Scaling Issues Research Articles

Recent and Future Advances in Water Electrolysis for Green Hydrogen Generation: Critical Analysis and Perspectives

This paper delves into the pivotal role of water electrolysis (WE) in green hydrogen production, a process utilizing renewable energy sources through electrolysis. The term “green hydrogen” signifies its distinction from conventional “grey” or “brown” hydrogen produced from fossil fuels, emphasizing the importance of decarbonization in the hydrogen value chain. WE becomes a linchpin, balancing surplus green energy, stabilizing the grid, and addressing challenges in hard-to-abate sectors like long-haul transport and heavy industries. This paper navigates through electrolysis variants, technological challenges, and the crucial association between electrolytic hydrogen production and renewable energy sources (RESs). Energy consumption aspects are scrutinized, highlighting the need for optimization strategies to enhance efficiency. This paper systematically addresses electrolysis fundamentals, technologies, scaling issues, and the nexus with energy sources. It emphasizes the transformative potential of electrolytic hydrogen in the broader energy landscape, underscoring its role in shaping a sustainable future. Through a systematic analysis, this study bridges the gap between detailed technological insights and the larger energy system context, offering a holistic perspective. This paper concludes by summarizing key findings, showcasing the prospects, challenges, and opportunities associated with hydrogen production via water electrolysis for the energy transition.

THE CURRENT STATE AND PROSPECTS FOR THE DEVELOPMENT OF TOURISM ENTITIES IN UKRAINE

The article analyzes the current state of tourism entities in Ukraine under martial law and identifies the prospects for development in the post-war period. It is substantiated that the dynamism, scale and diversification of tourism as a type of economic activity in the face of unfavorable conditions in the tourism market contribute to the preservation and restoration of positive development trends. It is substantiated that the economic contribution of the national tourism sector faces great opportunities, on the one hand, and threats, on the other, due to the pressure of competition and complacency about the constant attractiveness of Europe.
 The unprecedented conditions in which Ukraine has found itself as a result of Russia's full-scale military invasion have provoked dramatic changes in all spheres of public life. 
 Contrary to various forecasts, Ukraine has not only survived, but, despite very painful losses, is heroically fighting for its freedom and independence. 
 In the wartime and postwar periods in Ukraine, the progress of most domestic tourism actors will be difficult, as tourism competence is distributed at the local, national, and European levels.
 To succeed, tourism must become a top-level priority for Ukraine. The interests of the tourism business and relevant ministries and policy makers need to be aligned so that the potential of tourism can be realized. This will lead to the effective development and coordination of strategic policies that include appropriate consultations with tourism stakeholders.
 Promising for further research are the issues of scaling up the national tourism product by domestic tourism entities through integration with tourist routes of European countries, which will contribute to the development of technology management processes.

Diameter, height and species of 42 million trees in three European landscapes generated from field data and airborne laser scanning data.

Ecology and forestry sciences are using an increasing amount of data to address a wide variety of technical and research questions at the local, continental and global scales. However, one type of data remains rare: fine-grain descriptions of large landscapes. Yet, this type of data could help address the scaling issues in ecology and could prove useful for testing forest management strategies and accurately predicting the dynamics of ecosystem services. Here we present three datasets describing three large European landscapes in France, Poland and Slovenia down to the tree level. Tree diameter, height and species data were generated combining field data, vegetation maps and airborne laser scanning (ALS) data following an area-based approach. Together, these landscapes cover more than 100 000 ha and consist of more than 42 million trees of 51 different species. Alongside the data, we provide here a simple method to produce high-resolution descriptions of large landscapes using increasingly available data: inventory and ALS data. We carried out an in-depth evaluation of our workflow including, among other analyses, a leave-one-out cross validation. Overall, the landscapes we generated are in good agreement with the landscapes they aim to reproduce. In the most favourable conditions, the root mean square error (RMSE) of stand basal area (BA) and mean quadratic diameter (Dg) predictions were respectively 5.4 m 2.ha -1 and 3.9 cm, and the generated main species corresponded to the observed main species in 76.2% of cases.

Optical switching will innovate intra data center networks [Invited Tutorial

Reflecting the recent slow-down in Moore’s law and the proliferation of artificial intelligence/machine learning workloads, the performance and energy consumption of networks are becoming barriers in high-performance computing (HPC) and data centers. Optical switches are expected to break these barriers, and indeed their introduction has recently commenced in data centers. This paper discusses how optical switching technologies can innovate future intra data center networks. Hyperscale data centers are much bigger in scale, and network requirements are slightly different from those of HPC. This paper focus on data center networks, since the impact of optical technologies will be more significant in data centers than in HPC. In addition to the scale issue, important metrics to be considered for network design are traffic characteristics and latency, both of which are highlighted in this paper. For hybrid (electrical packet and optical circuit) switching networks, the target latency for the optical circuit switch network (connection setup/teardown time) is shown to be around 10 µs, and the needed technologies are clarified and verified by experiments. The optical switch can simplify the present multi-tier switching network above tier-1 switches into a single tier configuration, which is possible with the development of efficient large port count optical switches. Among the different switching architectures, combining the different dimensions of space and wavelength is shown to be one of the best solutions. Fast switching needs fast device response time. Si photonics devices using Mach–Zehnder interferometers or ring-resonator-based switches and tunable filters are the most promising candidates; they offer cost-effective mass-production and fast operation and so are excellent candidates for the optical switches envisaged. Another critical technology to maximize the benefits of optical switches is a simple and low-latency control mechanism. Different approaches have been suggested as summarized in this work. Among them, harnessing optical switch parallelism is a unique technique that matches recent advances in electrical switch chips. A fast control network is realized by using a fully decentralized and asynchronous control mechanism. A hyperscale data center offers a wide variety of services, and no one system fits all needs. Optimization of parameters is an important task for maximizing the impact of optical switching in different kinds of data centers.

Mapping the knowledge of ecosystem service-based ecological risk assessment: scientometric analysis in CiteSpace, VOSviewer, and SciMAT

The ecosystem services approach offers a more ecologically relevant method to establish environmental conservation goals and implement ecological risk assessment (ERA). The emergence of bibliometrics has facilitated the development of new systematic review techniques. In this study, we utilised CiteSpace, VOSviewer, and SciMAT software, based on the Web of Science database, to qualitatively and quantitatively analyse the ecosystem service-based ecological risk assessment (ESRA) literature knowledge map spanning from 1994 to 2023. This article explored the field’s evolution from macro to micro perspectives, incorporating background information, current trends, and knowledge structure. The findings demonstrate that ESRA has progressed from an initial stage to a phase of global cooperation and policy applications. This transition between stages has been characterised by a shift from focusing on natural processes to understanding human impacts on ecosystems. Key themes identified include ecosystem services, landscape ERA, aquatic ERA and ecosystem health. The overall development of ERA can be observed as a progression through different periods, namely, the traditional era, regional era, and landscape era. Currently, landscape ERA methods based on changes in land use and land cover are widely employed. The study also revealed various challenges in the ESRA field, such as data availability, scale issues, and uncertainty. Future ESRA studies should consider holistic ecosystem services, interdisciplinary approaches, ecological models, and advanced technologies to address complexity. Using big data and informatisation for research offers new opportunities but requires integration and innovation. It is anticipated that ESRA holds promise for ecological sustainability and human wellbeing.

Mini-Batch Alignment: A Deep-Learning Model for Domain Factor-Independent Feature Extraction for Wi-Fi–CSI Data

Unobtrusive sensing (device-free sensing) aims to embed sensing into our daily lives. This is achievable by re-purposing communication technologies already used in our environments. Wireless Fidelity (Wi-Fi) sensing, using Channel State Information (CSI) measurement data, seems to be a perfect fit for this purpose since Wi-Fi networks are already omnipresent. However, a big challenge in this regard is CSI data being sensitive to ‘domain factors’ such as the position and orientation of a subject performing an activity or gesture. Due to these factors, CSI signal disturbances vary, causing domain shifts. Shifts lead to the lack of inference generalization, i.e., the model does not always perform well on unseen data during testing. We present a domain factor-independent feature-extraction pipeline called ‘mini-batch alignment’. Mini-batch alignment steers a feature-extraction model’s training process such that it is unable to separate intermediate feature-probability density functions of input data batches seen previously from the current input data batch. By means of this steering technique, we hypothesize that mini-batch alignment (i) absolves the need for providing a domain label, (ii) reduces pipeline re-building and re-training likelihood when encountering latent domain factors, and (iii) absolves the need for extra model storage and training time. We test this hypothesis via a vast number of performance-evaluation experiments. The experiments involve both one- and two-domain-factor leave-out cross-validation, two open-source gesture-recognition datasets called SignFi and Widar3, two pre-processed input types called Doppler Frequency Spectrum (DFS) and Gramian Angular Difference Field (GADF), and several existing domain-shift mitigation techniques. We show that mini-batch alignment performs on a par with other domain-shift mitigation techniques in both position and orientation one-domain leave-out cross-validation using the Widar3 dataset and DFS as input type. When considering a memory-complexity-reduced version of the GADF as input type, mini-batch alignment shows hints of recuperating performance regarding a standard baseline model to the extent that no additional performance due to weight steering is lost in both one-domain-factor leave-out and two-orientation-domain-factor leave-out cross-validation scenarios. However, this is not enough evidence that the mini-batch alignment hypothesis is valid. We identified pitfalls leading up to the hypothesis invalidation: (i) lack of good-quality benchmark datasets, (ii) invalid probability distribution assumptions, and (iii) non-linear distribution scaling issues.

Modeling Exposure to Mobility-Related Pollution: Review and Key Challenges

The health and equity issues related to urban pollution, coupled with the need to assess the consequences of implementing transport policies or new mobility solutions, make it imperative to provide decision-makers with a relevant modeling framework. The coupling of activity-based travel models with environmental models presents a promising approach to thoroughly investigate the interconnected issues related to the impact of road traffic on individual exposure to noise and air pollution. Integrated modeling chains provide a robust framework to comprehensively assess traffic scenarios on a city scale and address issues related to environmental inequalities. This article reviews the recent work in this field to identify both the main contributions and key challenges still to be addressed. It illustrates how the use of a dynamic approach, which considers the intraday dynamics of individual mobilities and pollution concentrations, can contribute to more accurate estimates of the exposure of individuals to pollutants. This modeling approach also has the potential to investigate how the level of exposure depends on the characteristics of the individuals (socioeconomic factors, commuting patterns, location of residence, and main activities). Nonetheless, there are still important challenges to overcome to further enhance the accuracy and applicability of these models: taking into account all types of traffic and transport modes; refining the modeling of exposure to pollution during travel; taking better consideration of microenvironments; and so forth.

Regional-scale cultural conservation planning and policy in the United States: an appeal for improvement

Pennsylvania’s (PA) processes and policies for landscape-scale cultural and visual resource conservation are lacking. In PA, like much of the United States (US), landscape change policies are prescriptive and concerned mainly with ecology, health, safety, and welfare issues. These factors combined relegate cultural and scenic aspects to ancillary matters, often leading to their degradation. Culturally focused fields, such as landscape architecture, archaeology, and planning call for rescaling cultural conservation planning to regional scale. Rescaling would treat cultural resources like other environmental and ecological resources, giving cultural resources equal weight in conservation evaluations. The United Kingdom (UK) has policies specifically for visual impact assessment required for development projects. This paper discusses scale issues and political processes within regional visual and cultural resource conservation in PA, US, compares nascent regional-scale planning efforts in PA and the UK, and proposes improvements to PA and, by extension, US cultural landscape conservation policy implementation.

OECD/NEA/CSNI state-of-the-art report on scaling in system thermal-hydraulics applications to nuclear reactor safety and design (The S-SOAR)

The present paper deals with scaling in nuclear-system thermal-hydraulics (SYS TH), including the connection with Nuclear Reactor Safety Technology (NST). The paper is entirely derived from the S-SOAR document issued by CSNI of NEA, NEA/CSNI/R(2016)14, 2016 (Bestion et al., 2016). Scaling has constituted ‘an issue’ since the beginning of the exploitation of nuclear energy for civil purposes, with main reference to the generation of electricity. A Nuclear Power Plant (NPP) constitutes a technologically complex industrial system and it is characterized by the impossibility of, or the large difficulty in, characterizing the system’s performance under the conditions of the design. So, models were designed, constructed, and operated under downscaled ranges of values for one or more of selected parameters (e.g. power, volume, height, pressure, etc). These features lay at the origin of the scaling issue, i.e. the difficulty in demonstrating that a model behaves like the prototype. Integrated definitions of the widely adopted terms, ‘scaling’, ‘scaling issue’, and ‘addressing the scaling issue’ are part of the present document. The related application domain includes the NST, and the licensing for water-cooled nuclear reactors under operation, under construction, or under an advanced design stage at the time of publication of the (Bestion et al., 2016). Scaling-related analyses are done in different areas of SYS TH and NST. These include the design of test facilities (both integral and separate-effect test facilities, IETF and SETF), the design of experiments (including Counterpart Test, CT), the demonstration of the capability of any computational tool, and the evaluation of uncertainty affecting the prediction of the same computational tools. A variety of approaches have been used to address the scaling issue, including non-dimensional analysis of mass, energy- and momentum-balance equations, derivation and application of scaling factors, including the hierarchy of relative importance, performing experiments at different scales, and running the SYS TH computer codes. This paper discusses the key areas and the key approach for scaling. It was found that the SYS TH computer codes, following their application to differently scaled experiments, demonstrate that the accuracy of their predictions may not depend upon the scale of the considered experiments. The TH codes also may constitute an additional valuable tool for addressing the issue of scaling.

Scale transformation of place brands: a visual study of Xinhepu, Guangzhou

Place branding has become a popular strategy to promote place visibility and place image communication and gains attention in the field of city planning, geography, and place management. The contribution of geographical knowledge has been emphasized and discussed in recent place branding studies, including the role of geographical scale in branding strategies and management. However, current research mainly places the issues of scale from the top-down perspective of branding governance, and the bottom-up processes based on local culture and embodied experiences are overlooked. Drawing on participatory-based visual methodology, in the article, a combination of self‐directed photography, photo evaluation, eye-tracking experiment, and interview are employed to the scale transformation performance of place brands from the perspective of embodied experience. Taking Xinhepu in Guangzhou, China as a case study, the results show that both residents and nonresidents do not limit their perception of the Xinhepu brand to a microscale space but point to the city of Guangzhou. Specifically, the process of scale transformation of place brands is characterized by the interaction of temporal and spatial factors that provide possibilities for scale transformation. The physical landscape in space plays a role in place branding, while in time scale, Xinhepu connects Guangzhou’s past and present and is a microcosm of the city’s image and historical development; thus, in people’s embodied perception, Xinhepu can be transformed into a city brand through place culture creation. The article can provide a new explanation for the construction of place branding at multiple geographical scales and explore how the body’s unconscious, nonrepresentational rhythms play a role in place brand scale transformation.

Machinic Landscapes: Aesthetics of the Nonhuman

If the Anthropocene marks a changed way of being in the world resulting from the integration of human activity and geological processes, a main question is how this situation may be aesthetically represented and with what implications. This article sketches an aesthetics of the Anthropocene by analyzing artistic engagements with advanced tools of visual calculation used in the context of the environment; it explores what I term machinic landscapes. Machinic landscapes are landscape works made by contemporary artists using forms of machine vision and involve a confrontation between two mediums, that of the logic and the representational mechanisms of its technology and that of the forms and processes found in nature and the environment and the multiple layers of representation that emerge and further generate through this. The bringing together of the terms machinic and landscape is meant to counter binaries between nature and technology and to foreground ways in which forms found in the natural environment are integrated into the functioning of a technical, aesthetic gesture by machine. Here I focus on the work of contemporary artists Mishka Henner, Daniel Lefcourt, and Davide Quayola, each of whom works with the digital visualizing programs often utilized in fields of landscape architecture and the geosciences including heightmapping, LiDAR, 3D scanning, and satellite imaging systems to produce artistic forms of landscape. Through their artworks, these artists experiment with parameters of technologies outside the contexts of scientific implementation, revealing multiplicities of machinic aesthetic output and their limitations. I argue that these machinic landscapes provide speculative outcomes and alternative imaginaries that directly address some of the aesthetic challenges of the Anthropocene—specifically, issues of latency, entanglement, and scale. Drawing on landscape theory, philosophy of technology, and concepts within geography to frame this study, this article revisits the art historical genre of landscape as a vehicle to explore contemporary aesthetic and political dimensions of our relationship to and in the environment.

Best practices for portfolio optimization by quantum computing, experimented on real quantum devices

In finance, portfolio optimization aims at finding optimal investments maximizing a trade-off between return and risks, given some constraints. Classical formulations of this quadratic optimization problem have exact or heuristic solutions, but the complexity scales up as the market dimension increases. Recently, researchers are evaluating the possibility of facing the complexity scaling issue by employing quantum computing. In this paper, the problem is solved using the Variational Quantum Eigensolver (VQE), which in principle is very efficient. The main outcome of this work consists of the definition of the best hyperparameters to set, in order to perform Portfolio Optimization by VQE on real quantum computers. In particular, a quite general formulation of the constrained quadratic problem is considered, which is translated into Quadratic Unconstrained Binary Optimization by the binary encoding of variables and by including constraints in the objective function. This is converted into a set of quantum operators (Ising Hamiltonian), whose minimum eigenvalue is found by VQE and corresponds to the optimal solution. In this work, different hyperparameters of the procedure are analyzed, including different ansatzes and optimization methods by means of experiments on both simulators and real quantum computers. Experiments show that there is a strong dependence of solutions quality on the sufficiently sized quantum computer and correct hyperparameters, and with the best choices, the quantum algorithm run on real quantum devices reaches solutions very close to the exact one, with a strong convergence rate towards the classical solution, even without error-mitigation techniques. Moreover, results obtained on different real quantum devices, for a small-sized example, show the relation between the quality of the solution and the dimension of the quantum processor. Evidences allow concluding which are the best ways to solve real Portfolio Optimization problems by VQE on quantum devices, and confirm the possibility to solve them with higher efficiency, with respect to existing methods, as soon as the size of quantum hardware will be sufficiently high.

Architecture is Interface: Latent Virtuality from Antiquity to Zoom

AbstractArchitectural educators Joshua Bard and Francesca Torello embrace numerous digital technologies to subvert issues of scale, framing, materiality, inside and outside to provoke viewers into contemplating the current nature of architecture and its discourse. Their inspiration is Sir John Soane's Museum in London, which is a multilayered conversation between objects and their interrelationships with themselves and their settings.

Spiking neural networks fine-tuning for brain image segmentation.

The field of machine learning has undergone a significant transformation with the progress of deep artificial neural networks (ANNs) and the growing accessibility of annotated data. ANNs usually require substantial power and memory usage to achieve optimal performance. Spiking neural networks (SNNs) have recently emerged as a low-power alternative to ANNs due to their sparsity nature. Despite their energy efficiency, SNNs are generally more difficult to be trained than ANNs. In this study, we propose a novel three-stage SNN training scheme designed specifically for segmenting human hippocampi from magnetic resonance images. Our training pipeline starts with optimizing an ANN to its maximum capacity, then employs a quick ANN-SNN conversion to initialize the corresponding spiking network. This is followed by spike-based backpropagation to fine-tune the converted SNN. In order to understand the reason behind performance decline in the converted SNNs, we conduct a set of experiments to investigate the output scaling issue. Furthermore, we explore the impact of binary and ternary representations in SNN networks and conduct an empirical evaluation of their performance through image classification and segmentation tasks. By employing our hybrid training scheme, we observe significant advantages over both ANN-SNN conversion and direct SNN training solutions in terms of segmentation accuracy and training efficiency. Experimental results demonstrate the effectiveness of our model in achieving our design goals.

Gaussian Adapted Markov Model with Overhauled Fluctuation Analysis-Based Big Data Streaming Model in Cloud.

An accurate resource usage prediction in the big data streaming applications still remains as one of the complex processes. In the existing works, various resource scaling techniques are developed for forecasting the resource usage in the big data streaming systems. However, the baseline streaming mechanisms limit with the issues of inefficient resource scaling, inaccurate forecasting, high latency, and running time. Therefore, the proposed work motivates to develop a new framework, named as Gaussian adapted Markov model (GAMM)-overhauled fluctuation analysis (OFA), for an efficient big data streaming in the cloud systems. The purpose of this work is to efficiently manage the time-bounded big data streaming applications with reduced error rate. In this study, the gating strategy is also used to extract the set of features for obtaining nonlinear distribution of data and fat convergence solution, used to perform the fluctuation analysis. Moreover, the layered architecture is developed for simplifying the process of resource forecasting in the streaming applications. During experimentation, the results of the proposed stream model GAMM-OFA are validated and compared by using different measures.

A review of advances and applications of geothermal energy extraction using a gravity-assisted heat pipe

Enhanced geothermal system (EGS) provides a feasible way for extracting geothermal energy from hot dry rock (HDR) reservoirs, whereas seismic risk it might induce and enormous cost impair its prospects. For sustainability, circulating fluid in a closed loop to extract thermal energy from a deep geothermal reservoir seems more reasonable and receives increasing attention. With this aim, using a super long gravity heat pipe (SLGHP) to extract geothermal energy from HDR reservoirs was tried by several researchers due to its extremely extraordinary heat transport ability. Jiang et al. designed a SLGHP, by which a thermal power as high as 200 kW was achieved from a 3000 m depth HDR reservoir successfully. The pioneering work proves the feasibility of a safer way for harvesting geothermal energy other than EGS, more importantly, it does not consume power and avoids the toughest issue of pipe scaling in a conventional geothermal production system. The article reviews the latest advances in SLGHPs for geothermal energy extraction. Laboratory experiments and representative in-situ tests are thoroughly analyzed with respect to technologies employing natural circulation inclusive of gravity-assisted heat pipe (GHP) to harvest geothermal energy. Theoretical and numerical work is also reviewed as significant supplements to experimental investigation. Key factors involved are discussed to clarify the challenges of GHPs in engineering for geothermal energy extraction. Low thermal extraction power and economic benefits as well as lack of highly efficient technology to convert heat to electricity are the main barriers of the application of SLGHPs in geothermal energy extraction and utilization. Nonetheless, previous investigation on SLGHPs offers new insights into geothermal energy exploitation from HDR reservoirs.

Nanocomposite Hydrogel Engineered Janus Membrane for Membrane Distillation with Robust Fouling, Wetting, and Scaling Resistance.

Membrane distillation (MD) is considered to be rather promising for high-salinity wastewater reclamation. However, its practical viability is seriously challenged by membrane wetting, fouling, and scaling issues arising from the complex components of hypersaline wastewater. It remains extremely difficult to overcome all three challenges at the same time. Herein, a nanocomposite hydrogel engineered Janus membrane has been facilely constructed for desired wetting/fouling/scaling-free properties, where a cellulose nanocrystal (CNC) composite hydrogel layer is formed in situ atop a microporous hydrophobic polytetrafluoroethylene (PTFE) substrate intermediated by an adhesive layer. By the synergies of the elevated membrane liquid entry pressure, inhibited surfactant diffusion, and highly hydratable surface imparted by the hydrogel/CNC (HC) layer, the resultant HC-PTFE membrane exhibits robust resistance to surfactant-induced wetting and oil fouling during 120 h of MD operation. Meanwhile, owing to the dense and hydroxyl-abundant surface, it is capable of mitigating gypsum scaling and scaling-induced wetting, resulting in a high normalized flux and low distillate conductivity at a concentration factor of 5.2. Importantly, the HC-PTFE membrane enables direct desalination of real hypersaline wastewater containing broad-spectrum foulants with stable vapor flux and robust salt rejection (99.90%) during long-term operation, demonstrating its great potential for wastewater management in industrial scenarios.

A power/energy-efficient, process-variation-resilient multiplier using graphene nanoribbon technology and ternary logic

In consideration of the difficulties related to interconnectivity and energy usage in binary logic, designers have felt compelled to explore ternary logic as a viable alternative. The most effective approach for implementing circuits based on ternary logic involves the use of the multi-threshold voltage (Vth) technique. In the case of field-effect transistors (FETs) that utilize graphene nanoribbons (GNRs), known as GNRFETs, the value of Vth relies on the GNR’s width, which can be adjusted by altering the dimer lines number. GNRFET shows potential as a replacement for CMOS technology due to the remarkable properties of GNR that help address scaling issues and short-channel effects. However, for real-time applications like portable electronic devices, power and energy efficiency are the primary concerns. Consequently, this paper presents a power and energy-efficient ternary multiplier based on 32-nm GNRFET technology. HSPICE simulation results obtained at 0.9 V demonstrate that the suggested circuit can achieve a minimum of 36.72% improvement in power and 28.17% enhancement in energy efficiency. This is accomplished through the utilization of distinct methods such as specific transistor configurations, unary operators, and dual power sources. Moreover, Monte-Carlo simulation results confirm the variation tolerance of the proposed circuit.

Surface spectral irradiance and irradiance partitioning in a complex mountain environment: understanding location-dependent topographic effects in satellite imagery

Remote sensing of mountain environments is extremely difficult due to atmosphere-topography-landcover couplings that govern the irradiant fluxes. Researchers have not adequately accounted for topographic effects in terms of irradiance components over the landscape. Consequently, we evaluated surface spectral-irradiance components and irradiance partitioning with respect to anisotropic-reflectance correction (ARC). Our work focuses on addressing issues of scale, parameter sensitivity analysis and irradiance partitioning to provide new insights into understanding the complex nature of topographic modulation of the radiation-transfer cascade. We accomplish this by characterizing irradiance components in numerical simulations that account for topographic effects and couplings. Our results reveal atmosphere-topographic couplings associated with irradiance components that are not spatially coincident. Furthermore, we found that commonly utilized adjacent-terrain irradiance parameterization schemes produce different results compared to a more comprehensive parameterization scheme. Parameter sensitivity analysis revealed that the exclusion of topographic effects also produces different results.

‘But, is it supposed to be a straight line?’ Scaffolding students’ experiences with pressure sensors and material resistance in a high school biology classroom

ABSTRACT This case study examines how material resistance (limitations posed by the physical world) and graph interpretation intersected during a high school biology investigation using digital sensors. We use an extended episode from a small group to illustrate how, in an inquiry-based unit, measuring near the resolution limit of a sensor caused scaling issues in graphs. Qualitative videotape analysis focuses on both the students’ attempts to make sense of a perceived lack of variation in the collected data and the teacher's and classroom researchers’ misinterpretation of the students’ difficulties with graph interpretation. We suggest that these educators, though experienced, could have benefited from additional strategies to help them recognise and respond to graph interpretation issues introduced by digital representations of real-world data, and that the students could have benefited from explicit prompts to discuss the limitations of their equipment. We describe several implications for researchers, teachers, and curriculum developers interested in implementing inquiry-based biology investigations using sensor data. We argue that students should be supported to recognise that encountering unexpected results from their investigations and working to understand what these results have to say about the real world is an important and valid part of the practice of real science.