Ad-hoc Solutions Research Articles

Use of Censored Data Methods to Estimate Blood Pressure Percentiles in US Adults

Abstract Blood pressure (BP) levels are important modifiable risk factors for incident cardiovascular disease (CVD). However, many adults are on anti-hypertensive medication (MEDS) which lowers their BP but is a marker for increased CVD risk. To capture the association of BP and its treatment with risk, it is important to estimate BP levels in the absence of medication, i.e., Underlying BP. One proposed ad-hoc solution is to add a fixed increment e.g., 10/5 mm Hg to systolic (SBP)/diastolic (DBP) for people on MEDS to approximate their Underlying BP. However, what this increment should be, and whether it applies to all MEDS users is unclear. In this manuscript, we propose a novel time-to-event approach to estimate Underlying BP, treating BPs on MEDS as censored observations. The results indicate that the median Treatment Effect (i.e., Underlying BP – Observed BP) for SBP/DBP is 12.0/7.7 for men and 16.9/6.5 for women and is significantly modified by age, BMI and black race. The Underlying BP for MEDS users is then combined with the observed BP for non-MEDS users to derive age-sex-specific BP percentiles for Underlying BP based on a representative sample of normal weight U.S. adults aged 18-79 using NHANES data from 1999-2018.

Deep learning model for optimizing control and planning in stochastic manufacturing environments

Within the context of Industry 4.0, manufacturing plants implement smart technologies, which adopt machine learning and deep learning, to identify manufacturing problems and provide sensible solutions to the experts. In literature, such applications have demonstrated high-performance in terms of feature extraction and big data analysis compared to typical machine learning methods. However, in the context of control and planning, the research efforts are still focused on integrating ad-hoc solutions, e.g., heuristic algorithms and deep reinforcement learning, thus making it difficult to generate general scheduling rules for manufacturing processes that could be easily applied to a variety of stochastic manufacturing systems. To this end, this paper considers a deep learning application for addressing the scheduling optimization problem in multi-stage stochastic manufacturing environments. The aim is the formulation of a predictive model that could derive suitable authorizations for activities under uncertainty. Findings demonstrate that the proposed predictive model generates policies that improve the performance of the integrated operations within manufacturing systems, constituting to efficient and sustainable manufacturing with effective material management and near-to-zero waste.

Transparent Image Layer Diffusion using Latent Transparency

We present an approach enabling large-scale pretrained latent diffusion models to generate transparent images. The method allows generation of single transparent images or of multiple transparent layers. The method learns a "latent transparency" that encodes alpha channel transparency into the latent manifold of a pretrained latent diffusion model. It preserves the production-ready quality of the large diffusion model by regulating the added transparency as a latent offset with minimal changes to the original latent distribution of the pretrained model. In this way, any latent diffusion model can be converted into a transparent image generator by finetuning it with the adjusted latent space. We train the model with 1M transparent image layer pairs collected using a human-in-the-loop collection scheme. We show that latent transparency can be applied to different open source image generators, or be adapted to various conditional control systems to achieve applications like foreground/background-conditioned layer generation, joint layer generation, structural control of layer contents, etc. A user study finds that in most cases (97%) users prefer our natively generated transparent content over previous ad-hoc solutions such as generating and then matting. Users also report the quality of our generated transparent images is comparable to real commercial transparent assets like Adobe Stock.

Bracing for the next wave: A critical incident study of frontline decision-making, adaptation and learning in ambulance care during COVID-19.

To explore frontline decision-making, adaptation, and learning in ambulance care during the evolving COVID-19 pandemic. Descriptive and interpretative qualitative study. Twenty-eight registered nurses from the Swedish ambulance services described 56 critical incidents during the COVID-19 pandemic through free-text questionnaires. The material was analysed using the Critical Incident Technique and Interpretive Description through the lens of potential for resilient performance. The findings were synthesized into four themes: 'Navigating uncharted waters under never-ending pressure', 'Balancing on the brink of an abyss', 'Sacrificing the few to save the many' and 'Bracing for the next wave'. Frontline decision-making during a pandemic contribute to ethical dilemmas while necessitating difficult prioritizations to adapt and respond to limited resources. Learning was manifested through effective information sharing and the identification of successful adaptations as compared to maladaptations. During pandemics or under other extreme conditions, decisions must be made promptly, even amidst emerging chaos, potentially necessitating the use of untested methods and ad-hoc solutions due to initial lack of knowledge and guidelines. Within ambulance care, dynamic leadership becomes imperative, combining autonomous frontline decision-making with support from management. Strengthening ethical competence and fostering ethical discourse may enhance confidence in decision-making, particularly under ethically challenging circumstances. Performance under extreme conditions can elevate the risk of suboptimal decision-making and adverse outcomes, with older adults being especially vulnerable. Thus, requiring targeted decision support and interventions. Enhancing patient safety in ambulance care during such conditions demands active participation and governance from management, along with decision support and guidelines. Vertical communication and collaboration between management and frontline professionals are essential to ensure that critical information, guidelines, and resources are effectively disseminated and implemented. Further research is needed into management and leadership in ambulance care, alongside the ethical challenges in frontline decision-making under extreme conditions. Findings are reported per consolidated criteria for reporting qualitative research (COREQ). No Patient or Public Contribution.

Evolving pathways towards water security in the Vietnamese Mekong Delta: An adaptive management perspective.

The interplay of climate change, upstream hydropower development, and local water engineering interventions for agricultural production contributes substantially to the transformation of waterscapes and water scarcity in the Vietnamese Mekong Delta. This paper aims to examine how these dynamics are linked to the paradigm shift in water management in An Giang and Ben Tre, the two ecologically distinct provinces that face serious water scarcity in the delta. We used the adaptive management concept to examine how state-led policy directions from food security towards water security enable change in water management that gives priority to water retention. While policy learning is evident, questions remain about how this ad-hoc solution could help address the presently acute water scarcity and water security over the long term. The paper advocates achieving water security should focus not only on diplomatic interventions into upstream climate-development complexities but also local water-livelihood politics.

Towards Resilient Energy Forecasting: A Robust Optimization Approach

Energy forecasting models deployed in industrial applications face uncertainty w.r.t. data availability, due to network latency, equipment malfunctions or data-integrity attacks. In particular, the case when a subset of features that has been used for model training becomes unavailable when the model is used operationally poses a major challenge to forecasting performance. Ad-hoc solutions, e.g., retraining without the missing features, may work for a small number of features, but they soon become impractical, as the number of models grows exponentially with the number of features. In this work, we present a principled approach to introducing resilience against missing features in energy forecasting applications via robust optimization. Specifically, we formulate a robust regression model that is optimally resilient against missing features at test time, considering both point and probabilistic forecasting. We develop three solution methods for the proposed robust formulation, all leading to Linear Programming problems, with varying degrees of tractability and conservativeness. We provide an extensive empirical validation of the proposed methods in prevalent applications, namely, electricity price, load, wind production, and solar production, forecasting, and we further compare against well-established benchmark models and methods of dealing with missing features, i.e., imputation and retraining. Our results demonstrate that the proposed robust optimization approach outperforms imputation-based models and exhibits similar performance to retraining without the missing features, while also maintaining computational practicality. To the best of our knowledge, this is the first work that introduces resilience against missing features into energy forecasting.

Towards time-evolving analytics: Online learning for time-dependent evolving data streams

Traditional historical data analytics is at risk in a world where volatility, uncertainty, complexity, and ambiguity are the new normal. While Streaming Machine Learning (SML) and Time-series Analytics (TSA) attack some aspects of the problem, we still need a comprehensive solution. SML trains models using fewer data and in a continuous/adaptive way relaxing the assumption that data points are identically distributed. TSA considers temporal dependence among data points, but it assumes identical distribution. Every Data Scientist fights this battle with ad-hoc solutions. In this paper, we claim that, due to the temporal dependence on the data, the existing solutions do not represent robust solutions to efficiently and automatically keep models relevant even when changes occur, and real-time processing is a must. We propose a novel and solid scientific foundation for Time-Evolving Analytics from this perspective. Such a framework aims to develop the logical, methodological, and algorithmic foundations for fast, scalable, and resilient analytics.

Implementing Serverless Architecture: Discuss the practical aspects and challenges

Function as a Service (FaaS) has garnered significant attention for its approach to deploying computations to serverlessbackends across various cloud environments. It simplifies the complexity of provisioning and managing resources for applications by leveraging cloud providers' capabilities, offering users an illusion of perpetual resource availability. Among these providers, the AWS serverless platform stands out, offering a novel paradigm for cloud application development, abstracting away concerns about underlying hardware infrastructure while ensuring scalability, security, and cost-effectiveness.However, due to the absence of standardized benchmarks, serverless functions often rely on ad-hoc solutions for building cost-efficient and scalable applications. The development of the SeBS framework has addressed this gap, enabling comprehensive testing, evaluation, and performance analysis across different cloud providers. While previous research has explored serverless platforms among various providers, little attention has been given to AWS Lambda service's performance within the ARM64 architecture and its comparison with traditional x86 architectures.

Unyielding humanity from catastrophic ruins: new political society for social and environmental justice after Bhopal

ABSTRACT This paper analyzes the ways in which a site of catastrophic ruins transpires as a new political society for critical social and environmental justice. Predicated on two and half months of fieldwork in Bhopal – consisting of 60 interviews with gas and water victims and activists, archival research, and observation of sites and events – the paper specifically explains how the Bhopal Movement, the longest-running social movement in post-colonial India, has become an exemplar of what I call ‘unyielding humanity,’ one that rejects conciliatory posturing and ad-hoc solutions, constraining states and corporations alike to meaningfully address the ongoing social and environmental mutilation of Bhopal. Underlining the agency, targets, means, objectives, and determinations of the subaltern people, the paper illustrates how this women-led movement poses triumphant challenges to dominant narratives by focusing on the politics of long-term, intergenerational suffering. Findings indicate that prolonged marginalization can give rise to distinct forms of politics, diverging not only from labor and identity politics but also from various environmental justice movements that have been theorized emphasizing primarily material, visible, and immediate consequences. The paper holds implications for social and environmental justice struggles worldwide.

Accuracy and precision of fixed and random effects in meta-analyses of randomized control trials for continuous outcomes.

Meta-analyses of treatment effects in randomized control trials are often faced with the problem of missing information required to calculate effect sizes and their sampling variances. Particularly, correlations between pre- and posttest scores are frequently not available. As an ad-hoc solution, researchers impute a constant value for the missing correlation. As an alternative, we propose adopting a multivariate meta-regression approach that models independent group effect sizes and accounts for the dependency structure using robust variance estimation or three-level modeling. A comprehensive simulation study mimicking realistic conditions of meta-analyses in clinical and educational psychology suggested that imputing a fixed correlation 0.8 or adopting a multivariate meta-regression with robust variance estimation work well for estimating the pooled effect but lead to slightly distorted between-study heterogeneity estimates. In contrast, three-level meta-regressions resulted in largely unbiased fixed effects but more inconsistent prediction intervals. Based on these results recommendations for meta-analytic practice and future meta-analytic developments are provided.

URLGEN—Toward Automatic URL Generation Using GANs

URLs play an essential role on the Internet, allowing access to Web resources. Automatically generating URLs is helpful in various tasks, such as application debugging, API testing, and blocklist creation for security applications. Current testing suites deeply embed experts’ domain knowledge to generate suitable URLs, resulting in an ad-hoc solution for each given application. These tools thus require heavy manual intervention, with the expensive coding of rules that are hard to maintain. We here introduce URLGEN, a system that uses Generative Adversarial Networks (GANs) to tackle the automatic URL generation problem. URLGEN is designed for web API testing and generates URL samples for an application without any system expertise, complementing the existing tools. It leverages Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) architectures, augmented by an embedding layer that simplifies the URL learning and generation process. We show that URLGEN learns to generate new valid URLs from samples of real URLs without requiring any domain knowledge and following a purely data-driven approach. We compare the GAN architecture of URLGEN against other design options and show that the LSTM architecture can better capture the correlation among URL characters, outperforming previously proposed solutions. Finally, we show that the URLGEN approach can be extended to other scenarios, which we illustrate with two use cases, i.e., cybersquatting domain prediction and URL classification.

Ad-hoc solutions for ultrasonic inspection of highly complex aircraft composite structures

Nowadays, the ultrasonic inspection (UT) is the most common non destructive technique (NDT) for composite components in the aerospace industry, due to its high accuracy, reliability and the degree of industrialization. Although UT is widely developed, new manufacturing processes and part concepts are continuously pushing the technology for new improvements and applications. This is the case of the geometries resulting from novel out of autoclave composites manufacturing technologies, Liquid Resin Infusion (LRI) or Resin Transfer Moulding (RTM), allow the fabrication of parts in one shoot, with focus in bringing answers to the challenge of manufacturing fully assembled parts and avoiding secondary assembly phases but also limiting the accessibility for NDT inspections. In this sense, one of the critical issues in the aeronautic sector is the improvement of the inspection of geometries with difficult access and small dimensions. The requirement of the aeronautic industry of guaranteeing the quality of the primary composite structures implies the inspection of the whole component through certified technologies. This constraint, together with the lack of technological solutions for the inspection of regions with limited accessibility is blocking the industrial implementation of optimized manufacturing processes. The present work describes the design and development of adaptive hardware solutions for the single and phased array UT inspection of internal structures on a highly integrated composite aeronautic component. The final solution has been obtained as result of an iterative design process and manufactured through additive manufacturing technology (Powder Bed Laser Fusion, formerly Selective Laser Sintering) using polyamide. This device was validated in a for inspection of flat surfaces of stiffener internal structure, belonging to the composite flap demonstrators of lengths: 3 meters in a first approach, and 8 m for final validation. This novel inspection solution has been performed in the framework of the FLAP project to develop and innovate the manufacturing process for complex mobile surfaces in a collaboration between Aernnova, through its Composites Division, and CATEC teams.

Post War Justice: Jus Post Bellum for Just War and Peace

This paper aims to address a key topic of speculation within political philosophy, namely the Just War Theory. The Just War Theory works to ethically restrain wars based on principles listed out in jus ad bellum (reasons to go to war) and jus in bello (conduct during war). As such, the theory dominated by the debate between the ‘traditionalists’ and ‘revisionists’ who are concerned about the integration of jus post bellum as the third branch of just war theory and feel it is better suited to the domain of international politics and security. This paper explores this lacunae over the neglect of jus post bellum (post war justice) within the just war discourse. By identifying the limitations of a minimalist jus post bellum and this misrepresentation of peace with security, this paper defends a maximalist account of jus post bellum and also situates post war justice as a necessary third branch of the Just War Theory. The challenges to taking such an extensive stance on post war justice are also addressed. As the first two branches of just war theory have been codified, the neglect of the post war stage leads to unrestrained war endings and ad-hoc solutions. Any just war theory needs to recognize the legitimacy of the third branch of post war justice as well, as only then can the Just War Theory function to restrain wars.

The "No bed syndrome" in Ghana - what, how and why? A literature, electronic and print media review.

"No bed syndrome" has become a familiar phrase in Ghana. Yet, there is very little in medical texts or the peer reviewed literature about it. This review aimed to document what the phrase means in the Ghanaian context, how and why it occurs, and potential solutions. A qualitative desk review using a thematic synthesis of grey and published literature, print and electronic media content covering the period January 2014 to February 2021. Text was coded line by line to identify themes and sub-themes related to the research questions. Analysis was manual and with Microsoft Excel to sort themes. Ghana. Not applicable. "No bed syndrome" describes the turning away by hospitals and clinics of people seeking walk in or referral emergency care with the stated reasons "no bed available" or "all beds are full". There are reported cases of people dying while going round multiple hospitals seeking help and being repeatedly turned away because there is "no bed". The situation appears to be most acute in the highly urbanized and densely populated Greater Accra region. It is driven by a complex of factors related to context, health system functions, values, and priorities. The solutions that have been tried are fragmented rather than well-coordinated whole system reform. The "no bed syndrome" describes the challenge of a poorly functioning emergency health care system rather than just the absence of a bed on which to place an emergency case. Many low and middle income countries have similar challenges with their emergency health care systems and this analysis from Ghana is potentially valuable in attracting global attention and thinking about emergency health systems capacity and reform in low and middle income countries. The solution to the "no bed syndrome" in Ghana requires reform of Ghana's emergency healthcare system that takes a whole system and integrated approach. All the components of the health system such as human resource, information systems, financing, equipment tools and supplies, management and leadership need to be examined and addressed together alongside health system values such as accountability, equity or fairness in the formulation, implementation, continuous monitoring and evaluation of policies and programs for system reform to expand and strengthen emergency healthcare system capacity and responsiveness. Despite the temptation to fall back on them as low hanging fruit, piecemeal and ad-hoc solutions cannot solve the problem.

The Responses of Pakistan and Turkey to Refugee Influxes: A Comparative Analysis of Durable Solutions to Protracted Displacements

This research aims to study how Turkey and Pakistan (the world’s largest refugee hosting countries) have reacted to and managed protracted refugee challenges. In the context of Pakistan, the study explores the Afghan refugee influx since the late 1970, while for Turkey the focus is on the Syrian refugee inflow since 2011. While cognizant of contextual differences (time and space) between these two countries, we aim to comparatively explore policies, best practices, and short and long-term solutions developed to protracted displacement issues in each country. To do this we intend to comparatively explore the role of institutional environments and the international refugee regime in resolving protracted refugee issues in a chronological sequence. First, we investigate how each state’s “open-door policies” toward the first influx of refugees evolved into ad-hoc solutions in the course of protracted displacement. Second, we comparatively assess multilevel integration approaches and solution strategies at the macro (states and their cooperation with countries of origin and inter-governmental organizations such as the UN High Commissioner for Refugees (UNHCR) and International Organization for Migration (IOM)), meso (interventions/implementations), and micro levels (civil society and refugee-led initiatives). Third, we examine how refugee policies in each country are negotiated with national and international humanitarian and refugee agencies, including regional organizations and countries of origin. Theoretically, this article is grounded on a macro structural approach (state’s international humanitarian obligations and responsibilities), refugee diplomacy (state’s use of diplomatic tools, process administering refugees), and management theories.

RECA: Related Tables Enhanced Column Semantic Type Annotation Framework

Understanding the semantics of tabular data is of great importance in various downstream applications, such as schema matching, data cleaning, and data integration. Column semantic type annotation is a critical task in the semantic understanding of tabular data. Despite the fact that various approaches have been proposed, they are challenged by the difficulties of handling wide tables and incorporating complex inter-table context information. Failure to handle wide tables limits the usage of column type annotation approaches, while failure to incorporate inter-table context harms the annotation quality. Existing methods either completely ignore these problems or propose ad-hoc solutions. In this paper, we propose Related tables Enhanced Column semantic type Annotation framework (RECA), which incorporates inter-table context information by finding and aligning schema-similar and topic-relevant tables based on a novel named entity schema. The design of RECA can naturally handle wide tables and incorporate useful inter-table context information to enhance the annotation quality. We conduct extensive experiments on two web table datasets to comprehensively evaluate the performance of RECA. Our results show that RECA achieves support-weighted F1 scores of 0.853 and 0.937 with macro average F1 scores of 0.674 and 0.783 on the two datasets respectively, which outperform the state-of-the-art methods.

High granularity small-pad resistive micromegas for rates above MHz/cm[formula omitted

An R&D project has been recently started to consolidate the technology of resistive Micromegas for operations well beyond the actual operations at HEP experiments, aiming at stable, reliable, and high gain operation up to particle fluxes of the order of 10 MHz/cm2, over large surfaces.To cope with these challenges, readout copper pads, of a few mm2 size, have been proposed to reduce the occupancy of the readout elements, calling for innovative solutions for the spark protection resistive scheme. It is known that single stage amplification Micro Pattern Gaseous Detectors suffer from sparks when operated under harsh environments. Resistive anodes drastically mitigate the spark intensities but, on the other hand, they reduce the rate capability when high currents flow into the detectors, generating a drop in the amplification voltage. Ad-hoc solutions must be adopted.Two resistive schemes have been studied. The first one is based on a pad-patterned resistive double layer, superimposed to the readout pads, with an embedded resistor connecting the resistive pads. In this scheme, each pad is independent from the others. The second scheme exploits the recently developed Diamond-Like Carbon (DLC) resistive foils. A double layer of DLC is superimposed to the readout pads, with a grid of interconnecting vias to ground for a fast evacuation of the accumulated charge. In this case the pads are not completely independent since the charge can spread over more pads. For each of these resistive schemes, detectors with different configurations and construction techniques have been built.All detectors have been thoroughly tested and fully characterized with radioactive sources, X-rays and with test beams carried out at CERN in 2021. The performance and achievements in terms of gains, rate capabilities, energy, space and time resolutions will be reported, along with a detailed comparison among the different schemes and configurations.

Usability Tests for Texture Comparison in an Electroadhesion-Based Haptic Device

Haptic displays have been gaining more relevance over the recent years, in part because of the multiple advantages they present compared with standard displays, especially for improved user experience and their many different fields of application. Among the various haptic technologies, electroadhesion is seen as capable of better interaction with a user, through a display. TanvasTouch is an economically competitive haptic device using electroadhesion, providing an API and respective haptic engine, which makes the development of applications much easier and more systematic than in the past, back when the creation of these haptic solutions required a greater amount of work and resulted in ad-hoc solutions. Despite these advantages, it is important to access its ability to describe textures in a way understandable by the user’s touch. The current paper presents a set of experiments using TanvasTouch electroadhesion-based haptic technology to access how a texture created on a TanvasTouch device can be perceived as a representation of a real-world object.

An Ad-hoc Solution for Ear Anatomy Acquisition in Pediatric Setting

Computer-Aided Design and Applications is an international journal on the applications of CAD and CAM. It publishes papers in the general domain of CAD plus in emerging fields like bio-CAD, nano-CAD, soft-CAD, garment-CAD, PLM, PDM, CAD data mining, CAD and the internet, CAD education, genetic algorithms and CAD engines. The journal is aimed at all developers and users of CAD technology to ptovide CAD solutions for various stages of design and manufacturing. The journal publishes all about Computer-Aided Design and Computer-Aided technologies.

PROACTIVE MONITORING FOR SMEs USING APPINSIGHT

Data Security is a worldwide concern mostly for small medium enterprise (SMEs) and frameworks, approaches, methods are constantly evolving that has a connection with cloud computing, information systems, artificial intelligence, blockchain. Many developers, administrators or product teams running blind. Those are not knowing of problems with their application or do not have the information to fix the problems. The things which can go wrong with web and mobile applications or services is unlimited like dependency failures, resources, and crashes. Main argument is an evaluation of benefits by using Cloud as infrastructure and application on proactive monitoring called Azure Application Insights (AppInsight) towards target like web application, web API, PKI etc. The findings, demonstration of the study should reveal and support our main hypothesis that there is direct link between the proactive monitoring and the main factors that affects utilizing the cloud services. To address this need, in this paper, we introduce AppInsight, the best practice and a model of proactive approach to monitor different targets using Microsoft technology on Azure Cloud services. AppInsight – a model of proactive monitoring includes several functionalities: (1) identifying availability, (2) failures dependencies, (3) performance and (4) using telemetry data generates ad-hoc solution to fix potential failure of web application, web API etc. AppInsight a feature of Azure Monitor used to monitor live applications. AppInsight will automatically detect performance anomalies, and includes powerful analytics tools to help you diagnose issues. You will get a range of telemetry data of analytics of your target which is monitored by AppInsight. To evaluate this tool, we conduct an empirical evaluation by comparing data from actual live monitoring of Y target. Demo Video: https://www.youtube.com/watch?v=q7R8-c0ge7M