in reacting systems especially in combustion chambers. Reacting flow is seen as an open, non-equilibrium (parameters are distributed unevenly locally) thermodynamic system. This Lyapunov function is defined as the maximum mechanical work, which could be extracted by a heat engine from studied system, if this system would be disconnected from inlet and outlet and from any other surrounding environment, and the engine could transfer to the surrounding environment only mechanical work.

The COVID-19 pandemic has disrupted healthcare worldwide, potentially impacting disease management. This cross-sectional study assessed the influence of COVID-19 on diabetes self-management in 341 Saudis with type 2 diabetes using the Diabetes Self-Management Questionnaire (DSMQ). The DSMQ contains 16 items across 4 domains assessing glucose management, dietary control, physical activity, and healthcare use. Three additional items measured the perceived overall impact of COVID-19. Total DSMQ scores were significantly higher in patients aged &gt;60 years versus younger groups. Scores were significantly lower in patients diagnosed for 1-5 years versus longer durations. Patients on insulin had higher glucose management subscores than oral medication users. Overall, DSMQ scores were higher than the pre-pandemic Saudi population and Turkish post-pandemic findings. DSMQ results suggest that while COVID-19 negatively impacted some self-management domains, Saudi patients upheld relatively good diabetes control during the pandemic. Further research is warranted on specific barriers to optimize diabetes care during public health crises.

Advances in biomimetic triboelectric nanogenerators (TENGs) have significant implications for electronic skin (e-skin) and human-machine interaction (HMI). Emphasizing the need to mimic complex functionalities of natural systems, particularly human skin, TENGs leverage triboelectricity and electrostatic induction to bridge the gap in traditional electronic devices' responsiveness and adaptability. The exploration begins with an overview of TENGs' operational principles and modes, transitioning into structural and material biomimicry inspired by plant and animal models, proteins, fibers, and hydrogels. Key applications in tactile sensing, motion sensing, and intelligent control within e-skins and HMI systems are highlighted, showcasing TENGs' potential in revolutionizing wearable technologies and robotic systems. This review also addresses the challenges in performance enhancement, scalability, and system integration of TENGs. It points to future research directions, including optimizing energy conversion efficiency, discovering new materials, and employing micro-nanostructuring techniques for enhanced triboelectric charges and energy conversion. The scalability and cost-effectiveness of TENG production, pivotal for mainstream application, are discussed along with the need for versatile integration with various electronic systems. The review underlines the significance of making bioinspired TENGs more accessible and applicable in everyday technology, focusing on compatibility, user comfort, and durability. Conclusively, it underscores the role of bioinspired TENGs in advancing wearable technology and interactive systems, indicating a bright future for these innovations in practical applications.

Self-compacting concrete (SCC) is a high-quality construction solution combining high fluidity, passing and filling ability with improved mechanical properties and durability. In the present study, the effect of incorporating alternative waste materials, such as two by-products of the ce-ment industry, namely cement kiln dust (CKD) and by-pass dust (BPD), into SCC as partial re-placement for traditional filler material was investigated. The produced compositions were compared with reference mixtures containing only marble powder (MP) as a filler. A series of tests including specific test methods for wet SCC, compressive, flexural and tensile splitting strength tests as well as drying shrinkage determination were conducted to evaluate the quality of the produced SCC in terms of fresh and hardened SCC properties. The use of alternative fine filler materials resulted in a high-performance sustainable SCC of low cement content. Specifi-cally, incorporating CKD into the SCC enhanced its rheological behavior and improved slightly its mechanical properties, while the use of BPD led to SCC mixtures of adequate rheological characteristics but with highly upgraded mechanical and physical properties

We report a detailed investigation of vibrational modes, structure, and dynamics of glutathione (GSH) solutions using ultrasonic relaxation spectroscopy, FT-IR vibrational spectroscopy and electronic absorption measurements. The experimental data were analyzed in view of density functional theory (DFT) and molecular docking calculations. Three distinct Debye-type relaxation processes are resolved in the acoustic spectra, which are assigned to conformational changes between GSH conformers, self-association of GSH, and protonation processes. The standard volume changes for each process were estimated both experimentally and theoretically revealing a close resemblance among them. The higher the effect of the relaxation process in the structure, the greater the induced volume changes. From the temperature dependence of specific acoustic parameters, the thermodynamic characteristics of each process were determined. The experimental FT-IR spectra were compared with the corresponding theoretically predicted vibrational spectra revealing that the GSH dimers and extended conformers dominate the structure of GSH solutions in the high concentration region. The absorption spectra in the ultraviolet region confirmed the gradual aggregation mechanism that takes place in the aqueous GSH solutions. The results of the present study were discussed and analyzed in the framework of the current phenomenological status of the field.

Resistive random-access memory has emerged as a promising non-volatile memory technology, garnering substantial attention due to its potential for high operational performance, low power consumption, and scalability. Two-dimensional nanostructured materials play a pivotal role in RRAM devices, offering enhanced electrical properties and physical attributes that contribute to overall device improvement. In this study, self-rectifying switching behavior in RRAM devices is proposed based on molybdenum disulfide nanocomposites decorated with palladium on SiO2/Si substrates. The integration of Pd and MoS2 at the nanoscale effectively mitigates leakage currents decreasing from cross-talk in the RRAM array, eliminating the need for a separate selector device. The successful demonstration of the expected RRAM switching behavior follows the application of a Pd nanoparticle coating. The fabricated Pd-MoS2 synaptic device showed a high current ratio for forward/reverse current higher than 60 at a low resistance state and observed a memory on/off ratio of 103 performing stable resistance switching behavior.

Ionic gel electrolyte retains the characteristics of non-volatilization, non-flammability and outstanding electrochemical stability of ionic liquid, and shows good electrochemical performance combined with the excellent characteristics of different matrix materials, which is considered to be the best choice to achieve the high energy density and safety at the same time. In this paper, a flexible and self-healing ionic gel electrolyte is prepared by solvent-assisted method based on a zteric ion (ZI) copolymer. Abundant hydrogen bonds and synergistic interaction of ions in the electrolyte system endowed it with wonderful self-healing ability. The ionic conductivity of 9.06×10-4 S cm-1 at room temperature was achieved. Besides, the tLi+ was also increased to 0.312. The self-healing function of the ionic gel electrolyte ensured the long-term tolerance of the SHIGE. The capacity retention rate of the Li//LiFePO4 battery is 96% after 155 cycles at 0.1 C at 60 oC. This polymer electrolyte is expected to solve the problem of increasing polarization，which is caused by the low lithium ions migration number in ionic liquid electrolyte. And ultimately, it gives rise to a good rate performance.

The implementation of residential distributed generation (DG) in South America has shown significant growth in the last decade, driven by increased research on photovoltaics and renewable energy sources. Regulatory policies and business models have been crucial in fostering the adoption of DG in the region, with residential self-supply being the most receptive sector for implementing the concept. This article presents a literature review on DG as applied to residential self-supply in South America by comprehensively analyzing documents published between 2013 and 2023, applying the PRISMA methodology. In total, 37 literature resources have been systematically selected and reviewed to contribute to the identification of trends and developments in the field of DG in the residential sector, as well as highlighting areas that require further research and attention from stakeholders, including policymakers, investors, technology providers and consumers in the South American context.

This study examines the behavior of stearic acid (SA) and 12-hydroxystearic acid (12-HSA) aqueous mixtures as a function of temperature and 12-HSA/SA molar ratio (R). Whatever R, the system exhibits a morphological transition at a given threshold temperature, from multilamellar self-assemblies at low temperature towards small micelles at high temperature, as shown by a combination of transmittance measurements, Wide Angle X-ray diffraction (WAXS), small angle neutron scattering (SANS) and differential scanning calorimetry (DSC) experiments. Such transition originates from the melting of the crystallized fatty acids within the lamellar phase. The precise determination of the threshold temperature, which ranges between 20°C and 50°C depending on R, allows for the construction of the whole phase diagram of the system as a function of R. At high temperature the micelles that are formed are oblate for pure SA solutions (R = 0) and prolate for pure 12-HSA solution (R = 1). In the case of mixtures, there is a progressive continuous transition from oblate to prolate shape when increasing R, with micelles that are almost purely spherical for R = 0.33.

The presented research focused on the evaluation of the novel concept of overmolding technique using self-reinforced composite prepregs and recycled polymer blends. In order to evaluate the effectiveness of the proposed manufacturing technique, several series of materials based on polycarbonate/polyethylene terephthalate (PC/PET) and polycarbonate/polyethylene terephthalate glycol (PC/PETG) blends were prepared. The reinforcing component in the form of overmolded prepreg was made from polyester-based self-reinforced composite (srPET). The prepared materials were compared in terms of mechanical properties and heat resistance; the study was supplemented by thermal analysis measurements (DMTA, DSC). Considering the mechanical characteristics, the overmolding technique turns out to be an effective method of improving the properties of composites, and the increase in impact strength turns out to be particularly beneficial. The increase of the impact strength for overmolded PC/PET blend reached 430 % for PC/PETG sample 330% , while for PC-based composite, only 100%. The expected improvement in thermomechanical properties turned out to be difficult to achieve due to the rapid softening of the srPET prepreg at around 70 °C. However, technological tests and analysis of properties clearly indicated that the use of PC-based blends makes it possible to create a permanent connection with reinforcement based on srPET prepregs, which can significantly expand the potential of applications of this type of materials.

The potential use of nanostructured dipeptide self-assemblies in materials science for energy harvesting devices is a highly sought-after area of research. Specifically, aromatic cyclo-dipeptides containing tryptophan have garnered attention due to their wide-bandgap semiconductor properties, high mechanical rigidity, photoluminescence, and nonlinear optical behavior. In this study, we present the development of a hybrid system comprising biopolymer electrospun fibers incorporated with the chiral cyclo-dipeptide L-Tryptophan-L-Tyrosine. The resulting nanofibers are wide-bandgap semiconductors (bandgap energy 4.0 eV) and consist of self-assembled nanotubes embedded within a polymer matrix, exhibiting intense blue photoluminescence. Moreover, the cyclo-dipeptide L-Tryptophan-L-Tyrosine incorporated into polycaprolactone nanofibers displays a strong effective second harmonic generation signal of 0.36 pm/V and also shows notable piezoelectric properties with a high effective coefficient of 22 pCN−1. These hybrid systems hold great promise for applications in the field of nanoenergy harvesting and nanophotonics.

Evidence of the efficacy and safety of colorectal stent placement for palliation remains insufficient. This single-arm, prospective, multicenter study with WallFlex enteral colonic stent included 200 consecutive patients with malignant large bowl obstruction in the palliation cohort. The technical and clinical success, as well as stent patency and complications as short-term (≤ 7 days) and long-term (&gt; 7days) outcomes, of high axial force self-expandable metal stent (SEMS) placement was evaluated. The technical and clinical success rates were 98.5% and 94.5%, respectively. Non recurrent colorectal obstruction at 1 year was 63.9%, and 71.2% of the patients remained free of recurrent colorectal obstruction until death or the last follow-up. Fifty-six patients (28.0 %) received chemotherapy and five patients were administered bevacizumab after stent placement. The overall complication rate was 47%, including four (2.0%) early-onset and ten (5.0%) late-onset perforations, mostly due to stent-edge injury. Only the use of a long SEMS was a risk factor for perforation. In conclusion, endoscopic colorectal stenting using high axial force SEMS is an effective and safe procedure for palliation in patients with malignant colorectal obstruction. However, care should be taken to avoid perforation at the stent edge when using a long SEMS.

We describe the behavior of mixtures of stearic acid (SA) and its hydroxylated counterpart 12-hydroxystearic acid (12-HSA) in aqueous mixtures at room temperature, as function of the 12-HSA/(fatty acid) mole ratio R. The morphologies of the self-assembled aggregates are obtained by a multi-structural approach that combines confocal and cryo-TEM microscopies with Small-Angle Neutron Scattering (SANS) and Wide-Angle X-Rays Scattering (WAXS) measurements, coupled with rheology measurements. Fatty acids are solubilized by an excess of ethanolamine counterions, so that their heads are negatively charged. The behavior is driven by the competition between mixing entropy and enthalpic effects. A clear trend towards partitioning between the two types of fatty acids is observed, presumably driven by the favorable formation of a H-bond network between hydroxyl OH function on the 12th carbon. For all R, the self-assembled structures are locally lamellar, with bilayers composed of crystallized and strongly interdigitated fatty acids. At high R, multi-lamellar tubes are formed, similarly as for pure aqueous solutions of 12-HSA. The doping by a low amount of SA molecules only slightly modify the dimensions of the tubes (radius and length), and slightly decreases the bilayers rigidity. The solutions have a gel-like behavior originating from entanglements between tubes and show a yield stress. At intermediate R, a partial local partitioning occurs between the two types of fatty acids. The tubes coexist in solution with helical ribbons. The two structures possibly merge to form very long snaky objects with straight and curved parts. The rigidity of the bilayers and the rheological behavior of the solutions are very similar to those of the high R case. At low R, local partitioning also occurs, and the architecture of the self-assemblies associates the two morphologies of the pure fatty acids systems, respectively 2-D planar bilayers for SA and the tubes describe above for the 12-HSA molecules: they are facetted objects with planar domains enriched in SA molecules, capped with curved domains enriched in 12-HSA molecules. The rigidity of the bilayers is strongly increased, as well their storage modulus, compared to systems with larger R. The solutions remain however viscous fluids in this regime of low R.

Self-healing ceramic composites are promising smart materials for high-temperature applications. To better understand their behaviors under service conditions, numerous studies have been performed both experimentally and numerically, and kinetics parameters such as activation energy and frequency factor are reported to be indispensable for investigating healing phenomena. Here, we propose a method for determining the kinetic parameters of self-healing ceramic composites based on the oxidation kinetics model of strength recovery. These parameters are determined by an optimization method using experimental strength recovery data under various healing temperatures and times and microstructural features on the fractured surfaces. Alumina and mullite matrix-based self-healing ceramic composites, such as Al2O3/SiC, Al2O3/TiC, Al2O3/Ti2AlC (MAX phase), and mullite/SiC, were selected as target materials. The theoretical strength recovery behaviors of the cracked specimens obtained from the determined kinetic parameters were compared with the experimental results. The determined parameters are within the previously reported range, and the predicted strength recovery behaviors are reasonably agreed with the experimental values. The proposed method can also be applied to other self-healing ceramics with matrices reinforced with different healing agents to evaluate oxidation rate, crack healing rate, and theoretical strength recovery behaviors for designing self-healing materials used in high temperature applications. Furthermore, the healing ability of composites can be fairly discussed regardless of the type of strength recovery test.

The purpose of this study is to develop and evaluate a self-microemulsifying drug delivery system (SMEDDS) to improve the oral absorption of poorly water-soluble Olaparib. Through the solubility test of Olaparib in various oils, surfactants and co-surfactants, pharmaceutical excipients for use in SMEDDS manufacturing were selected. Self-emulsifying regions were identified by mixing the selected materials at various ratios, and a pseudoternary phase diagram was constructed by synthesizing these results. Various physicochemical properties of microemulsion incorporating Olaparib were confirmed by investigating the particle morphology, particle size, zeta potential, drug content and stability. In addition, the improved dissolution and oral absorption of Olaparib were also confirmed through a dissolution test and a pharmacokinetic study. An optimal microemulsion was generated in the formulation of Capmul MCM EP/NF 10%, Labrasol 80% and PEG 400 10%. Fabricated nano-sized microemulsions were well-dispersed in aqueous solutions, and it was also confirmed that it was maintained well without any problems of either physical or chemical stability. The 5 min initial dissolution of Olaparib in fabricated formulations was improved by 10.6 times (pH1.2), 14.8 times (pH6.8) and 14.8 times (distilled water) compared to the values of Olaparib powder. The dissolution at 120min was 45.4±4.0%(pH1.2), 53.5±1.1% (pH6.8) and 58.1±1.00% (distilled water) in Olaparib powder but has been significantly improved by 1.7 times (74.8±1.9%), 1.6 times (84.2±0.3%) and 1.5 times (81.2±0.7%) in SMEDDS. Associated with the high dissolutions of Olaparib in fabricated microemulsions, the pharmacokinetic parameters were also greatly improved. The Cmax and AUCinf of Olaparib in the prepared microemulsion were increased 4.2- and 3.2-fold, respectively, compared to powder. Taken together with the results mentioned above, the microemulsion system could be an effective tool as an oral delivery formulation for Olaparib. Additionally, our results demonstrate that the SMEDDS systems comprising Capmul MCM, Labrasol and PEG 400 could be a useful option for improvement of several other poorly water-soluble drugs with physico-chemical properties similar to Olaparib.

Abstract— In this paper, degradation effects such as the self-heating effect and the DIBL effect in 2D-MoS2-based MOSFETs are investigated through simulations. These are transistors with Al2O3 and HfO2 as the gate oxide and SiO2 and HfO2 as the back oxide (BOX). The self-heating effect (SHE) is simulated using the thermodynamic transport model. The dependence of the DIBL effect (Drain Induced Barrier Lowering) and the lattice temperature in the center of the channel on the gate length for transistors with different gate oxide and BOX materials is considered. Transistors are considered where the channel is fully and partially (just below the gate) covered by gate oxide. It is shown that the transistors with Al2O3 as gate oxide and SiO2 as BOX materials have higher immunity to DIBL effect and transistors with HfO2 as gate oxide and HfO2 as BOX materials have higher immunity to SHE.

This study considers the change-point test problem for time series based on the self-normalization ratio statistic test, which is constructed using residuals obtained from a support vector regression (SVR)-autoregressive moving average (ARMA) model. Under the null hypothesis, the series is a stationary process, and our test statistic converges to a non-degenerate distribution. Under the alternative hypothesis, there are change-points in the time series, and the self-normalization test statistic diverges to infinity. The simulations show that our proposed new test has better finite sample performance than other SVR-based tests in the literature. Finally, we illustrate its usefulness by analyzing two actual data sets.

Human Papillomavirus (HPV) self-sampling has the potential to increase cervical cancer screening (CCS) and reduce the cervical cancer burden in Medically Underserved Women (MUW). However, interventions promoting self-sampling are limited. We examined the effectiveness of an intervention study in increasing CCS among MUW. We conducted a quasi-experimental intervention study. Face-to-face verbal approach was to recruit MUW (n=85, mean age 48.57±11.02) living in a small city in the US. Behavioral intervention based on reframing, reprioritizing, and reforming (3R model) was used to educate the women about CCS in a group format. The women completed pre-and post-intervention assessments and followed-up interviews. The primary outcome was CCS uptake. Mixed methods analyses were conducted using a t-test for the primary outcome, PROCESS for mediation analysis, and NVivo for interview data. Majority of women (75%) completed self-testing. High-risk HPV prevalent among the women was 11%, and of those, 57% followed-up with physicians for care. We found that the significant increase in the women’s post-intervention screening behaviors was mediated by the increase in knowledge (Indirect Effect [IE] = .1314; 95% CI, .0104, .4079) and attitude (IE = .2167; 95% CI, .0291, .6050) scores, (p&lt;0.001). Interview analyses offered further explanations (see the explanations in parenthesis) why MUW found the intervention messages acceptable (encourages proactive behavior), feasible (simple and easy to understand), and appropriate (helpful and informative). Barriers including lack of trust and fear of results were identified. The findings suggest that an intervention that combines the 3R model and self-sampling may increase CCS among MUW.

Objective: Physical inactivity is linked to chronic illnesses and disabilities among workers, especially those in high demanding jobs like teachers. Despite the global prominence of sedentary behaviour research, studies drawing the relationships between physical inactivity and multimorbidity among working teacher populations in low-and middle-countries remain untapped. This study assessed the sedentariness and health status of primary school teachers in Cape Coast Metropolis in Ghana. Method: This cross-sectional survey employed 1109 primary school teachers from the Cape Coast Metropolis in the Central Region of Ghana, using convenient sampling method. Results: Generally, the teachers were highly sedentary and reported poor health status. Other results showed no difference in sedentariness across gender, t (1107) = -0.32, p > .05. However, female teachers suffer more pain and discomfort, [t (1103.51) = - 3.07, p < .05, anxiety and depression, [t (1099.56) = - 2.85, p < .000,)], and poor health status [t (1107) = 2.14, p < .05, than their male counterparts. Also, pain and discomfort, anxiety and depression, health status and years of work significantly predicted sedentariness among the teachers, F (4, 1104) = 5.966, p=.00, R = .145, R2 = .029, adjusted R2 = .018. Conclusion: The findings suggest that individualized or personalized interventions are urgently needed to promote regular physical activity to improve the health status and reduce associated complications on the health and well-being, especially among the female primary school teachers. Given the health risks of sedentary lifestyle, behavioral interventions at the person-level (i.e., individualized- routine weekly physical activity programmes) and built environmental restructuring (e.g., creation of walkways to encourage regular walking) could be done to physical activity behavior among teachers within the Cape Coast Metropolis, and perhaps beyond.

To reveal the influence of variables such as entrepreneurial learning, entrepreneurial inspiration, and incubation resources on students' entrepreneurial intentions in vocational colleges, this study took vocational colleges in Zhejiang Province, a province with a strong entrepreneurial climate in China, as an example. The study used a questionnaire to obtain data, and 614 valid questionnaires were collected from seven vocational colleges. The study divided entrepreneurship education into three dimensions: entrepreneurial learning, entrepreneurial inspiration, and incubation resources, and used structural equation modeling to investigate the influence of entrepreneurship education on entrepreneurial intentions while introducing entrepreneurial efficacy as a mediating variable to examine the influence mechanism of entrepreneurship education on entrepreneurial intentions of higher education students. The results showed that entrepreneurial learning, entrepreneurial inspiration, incubation resources, and entrepreneurial efficacy were all significantly and positively correlated with entrepreneurial intentions and that entrepreneurial efficacy mediated between the two dimensions of entrepreneurship education (entrepreneurial learning and entrepreneurial inspiration) and entrepreneurial intentions. Therefore, to enhance the effectiveness of entrepreneurship education in vocational institutions, we should stimulate students' sense of entrepreneurial efficacy, strengthen their entrepreneurial learning and inspire them to start their businesses. In turn, university incubation resources can be developed and utilized to enhance students' entrepreneurial confidence and intentions.

The system identification problem with multiple nonlinearities is relevant. Its decision depends on many factors. These include: feedbacks, the method of connecting nonlinear links, signal properties. They affect the identifiability of the system parameters. We introduced a condition for the excitation constancy for state variables, which considers the S-identifiability of the system. We propose system decomposition by measuring input to identify parameters. Each subsystem has an implicit identification representation. It guarantees obtaining estimates of subsystem parameters based on experimental data. The trajectories boundedness of adaptive system proved in parametric and coordinate spaces. Conditions guaranteeing exponential stability of the system obtained. Systems of self-oscillation generation and nonlinear correction of a nonlinear system consider. Conditions for the trajectories boundedness of the adaptive system obtained for these cases. The influence of nonlinearity and feedback on the system performance estimated.

In 2018, the author identified a previously unknown/unreported association between dexrazoxane and poly(ADP-ribose) (PAR). Interestingly, PAR is a close structural analogue of the polyadenine nucleotide polymer, polyadenosine monophosphate (poly(A)). In this report, subsequent in silico modelling of the interaction between dexrazoxane and poly(A) reveals some notable differences from the previously reported interaction between dexrazoxane and PAR. Significantly, the supramolecular self-assembly of dexrazoxane and poly(A) is distinguished by vertically-orientated nonelectrostatic forces comparable to the stabilizing interactions between stacked bases within DNA. Notably, the vertical separation of 3.4 Å between each stack is consistent with solvent entropy as a dominant driving force in stabilising the interaction. Additionally, concomitant conformational analysis by the author reveals the existence of low energy planar conformers of dexrazoxane. This analysis enables an explanation for the considerable discrepancies and conflicts that exist within the reported pharmacokinetic data for dexrazoxane. Exploring the significance of the interaction between dexrazoxane and poly(A), the author illustrates that survival, translation and replication of the severe acute respiratory syndrome virus 2 (SARS-CoV-2) is absolutely dependent upon the mature and unhindered poly(A) tail of the SARS-CoV-2 genome. The proposition herein, that dexrazoxane, as a chameleonic agent sequesters the poly(A) tail of the SARS-CoV-2 genome by the catalysis of a supramolecular hybrid assembly establishes SARS-CoV-2 infected cells as deep compartments for the accumulation of dexrazoxane. Taken together, dexrazoxane or its demethylated analogue, represent a novel treatment to kill the SARS-CoV-2 virus by irreversible destabilization of the SARS-CoV-2 poly(A) tail.

We evaluated hexadecyltrimethylammonium bromide (HTAB) for use in radio frequency (RF) applications in which liquid crystals (LCs) are used in layered structures that have various cell gap conditions. HTAB is a surfactant that can self-align vertically on the surface of indium tin oxide (ITO) substrates, and can induce homeotropic alignment of the LCs. For implementing RF devices with HTAB and LCs, we should consider limitations caused by the design conditions which are different from conventional liquid crystal displays such as cell gap. We quantified the concentration of HTAB ([HTAB]) that is necessary to form and maintain a sufficiently dense vertical alignment of 5CB (4-Cyano-4'-pentylbiphenyl). The required [HTAB] for full-homeotropic alignment was proportional to the cell gap until it was too large to support the transfer of the surface alignment to the LC, due to the weak anchoring nature of HTAB. We also showed the phase-change characteristic of the LC mixture depended on [HTAB] for the design of RF devices driven by light or heat. This work may help to guide the development of new approaches to designing efficient RF devices that use LCs.

Background: Current drug for the treatment of endometriosis is not able of completely cure and significant side effects hinder the continuation of treatment. So, the search for new drug candidates is necessary, and the use of plants extracts is highlighted. Babassu oil and copaiba oilresin have several therapeutic properties. We investigated the in vitro effects of two nanoemulsions containing babassu oil (Orbignya speciosa) (SNEDDS-18) and/or copaiba oilresin (Copaifera langsdorffii) (SNEDDS-18/COPA) on cultured human eutopic endometrium stromal cells from endometrial biopsies of patients without (CESC) and with (EuESC) endometriosis, and human stromal cells from biopsies of endometriotic lesions (EctESC). Methods: CESC, EuESC and EctESC were established and treated with SNEDDS-18 and SNEDDS-18/COPA to evaluate its effects on cytotoxicity, cell morphology, proliferation, and signaling pathways. Results: After 48 hours incubation with SNEDDS-18 and SNEDDS-18/COPA, cell viability and proliferation were inhibited, especially in EctESC. The lowest concentration of both nanoemulsions reduced cell viability and proliferation and breakdown the cytoskeleton in EctESCs. After 24h treatment it was observed a decrease in IL-1, TNF-α, and MCP-1, and an increase in IL-10 production. Conclusions: Both nanoemulsions can affect the endometriotic stromal cell behaviors, thus revealing two potential candidates for new phytotherapeutic agents on the management of endometriosis.

I present an experimental paradigm to explore the interpersonal dynamics generating a collective mind. I hypothesized that collective organization is based on dual interpersonal modes: (1) symmetrical and (2) anti‑symmetrical. I specified the geometric topology of these modes by detecting the spatiotemporal patterns that embed cooperative agents in a three‑dimensional matrix. I found that the symmetrical mode is executed automatically and without guidance. Conversely, the anti‑symmetrical mode required explicit direction and recruited attention for execution. I demonstrate that self‑other mirror‑symmetry stabilized group dynamics, enabled fast and efficient symmetrical imitation that optimized information transmission, whereas anti‑symmetrical imitation was comparatively slow, inefficient, and unstable. I determined that the anti‑symmetrical mode spontaneously transitioned to the symmetrical mode under perturbations. Crucially, this renormalization mechanism never transitioned from symmetrical to anti‑symmetrical. These self-organizing dynamics speak to interpersonal symmetry‑breaking. In the present work, spontaneous group choice mandated that agents synchronize cooperative cycles in symmetrical space under internal or external perturbations. I provide examples to illustrate that this self-regulating pullback attractor manifests in invertebrates and vertebrates alike. I conclude by suggesting that inter‑agent symmetry provides the social stability manifold through which attention-driven interactions enable intrapersonal and interpersonal change.

Diabetes mellitus (DM) and obesity account for the highest burden of non-communicable diseases. There is increasing evidence showing therapeutic patient education (TPE) as a clinically and cost-effective solution to improve biomedical and psychosocial outcomes among people with DM and obesity. The present systematic review and meta-analysis presents a critical synthesis of development of TPE interventions for DM and obesity and the efficacy of these interventions across a range of biomedical, psychosocial and psychological outcomes. A total of 54 of these RCTs were identified among patients with obesity and diabetes and were thus, qualitatively synthesized. Out of these, 47 were included into quantitative synthesis. There was substantial heterogeneity in reporting of these outcomes (I²= 88.35%, Q= 317.64), with significant improvement (SMD=0.36, 95% CI: 0.23 to 0.49) noted in biomedical outcomes in the intervention group. The effect sizes were comparable across interventions delivered by different modes and delivery agents. These interventions can be delivered by allied health staff, doctors or electronically as self-help programs, with similar effectiveness (P < 0.001). These interventions should be implemented in healthcare and community settings to improve health of patients suffering from DM and obesity.

Background: The cumulative number of patients during the COVID-19 pandemic led to a significant shortage of hospital beds. Many patients may not require hospitalization and can be clinically observed in home settings. However, some psychosocial factors are correlated with unsuccessful home isolation (HI), which might negatively affect the transmission control in the community. Therefore, we developed a new psychosocial screening tool (CCPHIET) for assessing HI suitability and examined its validity and reliability.Methods: This cross-sectional descriptive study included COVID-19 patients who were deemed to be medically safe for 14 days of HI. The CCPHIET is comprised of 8 clinical domains pertinent to HI behavioral compliance and risk. We explored its statistical validity and reliability and discussed the potential utility of this tool. Results: A total of 65 COVID-19 patients participated in this study. Most patients (58.5%) were evaluated as good candidates for HI. The CCPHIET has an acceptable content validity (IOC index > 0.5), moderate internal consistency (Cronbach’s alpha = 0.611) and substantial to excellent inter-rater reliability (Intraclass correlation coefficient = 0.944, Cohen’s kappa= 0.627).Conclusions: To compromise between strict and costly absolute institutional quarantine and the potentially unsuccessful absolute HI, the CCPHIET may help to identify good candidates for HI in mild and asymptomatic COVID-19 patients. This psychosocial information would support the physicians in matching each patient to the most suitable setting. Therefore, safe medical care is provided, unnecessary use of medical resources is spared, and local transmission is contained.

This article has been developed to assess the economic feasibility of a roof-top photovoltaic installation of industrial self-consumption. Numerical models that enable an interested person to obtain the main expected parameters will be generated. To do this, a calculation methodology will be generated through which the reader, knowing the location of the facility and dimensions of the roof, will be able to calculate the maximum installable power, the main parameters related to production, the cost of the installation, and the LCOE of the plant. The use of actual costs will be facilitated in case they are known, but it will remain possible to apply the costs of the major equipment (modules, inverter, and structure) considered throughout the article. This developed calculation methodology will also allow a quick comparison of the forecasts of production, CAPEX, and LCOE of plants designed with different inclinations and different types of modules. Consequently, it will be especially useful for decision-making before developing the plant's basic engineering. Moreover, the calculations used for modeling the LCOE will be analyzed in depth. This analysis will allow evaluating how the different technical variables affect the profitability of a photovoltaic installation, such as the selected tilt, the location, the module's technology, or the available area.

The use of flexible sensors has tripled over the last decade due to the increased demand in various fields including health monitoring, food packaging, electronic skins and soft robotics. Flexible sensors have the ability to be bent and stretched during use and can still maintain their electrical and mechanical properties. This gives them an advantage over rigid sensors that lose their sensitivity when subject to bending. Advancements in 3D printing have enabled the development of tailored flexible sensors. Various additive manufacturing methods are being used to develop these sensors including inkjet printing, aerosol jet printing, fused deposition modelling, direct ink writing, selective laser melting and others. Hydrogels have gained much attention in the literature due to their self-healing and shape transforming. Self-healing enables the sensor to recover from damages such as cracks and cuts incurred during use and this enables the sensor to have a longer operating life and stability. Various polymers are used as substrates on which the sensing conductive material is placed. Polymers including polydimethylsiloxane (PDMS), polyvinyl acetate (PVA), and Kapton are extensively used in flexible sensors. The most widely used nanomaterials in flexible sensors are carbon and silver, however, other nanomaterials such as iron, copper, manganese dioxide and gold are also used to provide controlled levels of conductivity or other functional properties.

Cognitive biases are an important factor contributing to the development and symptom severity of psychosis. Despite that various cognitive biases are contributing to psychosis, they are rarely investigated together. In the current systematic review, we aimed at investigating specific and shared neural correlates of two important cognitive biases: aberrant salience and source monitoring. We conducted a systematic search of fMRI studies of said cognitive biases. Eight studies on aberrant salience and eleven studies on source monitoring were included in the review. We critically discussed behavioural and neuroimaging findings concerning cognitive biases. Various brain regions are associated with aberrant salience and source monitoring in individuals with schizophrenia and the risk of psychosis. Ventral striatum and insula contribute to aberrant salience. The medial prefrontal cortex, superior and middle temporal gyrus contribute to source monitoring. The anterior cingulate cortex and hippocampus contribute to both cognitive biases, constituting a neural overlap. Our review indicates that aberrant salience and source monitoring may share neural mechanisms, suggesting their joint role in producing disrupted external attributions of perceptual and cognitive experiences, thus elucidating their role in positive symptoms of psychosis. Account bridging mechanisms of these two biases is discussed. Further studies are warranted.

Breast self-Examination (BSE) is the cheapest most recommended Breast Cancer (BC) preventive tool for resource-deprived settings. There is paucity in the attitude research domain and comparative gender assessments of the BSE knowledge, attitude and performance (KAP) literature. Purpose: The purpose of this study was to assess the combined and exclusive gender BSE attitude of undergraduate health trainees and to determine significant differences between scores of both genders. Methodology: participants included 5 undergraduate health trainee classes purposively sampled from 2 faculties. Online cross-sectional method was used to assess BSE attitude of 336 purposively sampled Kwame Nkrumah University of Science and technology (KNUST) College of Health Sciences (CoHS) students. Data was analyzed using descriptive and inferential statistical analyses. Main findings: Compared to the construction groups’ average norm of 101.17 (SD = 9.55), our study participants’ (SPs) BSE attitude was lower (92.51; SD = 11.80). However, using popular mid-point and 3- part attitude scoring methods, our SPs’ attitude scores were comparable to sub-regional and national findings. Moreover,the male participants scored a generally high BSE attitude but significantly lower compared to their female counterparts (p < 0.5). Recommendations: There is the need to adjust the curriculum of all health trainees in developing nations to reflect relevant BC preventive measures. Furthermore, BSE research, education as well as advocacy should involve more males as important BC BSE stake holders.

The time’s arrow of macroscopic physical phenomenon is reflected by irreversible physical process, which essentially occurs from small probability state to high probability state. In this paper, simplified models are proposed to understand the macroscopic physical process. In order to describe the information of a physical system, we defined the full self-information as "information height" to describe the complexity or difficulty of a macrostate of physical system. In this way, the direction of macroscopic physical process is from high information height to low information height. We can judge the direction of physical process by the information height. If we want the macroscopic physical process to evolve from the low information height state to the high information height state, the system need to add extra information and corresponding energy to increase the information height.

Complex dynamical fluctuations, from molecular noise within cells, collective intelligence, brain dynamics or computer traffic have been shown to display noisy behaviour consistent with a critical state between order and disorder. Living close to the critical point can have a number of adaptive advantages and it has been conjectured that evolution could select (and even tend to) these critical states. One way of approaching such state is by means of so-called self-organized criticality (SOC) where the system poises itself close to the critical point. Is this the case of living cells? It is difficult to test this idea given the enormous dimensionality associated with gene and metabolic webs. In this paper, we present an alternative approach: to engineer synthetic gene networks displaying SOC behaviour. This is achieved by exploiting the presence of a saturation (congestion) phenomenon of the ClpXP protein degradation machinery in E. coli cells. Using a feedback design that detects and then reduces ClpXP congestion, a {\em critical motif} is built from a two-gene network system, where SOC can be successfully implemented. Both deterministic and stochastic models are used, consistently supporting the presence of criticality in intracellular traffic. The potential implications for both cellular dynamics and designed intracellular noise are discussed.

Stability considerations play a central role in structural dynamics to determine states that are robust against perturbations during the operation. Linear stability concepts, such as the complex eigenvalue analysis, constitute the core of analysis approaches in engineering reality. However, most stability concepts are limited to local perturbations, i.e. they can only measure a state’s stability against small perturbations. Recently, the concept of basin stability has been proposed as a global stability concept for multi-stable systems. As multi-stability is a well-known property of a range of nonlinear dynamical systems, this work studies the basin stability of bi-stable mechanical oscillators that are affected and self-excited by dry friction. The results indicate how the basin stability complements the classical binary stability concepts for quantifying how stable a state is given a set of permissible perturbations.

Objectives: This study proposes a model that integrates three determinants of social cognitive theory (SCT) to explain the impact of FoMO on SNS usage. Design: A cross-sectional study is conducted using data for 259 participants recruited from a website. Main Outcome Measures: The analysis focuses on FoMO, social influence, positive outcome expectancy, refusal self-efficacy, and SNS-related behavior cloud-based sites. Data are examined using descriptive analysis and structural equation modeling. Results: The proposed model reported proper goodness of fit. FoMO does not directly or indirectly impact SNS usage through the determinants of SCT. However, social influence and refusal self-efficacy have a direct effect. Conclusions: The roles of the three determinants of SCT vary by stage of SNS usage. FoMO and refusal self-efficacy are more strongly related with SNS addiction. Further research, particularly longitudinal and intervention studies, is needed to examine the effects of specific factors on SNS addiction.

In this paper we give the new formulas for calculating the self-inductance for the circular coils of the rectangular cross sections with the radial and the azimuthal current densities. These formulas are given by the single integration of the elementary functions which are integrable on the interval of the integration. From these new expressions we can obtain the special cases for the self-inductance of the thin disk pancake and the thin wall solenoid that confirm the validity of this approach. For the asymptotic cases, the new formula for the self-inductance of the thin wall solenoid is obtained for the first time in the literature. In this paper we do not use special functions such as the elliptical integrals of the first, second and third kind, Struve, and Bessel functions because that is very tedious work. The results of this work are compared with already different known methods and all results are in the excellent agreement. This is way we consider this approach as the novelty because of its simplicity in the self -inductance calculation of the previously mentioned configurations.

Background: Nowadays investigations in the field of dental implants engineering are focused on bioactivity and osseointegration properties.Objective: In this study, the oxide-covered titanium was functionalized by vitamin D3 molecules via a simple self-assembly method with the aim to design more corrosion resistant and at the same time more bioactive surface.Methods: Surface properties of the D3-coated titanium were examined by scanning electron microscopy, attenuated total reflectance Fourier transform infrared spectroscopy, and contact angle measurements, while a long-term corrosion stability during immersion in an artificial saliva solution was investigated in situ by electrochemical impedance spectroscopy.Results: Results of all techniques confirmed a successful formation of the D3 vitamin layer on the oxide-covered titanium. Besides very good corrosion resistivity (~5 MΩcm² ) the D3-modified titanium surface induced spontaneous formation of biocompatible bone-like calcium phosphates (CaP).Conclusion: Observed in vitro CaP-forming ability as a result of D3-modified titanium/artificial saliva interactions could serve as a promising predictor of in vivo bioactivity of implant materials.

How, if at all, consciousness can be part of the physical universe remains a baffling problem. This article outlines a new, developing philosophical theory of how it could do so, and offers a preliminary mathematical formulation of a physical grounding for key aspects of the theory. Because the philosophical side has radical elements, so does the physical-theory side. The philosophical side is radical, first, in proposing that the productivity or dynamism in the universe that many believe to be responsible for its systematic regularities is actually itself a physical constituent of the universe, along with more familiar entities. Indeed, it proposes that instances of dynamism can themselves take part in physical interactions with other entities, this interaction then being “meta-dynamism” (a type of meta-causation). Secondly, the theory is radical, and unique, in arguing that consciousness is necessarily partly constituted of meta-dynamic auto-sensitivity, in other words it must react via meta-dynamism to its own dynamism, and also in conjecturing that some specific form of this sensitivity is sufficient for and indeed constitutive of consciousness. The article proposes a way for physical laws to be modified to accommodate meta-dynamism, via the radical step of including elements that explicitly refer to dynamism itself. Additionally, laws become, explicitly, temporally non-local in referring directly to quantity values holding at times prior to a given instant of application of the law. The approach therefore implicitly brings in considerations about what information determines states. Because of the temporal non-locality, and also because of the deep connections between dynamism and time-flow, the approach also implicitly connects to the topic of entropy insofar as this is related to time.

Integrated information theory (IIT) was initially proposed to describe human consciousness in terms of intrinsic-causal brain network structures. This theory could potentially be used for conceptualising complex living systems. In a previous study, we analysed collective behaviour in {\it Plecoglossus altivelis}. We found that IIT 3.0 exhibits qualitative discontinuity between three and four schools of fish in terms of $\Phi$ values (i.e., group integrity). Other measures, such as mutual information, did not show such characteristics. In this study, we follow up on our previous findings and introduce two new factors. First, we define the global parameter settings to determine a different kind of group integrity. Second, we set several timescales (from $\Delta t =5/120$ s to $\Delta t =120/120$ s). The results showed that we succeeded in classifying fish school according to their group size in terms of the degree of group integrity, despite the small group size. The concrete classification includes the followership for a two-fish school, fission--fusion for a three-fish school, emergence of leadership for a four-fish school, and emergence of Boid-like behaviour for a five-fish school. These minute classifications have never been revealed before. Finally, we discuss one of the longstanding paradoxes in collective behaviour, known as the heap paradox, for which two tentative answers could be provided through our IIT analysis.

Different factors seemingly account for the emergence of present-day languages in our species. Human self-domestication has been recently invoked as one important force favouring language complexity mostly via a cultural mechanism. As a consequence, evolutionary changes impacting on aggression levels are expected to have fostered this process. Here we hypothesise about a positive effect of dog-human interactions on aggression management and more generally, on our self-domestication, ultimately, contributing to aspects of language evolution. We review evidence of diverse sort (ethological mostly, but also archaeological, genetic, and physiological) supporting a positive feedback loop between dog domestication and human-self domestication that might have favoured the mechanisms promoting structural complexity in human languages.

The purpose of this study was to detect mind-wandering experienced by pre-service teachers while learning video lecture on physics. The lecture was videotaped and consisted of a live lecture in a classroom. The lecture was about Gauss's law on physics. We investigated whether oculomotor data and eye movements could be used as a marker to indicate the learner’s mind-wandering. Each data was collected in a study in which 24 pre-service teachers (16 females and 8 males) reported self-caught mind-wandering while learning physics video lecture during30 minutes. A Tobii Pro Spectrum (sampling rate: 300Hz) was used to capture their eye-gaze during learning Gauss's law course video. After watching video lecture, we interviewed pre-service teachers about their mind-wandering experience. We first used the self-caught method to capture the mind-wandering timing of pre-service teachers while learning from video lectures. We detected more accurate mind-wandering segments by comparing fixation duration and saccade count. We investigated two types of oculomotor data (blink count, pupil size) and nine eye movements (average peak velocity of saccades; maximum peak velocity of saccades; standard deviation of peak velocity of saccades; average amplitude of saccades; maximum amplitude of saccades; total amplitude of saccades; saccade count/s; fixation duration; fixation dispersion). The result was that the blink count could not be used as a marker for mind-wandering during learning video lectures among them (oculomotor data and eye movements), unlike previous literatures. Based on the results of this study, we identified elements that can be used as mind-wandering markers while learning from video lectures that are similar to real classes, among the oculomotor data and eye movement mentioned in previous literatures. Also, we found that most participants focused on past thoughts and felt unpleasant after experiencing mind-wandering through interview analysis.

Background: In recent years, the abusive use of the smartphone has reached a situation that could be considered pathological, although there is no such classification in the DSM-IV. In this sense, different instruments to assess this problematic use or addiction to the smartphone are used. Among them, we found the SPAI, which has been validated in the Spanish language. The main difficulty of these scales is to establish a cut-off point that determines such mobile addiction. On the other hand, self-perception has been used in different addictions as a predictor of the problem. Aim: The objective of this study is to establish the cutting point in the scores of the SPAI scale validated in Spanish, using as a referent the self-perception of addiction values. Methods: A receiver operating characteristics (ROC) analysis was carried out, establishing as the cut-off point the one that presented a higher value of Youden J, indicative of its sensitivity and specificity. Results: 2598 participants from the university community completed the SPAI-Spain questionnaire. When using the self-perception of smartphone addiction as the benchmark value, a score of 44 has been established as the cutting point of the SPAI questionnaire, with a Youden J corresponding to 0.416. Conclusions: The implementation of a cut-off point of the SPAI questionnaire makes it an instrument that allows early identification of those individuals at risk of addiction as well as the establishment of preventive and/or intervention measures.

Mass insect rearing can have a range of applications, for example in biological control of insects. Since the performance of released biological control agents determines efficacy, the competitive fitness of insects post release is a key variable. Here, we tested whether inoculation with a gut symbiont, Enterobacter cloacae, and gnotobiotic rearing of larvae could improve insect growth and male competitive fitness of a transgenic diamondback moth, which has shown variation in fitness when reared in different insectaries. All larvae were readily infected with the focal symbiont. Under gnotobiotic rearing pupal weights were reduced and there was a marginal reduction in larval survival. However, gnotobiotic rearing substantially improved the fitness of transgenic males. In addition, in gnotobiotic conditions, inoculation with the gut symbiont increased pupal weights and male fitness, increasing the proportion of transgenic progeny from 20 to 30% relative to symbiont-free insects. Gnotobiotic conditions may improve the fitness of transgenic males by excluding microbial contaminants, while symbiont inoculation could further improve fitness by providing additional protection against infections, or by normalizing insect physiology. The simple innovation of incorporating antibiotic into diet, and inoculating insects with symbiotic bacteria that are resistant to that antibiotic, could provide a readily transferable tool for other insect rearing systems.

Integrated information theory (IIT) was initially proposed to describe human consciousness in terms of intrinsic, causal brain-network structures. This theory has the potential to be useful for conceptualising complex living systems. In a previous study, we analysed collective behaviour in Plecoglossus altivelis and found that IIT 3.0 exhibits qualitative discontinuity between three and four fish. Other measures like mutual information did not have such characteristics. In this study, we followed up on our previous findings and examined timescale effects on integrated information of collective behaviour. We found that a long timescale (1 s) causes Boid-like local interactions to dominate over interactions with the whole, but only when the group size is five at a given time scale (i.e. around 0.2 s). Interestingly, the most suitable time scale is roughly equal to fish reaction time. We used these data to propose a new classification for fish schools, with each size group being a unique and autonomous system, despite small group sizes.

Lignin has interesting functionalities to be exploited in adhesives for medicine, foods and textiles. Nanoparticles (NPs) <100 nm coated with poly(L-lysine), PL and poly(L-glutamic acid) PGA were prepared from the laccase treated lignin to coat nanocellulose fibrils (CNF) with heat. NPs ca. 300 nm were prepared, β-casein coated and cross-linked with transglutaminase (Tgase) to agglutinate chamois specimens. Size exclusion chromatography (SEC) and Fourier-transform infrared (FTIR) spectroscopy were used to characterize polymerized lignin, zetapotential and dynamic light scattering (DLS) to ensure coating of colloidal lignin particles (CLPs). Protein adsorption on lignin was studied by quartz crystal microbalance (QCM). Atomic force microscopy (AFM) was exploited to examine interactions between different polymers and to image NPs with transmission electron microscopy (TEM). Tensile testing showed, when using CLPs for the adhesion, the stress improved ca. 10 and strain ca. 6 times compared to polymeric lignin. For the β-casein NPs the values were 20 and 8, respectively, and for the β-casein coated CLPs between these two cases. When NPs were dispersed in adhesive formulation, the Young's moduli confirmed significant improvement in the elasticity of the joints over the adhesive alone. Exploitation lignin in nanoparticulate morphology is a potential method to prepare bionanomaterials for advanced applications.

In this study, a flexible strain sensor is devised using corrugated poly(3-hexylthiophene) (P3HT) thin film. In the previous studies, the P3HT-based photoactive thin film was shown to generate direct current (DC) under broadband light, and the generated DC voltage varied with applied tensile strain. Yet, the mechanical resiliency and strain sensing range of the P3HT-based thin film strain sensor were limited due to relatively more brittle thin film constituent—poly(3,4-ethylenedioxythiophene)-polystyrene(sulfonate) (PEDOT:PSS) conductive thin film as a bottom electrode. To address this issue, it is aimed to design mechanically resilient strain sensor using corrugated thin film constituents. Buckling is induced to form corrugation in the thin films by applying pre-strain to the substrate, where the thin films are deposited, and releasing the pre-strain afterwards. It is known that corrugated thin film constituents exhibit different optical and electronic properties from non-corrugated ones. Therefore, to optimize design of the flexible strain sensor, it was studied to understand how the applied pre-strain and thickness of the PEDOT:PSS thin film affect the optical and electrical properties. Also, pre-strain effect on light absorptivity of the corrugated P3HT-based thin films was studied. In addition, strain effect was investigated on the optical and electrical properties of the corrugated thin film constituents. Finally, flexible strain sensors are fabricated by following the design guideline, which is suggested from the studies on the corrugated thin film constituents, and DC voltage strain sensing capability was validated. As a result, flexible strain sensor exhibited tensile strain sensing range up to 5% at frequency up to 15 Hz with maximum gage factor ~7.

Structural DNA nanotechnology provides an excellent foundation for diverse nanoscale shapes that can be used in various bioapplications and materials research. From all existing DNA assembly techniques, DNA origami has proven to be the most robust one for creating custom nanoshapes. Since its invention in 2006, building from the bottom up using DNA has drastically advanced, and therefore, more and more complex DNA-based systems have become accessible. So far, vast majority of the demonstrated DNA origami frameworks are static by nature, but interestingly, there also exist dynamic DNA origami devices that are increasingly coming into view. In this review, we discuss DNA origami nanostructures that perform controlled translational or rotational movement triggered by predefined DNA strands, various molecular interactions and/or other external stimuli such as light, pH, temperature and electromagnetic fields. The rapid evolution of such dynamic DNA origami tools will undoubtedly have a significant impact on molecular scale precision measurements, targeted drug delivery and diagnostics, but they can also play a role in development of optical/plasmonic sensors, nanophotonic devices and nanorobotics for numerous different tasks.

The role played by non extensive thermodynamics in physical systems has been under intense debate for the last decades. With many applications in several areas, the Tsallis statistics has been discussed in details in many works and triggered an interesting discussion on the most deep meaning of entropy and its role in complex systems. Some possible mechanisms that could give rise to non extensive statistics have been formulated along the last several years, in particular a fractal structure in thermodynamics functions was recently proposed as a possible origin for non extensive statistics in physical systems. In the present work we investigate the properties of such fractal thermodynamical system and propose a diagrammatic method for calculations of relevant quantities related to such system. It is shown that a system with the fractal structure described here presents temperature fluctuation following an Euler Gamma Function, in accordance with previous works that evidenced the connections between those fluctuations and Tsallis statistics. Finally, the fractal scale invariance is discussed in terms of the Callan-Symanzik Equation.

Stimuli-responsive polymeric materials have attracted significant attentions in a variety of high-value-added and industrial applications during the past decade. Among various stimuli, light is of particular interest as a stimulus due to its unique advantages such as precisely spatiotemporal control, mild conditions, ease of use, and tunability. In recent years, a lot of effort toward synthesis of biocompatible and biodegradable polypeptide has resulted in many examples of photo-responsive nanoparticles. Depending on the specific photochemistry, those polypeptide derived nano-assemblies are capable of crosslinking, disassembling, or morphing into other shapes upon light irradiation. In this mini-review, we aim to assess the current state of photo-responsive polypeptide based nanomaterials. First, those “smart” nanomaterials will be categorized by their photo-triggered events (i.e., crosslinking, degradation, and isomerization) which are inherently governed by photo-sensitive functionalities including o-nitrobenzyl, coumarin, azobenzene, cinnamyl, and spiropyran. In addition, the properties and applications of those polypeptide nanomaterials will be highlighted as well. Finally, the current challenges and future directions of this subject will be evaluated.

In most industrialized countries, screening programs for cervical cancer have shifted from cytology (Pap smear or ThinPrep) alone on clinician-obtained samples to the addition of screening for human papillomavirus (HPV), its main causative agent. For HPV testing, self-sampling instead of clinician-sampling has proven to be equally accurate, in particular for assays that use nucleic acid amplification techniques. In addition, HPV testing of self-collected samples in combination with a follow-up Pap smear in case of a positive result is more effective in detecting precancerous lesions than a Pap smear alone. Self-sampling for HPV testing has already been adopted by some countries, while others have started trials to evaluate its incorporation into national cervical cancer screening programs. Self-sampling may result in more individuals willing to participate in cervical cancer screening, because it removes many of the barriers that prevent women, especially those in low socioeconomic and minority populations, from participating in regular screening programs. Several studies have shown that the majority of women who have been underscreened but who tested HPV-positive in a self-obtained sample, will visit a clinic for follow-up diagnosis and management. Additionally, a self-collected sample can also be used for vaginal microbiome analysis, which can provide additional information about HPV infection persistence as well as vaginal health in general.

The betel nut chewing habit of taxi drivers has shown that can promote companionship, relaxation, and emotional stability at work. 19.3% of betel nut behaviors are instigated by an invitation among drivers to engage in the habit. The objective of this study was to explore the impact of social learning, subjective norm, and self-efficacy on betel nut chewing in taxi drivers in Taiwan. We conducted a cross-sectional study on taxis’ driver of betel nut behaviour and measured their evaluations of its consequences using a self-report questionnaire. Other variables include socio-demographic characteristics and chewing-related experience. The overall variance is 44.5% in socio-demographic variable, social learning, self-efficacy, and subjective norm. Results show that the variables of affected betel nut chewing behavior, driving years has the strongest influence, followed by learning about betel nut from the media, relatives, and friends, and education level, which positively affect betel nut chewing behavior. Findings indicated that education related to quitting betel nut chewing, significant others, and rules of the taxi fleet are adopted to design a plan for quitting betel chewing to reduce the positive expectations of taxi drivers for betel nut chewing and decrease their betel nut chewing behavior.

The integration of Genetic Algorithms (GAs) into Serious Games (SGs) has gained traction as a method to optimize game mechanics and personalize user experiences. While GAs are well-known for their effectiveness in solving complex optimization problems, their application in SGs remains relatively under-explored. This review aims to address this gap by systematically mapping the existing literature on the intersection of these two fields. The primary objective of this scoping review is to identify and synthesize the existing research on the application of GAs in SGs, with a focus on understanding the current state of the field, identifying common applications, challenges, and potential future directions. The review includes studies published in English that focus on the use of GAs within SGs. The search encompassed articles from academic journals and conference proceedings without restrictions on publication dates. Exclusion criteria included studies that did not specifically address the integration of GAs in SGs. A systematic search was conducted in databases such as ACM Digital Library, Web of Science, Scopus and Inspec in July 2024. The search terms used included "genetic algorithm," "evolutionary algorithm," "computational algorithm," and "serious games." Unpublished data and articles were not considered. Data were charted using a PRISMA flowchart developed by the author VV. Key categories for data extraction included study objectives, methods, outcomes, type of study and GA used and the specific applications of GAs in SGs. Data charting was conducted independently by two reviewers, with disagreements resolved through discussion and consensus. The search identified 154 studies, of which 23 met the inclusion criteria. The included studies highlight the diverse applications of GAs in SGs, ranging from optimizing game scenarios to personalizing learning experiences. Challenges identified include the computational complexity of GAs and difficulties in their integration into existing game frameworks. The findings of this review suggest that while the use of GAs in SGs is a promising area of research, it remains in its nascent stages. Future research should focus on addressing the technical challenges of integrating GAs into SGs and exploring their application across a wider range of game genres and educational contexts. Limitations of the review include the potential for publication bias and the exclusion of non-English language studies.

Due to the manufacturing process of Additive Manufacturing (AM) Parts the mechanical properties cannot be predicted. The scope of the present work is to propose a predictive tool for the mechanical properties of AM parts through the use of Adaptive Neuro-Fuzzy Inference System (ANFIS). The Adaptive Neuro-Fuzzy Inference System (ANFIS) combines neural networks with fuzzy logic to generate a mapping between the inputs and the output. Real data experiments from different AM technologies and materials compose the initial dataset so a detailed investigation is conducted to gain knowledge of how different parameters affect the UTS, the elongation and the Young modulus of each experiment. Experimental data were also use to train, check and verify the methodology. The method is tested for FFF technology, PLA material and 3 point bending stress, with interesting results.

In the field of adaptive building technologies, this research introduces the development of a self-regulating solar shading actuator, that utilizes the thermal shape memory effect. The study places its focus on addressing the actuator’s performance under diverse environmental conditions. Thermal simulations were carried out during the development of the individual components of the actuator and for the prediction of specific switching temperatures. The investigation includes an analysis of the sunshade’s response to varying environmental conditions, emphasizing its effectiveness on clear summer days and identifying challenges during overcast periods. The critical coordination between the solar collector and the shape memory alloy (SMA) wire is examined, shedding light on the impact of SMA temperature dynamics on the actuation performance. Through the integration of simulation data and real-world measurements, the study validates the thermal model for the solar collector, establishing the robustness of the system’s operation. This research work contributes significantly to the development of intelligent actuators and outlines the importance of validation of SMA-based applications under real conditions.

Gesture recognition technology has been quickly developed in the field of human-computer interaction. The multiple-input multiple-output (MIMO) radar has been widely adopted in gesture recognition because of its notable spatial resolution. In this work, a highly accurate MIMO radar-based hand gesture recognition algorithm with very low complexity is proposed. To make the proposed system applicable in the industry and work well even when the size of the training data is limited, we applied several low-complexity adaptive signal processing methods to extract features and reduce the noise effect. First, spectrum analysis is applied to range-Doppler maps (RDMs) and a cell-averaging constant false alarm rate (CA-CFAR) with mirror filters is applied to improve the robustness to noise. Afterward, the features related to the distance, speed, direction, and the elevation angle of the moving object are determined by the proposed adaptive signal analysis techniques and the random forest is applied for classification. The proposed system has the capability to precisely distinguish and identify eight motions, including waving, moving to the left or right, patting, pushing, pulling, and rotating clockwise or anticlockwise, with an accuracy of 95%. Experiments demonstrate the capability of the proposed hand gesture recognition system to classify different movements precisely.

To ensure high-quality care, operationalize resilience and fill the knowledge gap regarding how to improve the prerequisites for resilient performance, it is necessary to understand how adaptive capacity unfolds in practice. Coronavirus 2019 stress-tested complex healthcare systems, and intensive care was driven to an unprecedented escalation. The main aim of this research was to explain the escalation process of intensive care from a microlevel perspective, including expressions of resilient performance, intervening conditions at the micro-meso-macrolevels and short- and long-term consequences. A secondary aim was to provide clinical recommendations regarding how to optimize the prerequisites for resilient performance in intensive care. A grounded theory methodology was used. First-person stories from different healthcare professionals (n70) in two Swedish regions were analyzed using the constant comparative method. A novel conceptual model of the escalation process of intensive care emerged, and 41 clinical recommendations concerning the optimal prerequisites for resilient performance were identified. Escalation was conceptualized as a transition from threatening chaos to temporary order through a complex process of adaptation. To prepare for the future, the components space, stuff, staff, system and science with associated continuity plans must be implemented, anchored and communicated to actors at all levels of the system.

Compared to wide-field telescopes, small-field detection systems have higher spatial resolution, resulting in stronger detection capabilities and higher positioning accuracy. When detecting by small-field in synchronous orbit, both space debris and fixed stars are imaged as point targets, making it difficult to distinguish them. In addition, with the improvement of detection capabilities, the number of stars in the background rapidly increases, which puts higher requirements on recognition algorithms. Therefore, star detection is indispensable for identifying and locating space debris in complex backgrounds. To address these difficulties, this paper proposes a real-time star extraction method based on adaptive filtering and multi frame projection. We use bad point repair and background suppression algorithms to preprocess star image. Afterwards, we analyze and enhance the target signal-to-noise ratio(SNR). Then, we use multi frame projection to fuse information. Subsequently, adaptive filtering, adaptive morphology, and adaptive median filtering algorithms are proposed to detect trajectories. Finally, the projection is released to locate the target. Our recognition algorithm has been verified by real star images, and the images were captured using small-field telescope. The experimental results demonstrate the effectiveness of the algorithm proposed in this paper. We successfully extracted hip-27066 star, which has magnitudes about 12 and SNR about 1.5. Compared with existing methods, our algorithm has advantages in both recognition rate and false-alarm rate, and can be used as real time target recognition algorithm for space-based synchronous orbit detection payload.

Discretization is the process of converting a continuous function or model or equation into discrete steps. In this work, adaptive and learning methods are implemented to control DC motors that are used for actuating control surfaces of unmanned underwater vehicles. Adaptive control is a method in which the controller is designed to adapt the system with parameters which vary or are uncertain. Parameter estimation is the process of computing the parameters of a system using a model &amp; measured data. Adaptive methods have been used in conjunction with different parameter estimation techniques. Deterministic artificial intelligence, a learning-based approach that uses the process dynamics defined by physics, is also applied to control the output of the DC motor to track a specified trajectory. This work goes further to evaluate the performance of the adaptive &amp; learning techniques based on different discretization methods. The results are evaluated based on the absolute error mean between the output &amp; the reference trajectory and the standard deviation of the error. The first order-hold method of discretization and surprisingly large sample time of seven tenths of a second yields over sixty percent improvement over the results presented in the prequel literature.

This study examined the association between pressure injuries and complexity of abdominal temperature measured in residents of a nursing facility. The temperature served as a proxy measure for skin thermoregulation. Refined multiscale sample entropy and bubble entropy were used to measure the complexity of the temperature time series measured over two days at 1-minute intervals. Robust summary measures were derived for the multiscale entropies and used in predictive models for pressure injuries that were built with adaptive lasso regression and neural networks. Both types of entropies were lower in the group of participants with pressure injuries (n=11) relative to the group of non-injured participants (n=15). This was generally true at the longer temporal scales, with the effect peaking at scale τ=23 minutes. Predictive models for pressure injury on the basis of refined multiscale sample entropy and bubble entropy yielded 92% accuracy, outperforming predictions based on any single measure of entropy. Combining entropy measures with a widely used risk assessment score led to the best prediction accuracy. Complexity of abdominal temperature series could therefore serve as an indicator of risk of pressure injury.

In this paper, the problem of observer-based adaptive sliding mode control is discussed for nonlinear systems with sensor and actuator faults. The time-varying actuator degradation factor and external disturbance are considered in the system simultaneously. In this study, the original system is described as a new normal system by combining the state vector, sensor faults and external disturbance into a new state vector. For the augmented system, a new sliding mode observer is designed, where a discontinuous term is introduced such that the effects of sensor and actuator faults and external disturbance will be eliminated. In addition, based on a tricky design of the observer, the time-varying actuator degradation factor term is developed in the error system. On the basis of the state estimation, an integral-type adaptive fuzzy sliding mode controller is constructed to ensure the stability of the closed-loop system. Finally, the effectiveness of the proposed control methods can be illustrated with a numerical example.

The issue of workplace toxicity constitutes a major problem for employers, employees and society at large. Toxicity in a workplace context relates to damaging behaviours perpetrated by individuals and organisations that netatively impact on the health and wellbeing of others. Workplace toxicty is habitually associated with activities such as bullying, harrassment, unethical and even illegal acts. In this article, today’s workplace is likenened to a cage where ambition and greed drive people to engage in toxic behaviour to fight for whatever scarce rewards are on offer. Set against a backdrop of continuous demands on employees in a era of constant change and challenge, leadership practice in the 21st century is understandably seeped in complexity. Whilst conditions of scarcity are in fact constructed by employers in today’s lean environments, workplace toxicity has significant implications for organisational leaders in terms of work place disputes, absenteeism and litigation etc. Twentieth century hierarchial and autocratic leadership models are now deemed to be both obsolete and wholly inadedgate to inform leadership practice in the knowledge era. This conceptual article looks at the efficacy of complexity leadership theory (CLT) in empowering today’s leader to better understand and address 21st century workplace toxicity.

Expectation maximization (EM) algorithm is a powerful mathematical tool for estimating statistical parameter when data sample contains hidden part and observed part. EM is applied to learn finite mixture model in which the whole distribution of observed variable is average sum of partial distributions. Coverage ratio of every partial distribution is specified by the probability of hidden variable. An application of mixture model is soft clustering in which cluster is modeled by hidden variable whereas each data point can be assigned to more than one cluster and degree of such assignment is represented by the probability of hidden variable. However, such probability in traditional mixture model is simplified as a parameter, which can cause loss of valuable information. Therefore, in this research I propose a so-called conditional mixture model (CMM) in which the probability of hidden variable is modeled as a full probabilistic density function (PDF) that owns individual parameter. CMM aims to extend mixture model. I also propose an application of CMM which is called adaptive regressive model (ARM). Traditional regression model is effective when data sample is scattered equally. If data points are grouped into clusters, regression model tries to learn a unified regression function which goes through all data points. Obviously, such unified function is not effective to evaluate response variable based on grouped data points. The concept “adaptive” of ARM means that ARM solves the ineffectiveness problem by selecting the best cluster of data points firstly and then evaluating response variable within such best cluster. In order words, ARM reduces estimation space of regression model so as to gain high accuracy in calculation.

In this article, the research team has systematically developed a method to model the kinematics and dynamics of a 3-wheeled robot subjected to external disturbances and sideways wheel sliding. These models will be used to design control laws that compensate for wheel slippage, model uncertainties, and external disturbances. These are control algorithms developed based on Dynamic Surface Control (DSC). Adaptive trajectory tracking DSC algorithm using fuzzy logic system (AFDSC) and radial neural network (RBFNN) with fuzzy logic system to overcome the disadvantages of DSC and expand the application domain for wheeled Mobile Robots (WMR). However, this Adaptive Fuzzy Neural Network Dynamic Surface Control (AFNNDSC) adaptive controller ensures the closed system is stable and follows the preset trajectory in the presence of wheel slippage model uncertainty and is affected by significant amplitude disturbances. The stability and convergence of the closed-loop system are guaranteed based on the Lyapunov analysis. The AFNNDSC adaptive controller is evaluated by simulation on MATLAB/Simulink software and in a steady state. The maximum position error on the right wheel and left wheel is 0.000572 (m) and 0.000523 (m), and the angular velocity tracking error in the right and left wheels of the control method is 0.000394 (rad/s). The experimental results show the correctness of the theoretical analysis, the effectiveness of the proposed controller, and the possibility of practical application.

To address the trajectory tracking issue for quadrotors working under the influence of external disturbances, a control scheme is designed for position and attitude trajectory tracking. Firstly, the quadrotor dynamic model is derived for control design. Secondly, adaptive integral backstepping control (AIBS) is employed in the position loop in which adaptive estimation is used to estimate the upper bounds of disturbances. Subsequently, a new adaptive backstepping fast nonsingular integral terminal sliding mode control (ABFNITSM) is proposed to follow desired Euler angles. The intro-duction of fast nonsingular terminal sliding mode and integral element is to realize fast convergence and accurate tracking. Dead zone technique is applied to minimize estimation errors. Besides, a saturation function is utilized to eliminate chattering phenomenon. Finally, simulation experiments are conducted under the Simulink environment. The proposed control is verified by comparing it with traditional integral terminal sliding mode control (ITSM) and integral backstepping control (IBS).

Low-intensity noisy galvanic vestibular stimulation (nGVS) can improve static and dynamic postural deficits in patients with bilateral vestibular loss (BVL). In this study, we aimed to explore the neurophysiological and neuroanatomical substrates underlying nGVS treatment effects in a rat model of BVL. Regional brain activation patterns and behavioral responses to repeated 30-min nGVS treatment in comparison to sham stimulation were investigated by serial whole-brain [18F]-FDG-PET measurements and quantitative locomotor assessments before and at 9 consecutive time points up to 60 days after chemical bilateral labyrinthectomy (BL). [18F]-FDG-PET revealed a broad nGVS-induced modulation on regional brain activation patterns encompassing biologically plausible brain networks in the brainstem, cerebellum, multisensory cortex and basal ganglia during the entire observation period post-BL. nGVS broadly reversed brain activity adaptions occurring in the natural course post-BL. Parallel behavioral locomotor assessment demonstrated a beneficial treatment effect of nGVS on sensory-ataxic gait alterations, particularly in the early stage of post-BL recovery. Stimulation-induced locomotor improvements were finally linked to nGVS brain activity responses in the brainstem, hemispheric motor and limbic networks. In conclusion, combined [18F]-FDG-PET and locomotor analysis discloses the potential neurophysiological and neuroanatomical substrates that mediate previously observed therapeutic nGVS effects on postural deficits in patients with BVL.

This work presents a new scheme for autonomous control and obstacle detection of a catamaran unmanned surface vessel. Based on the Newtonian mechanics, hydrodynamics, and maneuvering modelling group modelling principles in the attached coordinate system, a three-degree-of-freedom motion model of the catamaran unmanned surface vessel is constructed. The fuzzy inference method is used to overcome the drawback that the traditional PID control cannot tune the PID parameters online, and the trajectory controller is designed. Meanwhile, 3D LIDAR is used to achieve obstacle detection. The results of the trajectory tracking simulations and experiments verify the accuracy of the model design and the effectiveness of the autonomous control design.

Motivated for improving the efficiency and reliability of wind turbine energy conversion, this paper presents an advanced control design that enhances the power regulation efficiency and re-liability. The constrained behaviour of the wind turbine is taken into account, by using the barrier Lyapunov function in the analysis of the Lyapunov direct method. This, consequently, guarantees that the generated power remains within the desired bounds to satisfy the grid power demand. Moreover, a Nussbaum-type function is utilized in the control scheme, to cope with the unpre-dictable wind speed. This eliminates the need for accurate wind speed measurement or estimation. Furthermore, via properly designed adaptive laws, a robust actuator fault-tolerant capability is integrated into the scheme, handling the model uncertainty. Numerical simulations are performed on a high-fidelity wind turbine benchmark model, under different fault scenarios, to verify the effectiveness of the developed design. Also, a Monte-Carlo analysis is exploited for the evaluation of the reliability and robustness characteristics against the model-reality mismatch, measurement errors and disturbance effects.

This paper develops a Green Light Optimal Speed Advisory (GLOSA) system for buses (B-GLOSA). The proposed B-GLOSA system is implemented on diesel buses, and field tested to validate and quantify the potential real-world benefits. The developed system includes a simple and easy to calibrate fuel consumption model that computes instantaneous diesel bus fuel consumption rates. The bus fuel consumption model, a vehicle dynamics model, the traffic signal timings, and the re-lationship between vehicle speed and distance to the intersection are used to construct an optimi-zation problem. A moving-horizon dynamic programming problem solved using the A-star algo-rithm is used to compute the energy-optimized vehicle trajectory through signalized intersections. The Virginia Smart Road test facility was used to conduct the field test on 30 participants. Each participant drove three scenarios including a base case uninformed drive, an informed drive with signal timing information communicated to the driver, and an informed drive with the recom-mended speed computed by the B-GLOSA system. The field test investigated the performance of using the developed B-GLOSA system considering different impact factors, including road grades and red indication offsets, using a split-split-plot experimental design. The test results demonstrated that the proposed B-GLOSA system can produce smoother bus trajectories through signalized in-tersections producing fuel consumption and travel time savings. Specifically, compared to the uninformed drive, the B-GLOSA system produces fuel and travel time savings of 22.1% and 6.1% on average, respectively.

Frequent flooding from sea-level rise (SLR) is one of the immediate climate change impacts affecting low-lying and exposed coastal communities. These communities rely upon the delivery of three-waters services for wastewater, stormwater and water supply. Due to ongoing SLR, managing these networks will increasingly be a challenge. This raises the issue of how local government can reconcile maintaining levels of service as the impacts of climate change and their uncertainties worsen over the coming decades (and beyond). Can they be adapted over time to retain levels of service or will they eventually require retreat and if so at what adaptation threshold? This paper explores managed retreat of two-waters infrastructure (wastewater and stormwater) as an adaptation option using a Dynamic Adaptive Pathway Planning (DAPP) approach. In the study, we use DAPP to frame the retreat of two-water networks, developing a combination of an area specific retreat strategy, pathway portfolios, retreat phases, land use change signaling and identify pathway conflicts and synergies. Repurposing retreated areas by utilizing Water Sensitive Urban Design (WSUD) options was found to extend retreat thresholds for adjacent areas. A systematic ’routine’ developed in this study provides a structured approach for managed retreat of two-water infrastructure with the aim to reduce future disruption from flooding, signal land use changes early and allow for gradual budget adjustments by the agencies to manage expenditure over time. This approach helps inform and improve the decision-making process for the agencies and the communities they serve, by providing a stepwise process that can be communicated spatially and visually, thereby making a retreat adaptation option more manageable.

Reservoir computers (RCs) are a biology inspired computational framework for signal processing typically implemented using recurrent neural networks. Recent work has shown that Boolean networks (BN) can also be used as reservoirs. We analyze the performance of BN RCs, measuring their flexibility and identifying factors that determine effective approximation of Boolean functions that are applied in a sliding-window fashion over a binary signal, either non-recursively or recursively. We train and test BN RCs of different sizes, signal connectivity, and in-degree to approximate 3-bit, 5-bit and 3-bit recursive binary functions. We analyze how BN RC parameters and function average sensitivity, a measure of function smoothness, affect approximation accuracy as well as the spread of accuracies for a single reservoir. We found that approximation accuracy and reservoir flexibility are highly dependent on RC parameters. Overall, our results indicate that not all reservoirs are equally flexible and RC instantiation and training can be more efficient if this is taken into account. The optimum range of RC parameters opens up an angle of exploration for understanding how biological systems might be tuned to balance system restraints with processing capacity.

There is increasing demand from stakeholders for tools to support outcome-based approaches in environmental management. For such tools to be useful, understanding user requirements is key. In Scotland, UK, stakeholders were engaged in the development of an Environmental Decision Support System (EDSS) to support the management of land and freshwater resources for multiple policy outcomes. A structured participatory engagement process was employed to determine stakeholder requirements, establish development principles to fulfil these requirements and road-test prototypes. The EDSS specification that emerged from this bottom-up process was spatially-explicit, free at the point of use, and touch and mobile device compatible. This application, which is under development, does not closely resemble most existing published EDSS. We suggest that there is a mismatch between the way scientists typically conceptualise EDSS and the kinds of applications that are likely to be useful to decision-makers on the ground. Interactive mobile and web-based geospatial information services have become ubiquitous in our daily lives, but their importance is not reflected in current literature on EDSS. The current focus in environmental management on adaptive, stakeholder-centred strategies based on outcomes offers an opportunity to make better use of these new technologies to aid decision-making processes.

Background: The dynamic nature of today’s organizations, inclusive of educational institutions require employees to have self-leadership attributes. Several factors in an academic environment can affect the self-leadership necessary for the optimal performance of nurse educators. This study aims to explore nurse educators’ understanding of self-leadership and their perspectives on how this phenomenon can be promoted to create a healthy work environment in an educational setting. Methods: An exploratory, descriptive qualitative design was adopted. Data were collected through focus group interviews with nurse educators at four educational institutions in South Africa, then subjected to Tesch’s qualitative content analysis. Results: Two themes emerged, namely, Self-leadership means nurse educators being supported to take own initiatives, with its four sub-themes, and A supportive healthy academic work environment, with its four sub-themes. Conclusions: Aca-demic institutions who work collaboratively with nurse educators and support them in self-leadership practices contribute to the creation of healthy work environments.