Alarm therapy for nocturnal enuresis in children: A literature review

Nocturnal enuresis is a common childhood problem impacting the quality of life of children and families. Treatment with an enuresis alarm is recommended for 8–12 weeks by the International Children's Continence Society as first line management of monosymptomatic nocturnal enuresis. However, the effectiveness of alarm therapy varies between 80% and 45.9%. There is minimal evidence within the literature exploring the factors impacting this varying response to alarm therapy. Therefore, this literature review aims to explore factors that impact the effectiveness of the enuresis alarm as a treatment for nocturnal enuresis, in children aged 5–17 years. Literature searches were conducted on MEDLINE (Ovid), SCOPUS and CINAHL Databases. The PRISMA tool was used to report the data in the search strategy. The inclusion criteria of children aged 5 to 17 years was chosen based on International Children's Continence Society Guidelines. English language, academic journals and studies in the past 10 years were selected as additional inclusion criterion to identify the most recent, robust literature for the review. All 13 primary research articles were critiqued using the Caldwell Framework. Data were extracted and presented in table format highlighting study methodology, sample, duration of treatment, relevance to review topic and key findings. The findings highlight factors influencing the effectiveness of alarm therapy related to the impact on the child and family, heighten arousal to the alarm, the duration of therapy, age of child and the impact of overlearning. This review provides health professionals with an insight into strategies that may help children and their family to respond successfully to enuresis alarm treatment

Microbial Biosurfactants: Antimicrobial Activity and Potential Biomedical and Therapeutic Exploits

The rapid emergence of multidrug-resistant pathogens worldwide has raised concerns regarding the effectiveness of conventional antibiotics. This can be observed in ESKAPE pathogens, among others, whose multiple resistance mechanisms have led to a reduction in effective treatment options. Innovative strategies aimed at mitigating the incidence of antibiotic-resistant pathogens encompass the potential use of biosurfactants. These surface-active agents comprise a group of unique amphiphilic molecules of microbial origin that are capable of interacting with the lipidic components of microorganisms. Biosurfactant interactions with different surfaces can affect their hydrophobic properties and as a result, their ability to alter microorganisms’ adhesion abilities and consequent biofilm formation. Unlike synthetic surfactants, biosurfactants present low toxicity and high biodegradability and remain stable under temperature and pH extremes, making them potentially suitable for targeted use in medical and pharmaceutical applications. This review discusses the development of biosurfactants in biomedical and therapeutic uses as antimicrobial and antibiofilm agents, in addition to considering the potential synergistic effect of biosurfactants in combination with antibiotics. Furthermore, the anti-cancer and anti-viral potential of biosurfactants in relation to COVID-19 is also discussed

LARNet:Towards Lightweight, Accurate and Real-time Salient Object Detection

Salient object detection (SOD) has rapidly developed in recent years, and detection performance has greatly improved. However, the price of these improvements is increasingly complex networks that require more computing resources and sacrifice real-time performance. This makes it difficult to deploy these approaches on devices with limited computing resources (such as mobile phones, embedded platforms, etc.). Considering recently developed lightweight SOD models, their detection and real-time performance are always compromised in demanding practical application scenarios. To solve these problems, we propose a novel lightweight SOD method called LARNet and its corresponding extremely lightweight method LARNet* according to application requirements. These methods balance the relationship between lightweight requirements, detection accuracy and real-time performance. First, we propose a saliency backbone network tailored for SOD, which removes the need for pre-training with ImageNet and effectively reduces feature redundancy. Subsequently, we propose a novel context gating module (CGM), which simulates the physiological mechanism of human brain neurons and visual information processing, and realizes the deep fusion of multilevel features at the global level. Finally, the saliency map is output after fusion of multi-level features. Extensive experiments on popular benchmark datasets demonstrate that the proposed LARNet (LARNet*) achieves 98 (113) FPS on a GPU and 3 (6) FPS on a CPU. With approximately 680K (90K) parameters, the model has significant performance advantages over (extremely) lightweight methods, even surpassing some heavyweight model

Urban Revitalization in Small Cities across the Atlantic Ocean

City centers and riverfronts across the Atlantic Ocean have undergone substantial transformation over the last two decades. This paper analyzes walk-only precincts and waterfront revitalization in two pairwise cases (PCs) of small city exemplars on two continents in locations at about the same latitude but separated by the Atlantic Ocean. The argument is twofold. First, to be fully effective, city center revitalization interventions need to be coordinated with appropriate institutional programs to create collaborative management opportunities among multiple civic and business agents. Second, multiple cultural offerings, environmental amenities, and pro-active leadership positionalities have contributed positively to the evolution of waterfront community economic redevelopment opportunities in riverfront locations. The methods involved multiple site visits to cities of various sizes on the Iberian Peninsula and the Northeast of the United States at different times during the last twenty years, extensive literature reviews and syntheses, data analyses, assessment of policy priorities, and interviews with employees in various economic sectors, business owners, residents, elected officials, planning professionals, and community leaders. Two of the main conclusions are that, to be fully effective, the public space interventions on the Iberian Peninsula had to be coordinated with appropriate regulatory and institutional programs to generate collaborations with multiple civic and business agents and that the Northeastern cities have attempted to revitalize their riverfronts by conserving water-based and urban historic assets and amenities from further erosion due to downpours and floods as well as socio-economic and cultural transformations.</p

Corporate capital structure effects on corporate performance pursuing a strategy of innovation in manufacturing companies

Within the sphere of finance, the concept of capital structure has long been a subject of intense debate, serving as a quantitative depiction of the balance between debt, preference shares, and common stock within a company. This structure serves a crucial role in optimizing the utilization of a company's existing resources while simultaneously elevating the revenue streams for stakeholders. This particular study delves into the intricate relationship between corporate performance and capital structure, focusing on 78 publicly listed firms within the Dhaka Stock Exchange (DSE). Bangladesh holds the 29th position globally in terms of purchasing power, lending significant weight to this investigation. To comprehensively analyze this correlation, panel data encompassing the span from 2017 to 2021 was collected for these 78 sample companies operating within the DSE. Several key determinants of capital structure were considered in this analysis, namely the debt-to-equity ratio, short-term leverage ratio, long-term leverage ratio, and total debt ratio. Meanwhile, the performance of these firms was gauged using key metrics such as Return on Assets (ROA), Return on Equity (ROE), and Earnings Per Share (EPS). To ensure a robust analysis, factors such as inflation, liquidity, growth rate, tax rate, and firm size were meticulously controlled for. The findings unveiled a compelling narrative: all forms of debt ratios—be it short-term, long-term, or the total debt ratio—exhibited a substantial negative impact on ROA at a significant level of 1 %. Conversely, specific debt ratios, like the short-term total debt and the total debt-to-total asset ratio, displayed a notable positive correlation with ROE at a 1 % significance level. Intriguingly, the long-term total debt ratio yielded a negative and insignificant effect on ROE. Moreover, within the spectrum of predictors influencing a firm's performance, the liquidity ratio emerged as a non-significant factor—a notable discovery that highlights the nuanced nature of the interplay between capital structure and performance within these companies.</p

CDNA-SNN: A New Spiking Neural Network for Pattern Classification using Neuronal Assemblies

Spiking neural networks (SNNs) mimic their biological counterparts more closely than their predecessors and are considered the third generation of artificial neural networks. It has been proven that networks of spiking neurons have a higher computational capacity and lower power requirements than sigmoidal neural networks. This paper introduces a new type of spiking neural network that draws inspiration and incorporates concepts from neuronal assemblies in the human brain. The proposed network, termed as CDNA-SNN, assigns each neuron learnable values known as Class-Dependent Neuronal Activations (CDNAs) which indicate the neuron’s average relative spiking activity in response to samples from different classes. A new learning algorithm that categorizes the neurons into different class assemblies based on their CDNAs is also presented. These neuronal assemblies are trained via a novel training method based on Spike-Timing Dependent Plasticity (STDP) to have high activity for their associated class and low firing rate for other classes. Also, using CDNAs, a new type of STDP that controls the amount of plasticity based on the assemblies of pre- and post-synaptic neurons is proposed. The performance of CDNA-SNN is evaluated on five datasets from the UCI machine learning repository, as well as MNIST and Fashion MNIST, using nested cross-validation for hyperparameter optimization. Our results show that CDNA-SNN significantly outperforms SWAT (p&lt;0.0005) and SpikeProp (p&lt;0.05) on 3/5 and SRESN (p&lt;0.05) on 2/5 UCI datasets while using the significantly lower number of trainable parameters. Furthermore, compared to other supervised, fully connected SNNs, the proposed SNN reaches the best performance for Fashion MNIST and comparable performance for MNIST and N-MNIST, also utilizing much less (1-35%) parameters

How far can low emission retrofit of terraced housing in Northern Ireland go?

With both global and national targets to reduce greenhouse gas (GHG) emissions the improvement of existing buildings will be key to realising these ambitions. How this can be achieved, and the impact of whole-life emissions from retrofit remains a key question. This paper investigates the potential of retrofit to reduce and limit lifecycle GHG emissions resulting from an existing house, typical of one of the predominant housing typologies in Northern Ireland. Through the use of lifecycle assessment a range of retrofit scenarios are considered for an early 20th century, solid wall, terraced house, to understand the impacts of retrofit on lifecycle emissions. A range of retrofit scenarios were modelled and simulated, considering both embodied and operational emissions over the building’s lifetime, to understand how net emissions can be reduced. The results show that although fabric and some technological measures can reduce emissions by over 60% when applied in isolation, a holistic approach is required to achieve the greatest reductions. Although operation remains the largest single source of emissions, the results also show the importance of taking a holistic approach to the assessment of retrofit with varying lifecycle stages responsible for considerable emissions. It is seen that emissions reductions of up to 99% may be possible when taking a holistic approach to retrofit and its assessment, considering whole-life emissions. This study highlights the potential benefits of retrofit and how it could be effectively applied to the existing housing stock in Northern Ireland creating low-emission or net-zero emission buildings

ASL Fingerspelling Classification for use in Robot Control

This paper proposes a gesture based control system for industrial robots. To achievethat goal, the performance of an image classifier trained on 3 different American Sign Language (ASL) fingerspelling image datasets is considered. Then, the three are combined into a single larger dataset, and the classifier trained on that. The results of this process is then compared with the original three

Embracing the <i>impact </i>from instrumented mouthguards (iMGs): A survey of iMG managers' perceptions of staff and player interest into the technology, data and barriers to use

Instrumented mouthguards (iMGs) are a novel technology being used within rugby to quantify head acceleration events. Understanding practitioners' perceptions of the barriers and facilitators to their use is important to support implementation and adoption. This study assessed men's and women's rugby union and league iMG managers' perceptions of staff and player interest in the technology, data and barriers to use. Forty‐six iMG managers (men's rugby union and league n = 20 and n = 9 and women's rugby union and league n = 7 and n = 10) completed an 18‐question survey. Perceived interest in data varied across staff roles with medical staff being reported as having the most interest. The iMG devices were perceived as easy to use but uncomfortable. Several uses of data were identified, including medical applications, player monitoring and player welfare. The comfort, size and fit of the iMG were reported as the major barriers to player use. Time constraints and a lack of understanding of data were barriers to engagement with the data. Continued education on how iMG data can be used is required to increase player and staff buy‐in, alongside improving comfort of the devices. Studies undertaken with iMGs investigating player performance and welfare outcomes will make data more useful and increase engagement