Prototype Case Study Research Articles

The Sectoral Politics of Industrial Policy Making in Brazil: A Polanyian Interpretation

ABSTRACTThis article considers why Brazilian industrial policies have varied across sectors since the mid‐1990s. It relies on a Polanyian‐inspired framework to propose that the strength of counter‐movements against corporate welfare shapes the sector‐specific capacity of policy makers to exert state discipline over business interests and diverges from neoliberal scripts of industrial policy making. The authors use prototypical case studies on the automotive, animal protein and pharmaceutical sectors to support their argument. In the automotive industry, the continuous pressure from powerful and cohesive labour unions led to the emergence of a neo‐corporatist sectoral regime that was characterized by a tripartite policy design and encompassed conditionalities. In the case of animal protein, the lack of bottom‐up pressure culminated in a disembedded neoliberal sectoral regime, in which business owners received almost unconditional benefits, turning industrial policies into corporate welfare. Finally, in the pharmaceutical industry, the combination of diffuse societal demands and unions with intermediate relevance led to an embedded neoliberal sectoral regime that combined selective conditionalities with some space for non‐business participation in policy design.

Forecasting with an N-dimensional Langevin equation and a neural-ordinary differential equation.

Accurate prediction of electricity day-ahead prices is essential in competitive electricity markets. Although stationary electricity-price forecasting techniques have received considerable attention, research on non-stationary methods is comparatively scarce, despite the common prevalence of non-stationary features in electricity markets. Specifically, existing non-stationary techniques will often aim to address individual non-stationary features in isolation, leaving aside the exploration of concurrent multiple non-stationary effects. Our overarching objective here is the formulation of a framework to systematically model and forecast non-stationary electricity-price time series, encompassing the broader scope of non-stationary behavior. For this purpose, we develop a data-driven model that combines an N-dimensional Langevin equation (LE) with a neural-ordinary differential equation (NODE). The LE captures fine-grained details of the electricity-price behavior in stationary regimes but is inadequate for non-stationary conditions. To overcome this inherent limitation, we adopt a NODE approach to learn, and at the same time predict, the difference between the actual electricity-price time series and the simulated price trajectories generated by the LE. By learning this difference, the NODE reconstructs the non-stationary components of the time series that the LE is not able to capture. We exemplify the effectiveness of our framework using the Spanish electricity day-ahead market as a prototypical case study. Our findings reveal that the NODE nicely complements the LE, providing a comprehensive strategy to tackle both stationary and non-stationary electricity-price behavior. The framework's dependability and robustness is demonstrated through different non-stationary scenarios by comparing it against a range of basic naïve methods.

Modeling uncertainties associated with multi-attribute decision-making based evaluation of cooling system using interval-valued complex intuitionistic fuzzy hypersoft settings

<abstract><p>Academics encounter a challenge regulating data-driven unpredictability in numerous complicated decision scenarios. Regulating the cyclical nature of appraisal attributes, determining lower and higher limitations, granting multi-parametric values as a means of assessing argumentation, and modeling uncertainty are a few examples of these problems. It requires the incorporation of complex plane settings, interval-valued intuitionistic fuzzy settings, and hypersoft settings. Inspired by these kinds of scenarios, the goal of this research was to articulate a new theoretical framework, the interval-valued complex intuitionistic fuzzy hypersoft set ($ \Gamma $-set), which can handle these kinds of problems as a whole under the umbrella of a single framework. First, the concepts of $ \Gamma $-set, as well as its set operations and aggregations, such as decision matrix, cardinal matrix, aggregate matrix, and cardinality set, were examined. The second phase offers an appealing algorithm that consists of nine steps that go from taking into account necessary set construction to making the best choice. A prototype case study analyzing eighteen evaluation qualities and thirty-four sub-attributes for determining an optimal cooling system ($ \mathbb{CSYS}) $ for a factory validates the provided algorithm. Informative comparison analysis and preferred study features were provided as essential components of research to assist academics in making significant advances regarding their field and gradually, but thoroughly, advancing their specialization.</p></abstract>

From Urban Vulnerabilities to Resilience: Lessons from Messina's Integrated Risk Approach

In the city of Messina, the multiplicity of vulnerable situations is a distinctive feature and is representative of the pervasiveness of the risk conditions present in the Italian territory. The critical residential tissues are part of an extended geography in which, to the conditions of seismic and hydro geomorphological vulnerability, are added additional criticalities including altered natural resources, abandoned infrastructure, brownfields, quarries, and unregulated landfills, etc. Despite this, for Messina, there is still no urban planning tool capable of interpreting in an integrated way the risks present, which instead continue to be addressed on an emergency basis and individually, without considering the interactions that are generated and of further damage. For these reasons, Messina was chosen as a prototypical case study, at the national level, to start an experimentation aimed at developing an urban planning tool capable of a new integrated approach to risk interpretation and management. In this process, a decisive role was aimed at building an updated knowledge of the territory through the identification of widespread critical conditions using multiple GIS tools and thanks to the interaction of some analytical-specialist readings from different sources. The results of this process are represented in a system of maps that are strongly integrated with each other and constitute a working document for the interdisciplinary group drafting the General Urban Plan.

Occupants' interaction with building services: Development of a camera-based method for detailed monitoring of windows, shadings, and lights

Several energy gains or losses in buildings are influenced by occupant interactions with building services, such as lighting or HVAC systems, thermostat settings, and window and shading operations. Occupant behavior is usually triggered by discomfort, nevertheless actions taken to restore comfort can have an impact on final energy demand. Thus, an accurate energy assessment for both new and retrofit building design must properly account for occupant behavior, based on reliable models developed from real case studies and detailed monitoring. This work presents a new approach for continuous and non-intrusive monitoring of window opening angle, shading position, and lighting operation to determine the net air exchange area for ventilation. A camera-based device and a post-processing algorithm are developed, and a monitoring campaign over 6 month is carried out to showcase the monitoring system. The device consists of a camera setup connected to a microprocessor, and a dedicated script which enables the device to track window opening, shading movement and lighting operation through target and object identification. Results of the prototyping case study show that the proposed system can effectively detect window opening angles and shutter positions, dealing with multiple windows and shutters simultaneously and allowing the deployment of the benefits of continuous monitoring. The explored application is the direct use of the collected data for the calculation of natural ventilation rates from the net exchange area (EN 16798-7) over long term datasets. As future development, the monitoring system will be used to develop accurate behavioral models based on the experimental data to analyze and suggest the occupant’s response to discomfortable conditions in order to improve indoor air quality and save energy.

From policy to practice: how schools implement German language support policy in Austria

ABSTRACT In 2018/2019, the Government of Austria instituted a new language support model to Austrian schools that included segregated language support measures for non-fluent German-speaking students. The central research aim of the paper is to investigate how the implementation of this new language support model compares to the policy requirement by examining different models of implementation. In the framework of the initial phases of the project ‘A multi-perspective study on German language support’, 12 interviews with German language support teachers and school principals were conducted. Following a prototypical case study analysis, four models of implementation were identified: (1) Segregationmodel, (2) Mixed segregated and integrated model, (3) Individualized language support, and (4) Inclusive support model. Given that thisis the first study in Austria to show the concrete implementation models of German language support in schoolsFindings highlight important implications for school principals and educational policy makers.

Data-Driven Wind Farm Control via Multiplayer Deep Reinforcement Learning

This brief proposes a novel data-driven control scheme to maximize the total power output of wind farms subject to strong aerodynamic interactions among wind turbines. The proposed method is model-free and has strong robustness, adaptability, and applicability. Particularly, distinct from the state-of-the-art data-driven wind farm control methods that commonly use the steady-state or time-averaged data (such as turbines’ power outputs under steady wind conditions or from steady-state models) to carry out learning, the proposed method directly mines in-depth the time-series data measured at turbine rotors under time-varying wind conditions to achieve farm-level power maximization. The control scheme is built on a novel multiplayer deep reinforcement learning method (MPDRL), in which a special critic–actor–distractor structure, along with deep neural networks (DNNs), is designed to handle the stochastic feature of wind speeds and learn optimal control policies subject to a user-defined performance metric. The effectiveness, robustness, and scalability of the proposed MPDRL-based wind farm control method are tested by prototypical case studies with a dynamic wind farm simulator (WFSim). Compared with the commonly used greedy strategy, the proposed method leads to clear increases in farm-level power generation in case studies.

Self-Adaptive RISs Beyond Free Space: Convergence of Localization, Sensing, and Communication Under Rich-Scattering Conditions

We discuss the need for a confluence of localization, sensing, and communications if reconfigurable intelligent surfaces (RISs) are to be deployed in a self-adaptive manner in the dynamically evolving rich-scattering settings that are typical for important 6G deployment scenarios, such as factories. We establish that in such problems the rich-scattering wireless channels are subject to a highly nonlinear deterministic double-parametrization through both the RIS and uncontrolled moving objects. Therefore, acquiring full context-awareness through localization and sensing is a prerequisite for self-adaptive RIS-empowered communications. Yet, the byproducts of this daunting communications overhead can feed many appliances that require context awareness, such that overhead concerns may vanish. We illustrate the essential steps for operating a self-adaptive RIS under rich scattering based on a prototypical case study. We discover that self-adaptive RISs outperform context-ignorant RISs only below a certain noise threshold that depends, among other factors, on how strongly uncontrolled perturbers impact the wireless channel. We also discuss ensuing future research directions that will determine the conditions under which self-adaptive RISs may serve as technological enabler of 6G networks.

The child-friendly cities concept in China: A prototype case study of a migrant workers’ community

The United Nations International Children’s Emergency Fund (UNICEF) initiatives of Child-Friendly Cities have operated in more than 3000 cities around the world. However, the concept of making the city child-friendly has not been extensively recognized in China. This article aims to report a regeneration project in a migrant workers’ community, the Mingdong, and is used to illustrate the reactions of residents to youth participation (youth civic engagement) and the project through an international workshop. The article concludes with a discussion of how social workers help in the social development of migrant communities by promoting youth participation, community social awareness and social cohesiveness.

QuCTS—Single-Flux Quantum Clock Tree Synthesis

Superconductive rapid single-flux quantum (RSFQ) is an emerging cryogenic technology, promising a significant boost in performance and ultralow power consumption. The operating frequency achieved by RSFQ digital integrated circuits is several orders of magnitude greater than traditional CMOS circuits. The fundamental difference of RSFQ circuits, however, renders traditional clocking techniques appropriate for CMOS unsuitable for RSFQ technology. Most RSFQ logic gates, such as AND and OR, are sequential in nature. The number of pipeline stages is therefore significantly greater in RSFQ as compared to CMOS, complicating the clock distribution network design process. This issue is further exacerbated with the need for splitters to achieve a fanout greater than one and the need for transmission lines rather than ordinary metallic wires as in CMOS. In this work, QuCTS—single-flux Quantum (SFQ) Clock Tree Synthesis—is presented. QuCTS utilizes a two-stage framework for synthesizing clock networks. In the clock skew scheduling stage, the clock signal arrival time of each gate is chosen to maximize the robustness of the circuit to timing variations. In the clock tree synthesis stage, the layout of the clock distribution network is generated based on a novel delay equilibration technique. QuCTS is the first clock tree synthesis tool for RSFQ circuits utilizing useful clock skew. The synthesized network satisfies the clock arrival time requirements while minimizing the associated overhead, such as the interconnect length and number of delay elements. The tool is validated on a set of benchmark circuits. In a prototypical case study, a clock tree is generated for the AMD2901 with 1049 clock sinks in 53 min while satisfying the clock arrival time.

Vertically layered flow structure at confluence of a reservoir and tributary carrying high sediment loads

Enhanced understanding of flow structure at a river confluence is essential for predictions of sediment transport and morphological evolution. To date, however, the confluent flow structure of a reservoir and tributary carrying high sediment loads has remained poorly understood, and may be vertically layered sharply, featuring subaqueous sediment-laden flow, i.e., turbidity currents underneath subaerial clear water. Here a recently established 2D double layer-averaged model, able to resolve the formation, propagation, and recession of turbidity currents, is used to investigate a series of idealized laboratory-scale cases and a prototype case study of the Guxian Reservoir on the Yellow River, China. Four primary patterns of the stable, vertically layered flow structure at a reservoir-tributary confluence are identified: 1) single layers of sediment-laden inflow in both the main channel and tributary, sustained by sufficient vertical mixing; 2) a double layer in the main channel and a single layer of sediment-laden inflow in the tributary, when the sediment-laden flow in the tributary suffices to block intrusion of flow in the main channel; 3) a single layer of sediment-laden inflow in the main channel and a double layer in the tributary, induced by the intrusion of sediment-laden flow from the main channel into clear-water flow with small discharge in the tributary; and 4) double layers in both the main channel and tributary, which may be further divided into three subpatterns, as turbidity current exists in both the main channel and tributary, or in the main channel (tributary) intruding into the tributary (main channel). In response to unsteady discharge and sediment inputs from upstream, the vertically layered flow structure evolves in time, and may fall into one of the patterns identified above. Although bed deformation in the long term may modify the confluent flow, the vertically layered flow pattern remains so far as the present cases are concerned. The findings have implications for sediment transport and morphological evolution at a reservoir–tributary confluence, for which further studies are suggested to inform the optimization of reservoir operation schemes to mitigate capacity loss caused by sedimentation.

Crystal Engineering of Pharmaceutical Cocrystals in the Discovery and Development of Improved Drugs.

The subject of crystal engineering started in the 1970s with the study of topochemical reactions in the solid state. A broad chemical definition of crystal engineering was published in 1989, and the supramolecular synthon concept was proposed in 1995 followed by heterosynthons and their potential applications for the design of pharmaceutical cocrystals in 2004. This review traces the development of supramolecular synthons as robust and recurring hydrogen bond patterns for the design and construction of supramolecular architectures, notably, pharmaceutical cocrystals beginning in the early 2000s to the present time. The ability of a cocrystal between an active pharmaceutical ingredient (API) and a pharmaceutically acceptable coformer to systematically tune the physicochemical properties of a drug (i.e., solubility, permeability, hydration, color, compaction, tableting, bioavailability) without changing its molecular structure is the hallmark of the pharmaceutical cocrystals platform, as a bridge between drug discovery and pharmaceutical development. With the design of cocrystals via heterosynthons and prototype case studies to improve drug solubility in place (2000-2015), the period between 2015 to the present time has witnessed the launch of several salt-cocrystal drugs with improved efficacy and high bioavailability. This review on the design, synthesis, and applications of pharmaceutical cocrystals to afford improved drug products and drug substances will interest researchers in crystal engineering, supramolecular chemistry, medicinal chemistry, process development, and pharmaceutical and materials sciences. The scale-up of drug cocrystals and salts using continuous manufacturing technologies provides high-value pharmaceuticals with economic and environmental benefits.

SpillwayPro: Integrated Water Surface Profile, Cavitation, and Aerated Flow Analysis for Smooth and Stepped Chutes

Spillways of large and small dams experience flow conditions that exceed the capabilities of common water surface profile computer programs. Unique channel geometries, steep slopes, and streamline curvature demand rigorous mathematics, while phenomena associated with aerated flow and potential cavitation require special analyses, especially for stepped chutes. SpillwayPro is a generalized, energy-based, water surface profile calculation tool for both smooth and stepped chutes that integrates cavitation analysis and aerated flow effects, with separate modules supporting aerator design and the development of optimized structure profiles that meet cavitation parameter objectives. Simultaneous calculation of smooth and stepped-chute flow profiles enables the rapid assessment of the energy dissipation benefits of steps, as well as their unique aerated flow and cavitation issues. SpillwayPro’s technical basis is presented, and its results are compared to the data from prototype case studies and empirical methods developed from large-scale laboratory studies. SpillwayPro’s fundamental energy-based methods are useful for cases that differ from idealized empirical approaches and would benefit from a more rapid analysis than can be accomplished with physical or computational fluid dynamics (CFD) modeling.

Game.UP: Gamified Urban Planning Participation Enhancing Exploration, Motivation, and Interactions

ABSTRACT We present our interdisciplinary research on gamification as a communication tool in urban planning. Approaching the topic from a user-centered perspective, we combine knowledge from the domains of architecture and human-computer interaction and define domain-specific requirements and areas of interest. To substantiate our findings, we present a prototypical case study of a public participation application, developed in consultation with the urban planning project driver and evaluated on-location with members of the public. Our results highlight the opportunities in pursuing a new sub-field of gamification within urban planning and include concepts of game interfaces and spaces in this research. In our case study, we present a methodological approach to assessing gamification in urban planning participation and provide findings on how gamification changes the users’ relationship to participation as well as evidence that the “avatar” game element can affect the motivational affordances meaning of task, perceived competence, and social relatedness.

Filter Efficiency with Red Rock as Post-Treatment of Stabilization Pond Effluent, Prototype Case Study San Marcos, Carazo, from April to September 2019

Filter Efficiency with Red Rock as Post-Treatment of Stabilization Pond Effluent, Prototype Case Study San Marcos, Carazo, from April to September 2019

Bioclimatic and Regenerative Design Guidelines for a Circular University Campus in India

To transform the negative impacts of buildings on the environment into a positive footprint, a radical shift from the current, linear ‘make-use-dispose’ practice to a closed-loop ‘make-use-return’ system, associated with a circular economy, is necessary. This research aims to demonstrate the possible shift to a circular construction industry by developing the first practical framework with tangible benchmarks for a ‘Circular University Campus’ based on an exemplary case study project, which is a real project development in India. As a first step, a thorough literature review was undertaken to demonstrate the social, environmental and economic benefits of a circular construction industry. As next step, the guideline for a ‘Circular University Campus’ was developed, and its applicability tested on the case study. As final step, the evolved principles were used to establish ‘Project Specific Circular Building Indicators’ for a student residential block and enhance the proposed design through bioclimatic and regenerative design strategies. The building’s performance was evaluated through computational simulations, whole-life carbon analysis and a circular building assessment tool. The results demonstrated the benefits and feasibility of bioclimatic, regenerative building and neighbourhood design and provided practical prototypical case study and guidelines which can be adapted by architects, planners and governmental institutions to other projects, thereby enabling the shift to a restorative, circular construction industry.

Sleep Detection for Younger Adults, Healthy Older Adults, and Older Adults Living With Dementia Using Wrist Temperature and Actigraphy: Prototype Testing and Case Study Analysis.

BackgroundSleep is essential for one’s health and quality of life. Wearable technologies that use motion and temperature sensors have made it possible to self-monitor sleep. Although there is a growing body of research on sleep monitoring using wearable devices for healthy young-to-middle-aged adults, few studies have focused on older adults, including those living with dementia.ObjectiveThis study aims to investigate the impact of age and dementia on sleep detection through movement and wrist temperature.MethodsA total of 10 younger adults, 10 healthy older adults, and 8 older adults living with dementia (OAWD) were recruited. Each participant wore a Mi Band 2 (accemetry-based sleep detection) and our custom-built wristband (actigraphy and wrist temperature) 24 hours a day for 2 weeks and was asked to keep a daily sleep journal. Sleep parameters detected by the Mi Band 2 were compared with sleep journals, and visual analysis of actigraphy and temperature data was performed.ResultsThe absolute differences in sleep onset and offset between the sleep journals and Mi Band 2 were 39 (SD 51) minutes and 31 (SD 52) minutes for younger adults, 49 (SD 58) minutes and 33 (SD 58) minutes for older adults, and 253 (SD 104) minutes and 161 (SD 94) minutes for OAWD. The Mi Band 2 was unable to accurately detect sleep in 3 healthy older adults and all OAWDs. The average sleep and wake temperature difference of OAWD (1.26 °C, SD 0.82 °C) was significantly lower than that of healthy older adults (2.04 °C, SD 0.70 °C) and healthy younger adults (2.48 °C, SD 0.88 °C). Actigraphy data showed that older adults had more movement during sleep compared with younger adults and that this trend appears to increase for those with dementia.ConclusionsThe Mi Band 2 did not accurately detect sleep in older adults who had greater levels of nighttime movement. As more nighttime movement appears to be a phenomenon that increases in prevalence with age and even more so with dementia, further research needs to be conducted with a larger sample size and greater diversity of commercially available wearable devices to explore these trends more conclusively. All participants, including older adults and OAWD, had a distinct sleep and wake wrist temperature contrast, which suggests that wrist temperature could be leveraged to create more robust and broadly applicable sleep detection algorithms.

Development of Lateral Capacity-Based Envelopes of Piled Raft Foundation under CombinedV-M-HLoading

Combined piled raft foundation (CPRF) is an effective solution for supporting high-rise constructions. The traditional design philosophy of CPRF does not consider the capacity of foundation based on combined vertical (V), horizontal (H), and moment (M) load. However, V-M-H load acts on the CPRF system concurrently due to the action of wind, wave, earth pressure, and earthquake loading in addition to gravity loading. In this context, the present investigation deals with the effect of combined V-M-H load interaction on the behavior of CPRF in sand and clay, respectively. The response of CPRF under combined loading is obtained using a 3D finite element analysis. First, validation of the numerical model is performed considering the model test results conducted in Agartala sand and another based on the available field test result of Messeturm Tower in Frankfurt clay obtained from the literature. Further, failure and design envelopes for the validated CPRF system are developed considering V-M-H load in both sand and clay deposits attributing a variety of influential system parameters. Simplified expressions for design lateral and moment capacities are proposed based on multiple linear regression analysis. Finally, the implication in design based on the findings of the present study is illustrated using a prototype case study structure.

A reactive decentralized coordination algorithm for event-driven production planning and control: A cyber-physical production system prototype case study

Concepts and expectations of cyber-physical production systems (CPPSs) underscore their advantages over the present bespoke manufacturing systems. However, transitioning from traditional manufacturing to its CPPS counterpart remains a challenge. Although a CPPS can be realized, a gap between theory and practice exists considering the response of a CPPS to system disturbances, such as machine breakdowns and change in customer orders. Furthermore, the CPPS environment is stochastic; therefore, planning and control algorithms to reduce the discrepancy between the planned and actual system levels in real time is essential. In this paper, we present a CPPS prototype and a reactive production planning algorithm. Consistent with the literature on CPPS, we developed a moderately automated CPPS and a decentralized coordination mechanism that harnesses the inherent decentralized structure of the CPPS. Moreover, we have described the design and technologies used in building this CPPS. Through a production planning mathematical model, we proposed a control mechanism and measured the efficiency of the proposed algorithm under dynamic events based on work-in-progress inventories, throughput, and delayed demand. The decentralized algorithm determines a plan prior to start of production, which is re-optimized in case any dynamic events occur subsequent to the course of the production horizon. The results of the numerical investigation indicated that the proposed decentralized coordination algorithm outperforms other centralized planning algorithms on more than 50 % of the performance measures considered.

Bridging Police Work with the Public Health Domain: An Occupational Safety and Health Perspective

For good reasons, public health and public policing constitute two separate constellations of public affairs governance. They widely differ with regard to their objectives, legal basis, workforce, expertise, traditions, occupational culture and many more. In conjunction to both strands of governance Occupational Safety and Health (OSH) – being both a definition and umbrella term - encompasses any kind of activity related to foster the safety and wellbeing of workers. In that regard OSH is marked by being a highly interdisciplinary, hands-on and heuristic undertaken, in particular widely acknowledged of being ‘public health-close’ and at the same time ‘security risk management-near’. That way OSH is clearly identifiable as a highly promising interface bridging police work with public health, in particular by applying mutual theory and language. This conceptual paper proposes a new perspective and view on organisational OSH, functioning well as a legitimate medium for both frontline workers but also managerial functionaries. Vice-versa organisational OSH has been identified as a suitable trigger for transferring academic stances into the rather praxis- and realpolitik-driven domain of policing. Alongside the prototypical case study of Frontex operational OSH, OSH has been proven as legitimate driver for utilising the current pandemic COVID-19 outbreak as suitable tool for breaking down existing barriers and silos between the both mentioned strands of governance. That way as additional craft and capacity OSH might enfold truly operational strength and added value.