Search Results (30)

Virtual reality (VR) interfaces increasingly rely on interaction within peripersonal space. However, the conditions under which interaction performance in virtual environments can approximate those of comparable real-world tasks remain underexplored, particularly for hierarchical menus requiring precise sequential input. This study investigated how the presence or absence of tactile feedback influences movement time and selection accuracy during hierarchical menu interaction in peripersonal space across different task difficulty levels. Twelve participants performed a three-level hierarchical selection task on a 4 × 3 menu in two controlled experiments with a stereoscopic 3D TV. Two interaction conditions were tested: a surface-based condition, with the menu attached to the physical screen, and a mid-air condition, with the menu positioned 35 cm and 45 cm in front of participants. Selections were confirmed using a handheld remote. Results showed no statistically significant difference in movement time and selection accuracy between the virtual and real environments when screen-surface targets provided tactile feedback, but performance declined for mid-air targets without tactile references, particularly under higher task difficulty levels. These findings suggest that tactile feedback, coordinated visual target placement, and users’ familiarity with touchscreen-like interaction jointly act as key factors for designing effective, immersive, and user-friendly VR menu systems in peripersonal space. Full article

Clouds critically influence Earth’s radiation balance and climate, making accurate cloud detection essential for improving climate models. This study develops the TSAR model to improve the cloud detection accuracy of the FY-4A CLM product by incorporating physical features. The input features include FY-4A brightness temperature (BT) data from channels 8–14, geometric parameters (satellite zenith angle (SAZ), satellite azimuth angle (SAA), solar zenith angle (SOZ), solar azimuth angle (SOA), and latitude), and four ERA5 meteorological factors (2 m air temperature (T2m), skin temperature (SKT), air temperature profiles (ATP), and relative humidity profiles (RH)). Using the CALIPSO cloud detection product as labels, the model outputs cloud/clear-sky classification results. Additionally, four machine learning (ML) algorithms—RF, LightGBM, XGBoost, and MLP—achieved overall accuracies of 91.5%, 92.2%, 92.5%, and 92.8%, respectively, considerably outperforming the FY-4A L2 CLM product (83.1%). The results demonstrate that incorporating physical factors significantly improves cloud detection performance regardless of the algorithm employed. Incorporating meteorological factors notably improved nighttime and water–cloud detection, narrowing day–night accuracy gaps. Shapley additive explanation (SHAP) analysis indicated feature contributions of 15.8%, 50.8%, and 33.3% from geometric, BT, and meteorological variables, respectively, with stronger meteorological effects at mid- to high-latitudes. These findings demonstrate that integrating meteorological factors significantly improves FY-4A cloud detection accuracy and consistency, highlighting the MLP-TSAR model’s effectiveness for reliable all-day operational applications. Full article

Glide-snow avalanches pose a major challenge for operational forecasting and local avalanche authorities. Although their key prerequisite, a moist interface between the snowpack and smooth ground, is well known, predicting the timing of glide-snow avalanches remains difficult. We analyzed five seasons of avalanche monitoring data in the Planneralm area of Styria, Austria. Glide-snow avalanche activity in the study area follows typical temporal patterns, with the highest release probability in the early afternoon and peak activity from mid-March to mid-April. Using meteorological data and avalanche observations as input, we trained machine-learning models to predict hours with glide-snow avalanche release. The most significant predictors were the mean air temperature of the preceding 48h, the day of the winter season, the hour of the day, and the decrease in snow height. The combination of those variables suggests a longer-term predisposition toward glide-snow avalanche release, as well as short-term driving factors. Our decision tree model correctly identified the vast majority of avalanche hours (recall 90%) at the cost of a moderate false alarm rate (15%). Our model could support operational glide-snow avalanche forecasting by identifying hours with elevated glide-snow potential that warrant increased attention and may require warnings or temporary closures by local authorities. Full article

We present FingerType, a one-handed text input method based on thumb-to-finger gestures. FingerType detects tap events from 3D hand data using a Temporal Convolutional Network (TCN) and decodes the tap sequence into words with an n-gram language model. To inform the design, we examined thumb-to-finger interactions and collected comfort ratings of finger regions. We used these results to design an improved T9-style key layout. Our system runs at 72 frames per second and reaches 94.97% accuracy for tap detection. We conducted a six-block user study with 24 participants and compared FingerType with controller input and touch input. Entry speed increased from 5.88 WPM in the first practice block to 10.63 WPM in the final block. FingerType also supported more eyes-free typing: attention on the display panel within ±15° of head-gaze was 84.41%, higher than touch input (69.47%). Finally, we report error patterns and WPM learning curves, and a model-based analysis suggests improving gesture recognition accuracy could further increase speed and narrow the gap to traditional VR input methods. Full article

Urban Heat Island effects and the general rise in outdoor temperatures are increasing the cooling demand in buildings. As a consequence, electrical cooling systems are becoming more common, increasing energy consumption and thus resulting in negative environmental impacts. Optimizing passive solutions that require no energy input can provide substantial benefits for building energy efficiency and urban sustainability. This study presents a research activity, financed by the EU-funded project LIFE SUPERHERO, that enhances existing roofing technologies based on passive cooling; defines an experimental method to assess their benefits in terms of energy savings; and finally evaluates their effectiveness in future climate scenarios based on greenhouse gas Representative Concentration Pathways across a set of mid-temperate/hot climate locations, also in comparison with traditional unventilated roofs. A new Climate Adaptation Efficiency Index (CAEI) was introduced to evaluate the energy efficiency potential of buildings equipped with highly ventilated and permeable clay tile roofs compared to a baseline scenario without the intervention. The results confirm the potential of ventilated and air-permeable roofs to reduce incoming heat flux and support cooling energy-efficiency planning. Indeed, CAEI values were above 20%, reaching 45–50% in hot Mediterranean and arid climates and 28–33% in cooler/temperate contexts. Under future climate scenarios, benefits further increase in the hottest Mediterranean locations, reaching up to 66%, while rising to about 44% in temperate climates, with an average increase of 10–15 percentage points, highlighting the strong potential of highly ventilated and air-permeable clay tile roofs as an effective, affordable, sustainable, and easy-to-install climate adaptation strategy. Full article

With the continuous acceleration of industrialization, air pollution has become increasingly severe and has, to some extent, contributed to the progression of global climate change. Against this backdrop, accurate temperature forecasting plays a vital role in various fields, including agricultural production, energy scheduling, environmental governance, and public health protection. To improve the accuracy and stability of temperature prediction, this study proposes a hybrid modeling approach that integrates convolutional neural networks (CNNs), Long Short-Term Memory (LSTM) networks, and random forests (RFs). This model fully leverages the strengths of CNNs in extracting local spatial features, the advantages of LSTM in modeling long-term dependencies in time series, and the capabilities of RF in nonlinear modeling and feature selection through ensemble learning. Based on daily temperature, meteorological, and air pollutant observation data from Wuhan during the period 2015–2023, this study conducted multi-scale modeling and seasonal performance evaluations. Pearson correlation analysis and random forest-based feature importance ranking were used to identify two key pollutants (PM_2.5 and O₃) and two critical meteorological variables (air pressure and visibility) that are strongly associated with temperature variation. A CNN-LSTM model was then constructed using the meteorological variables as input to generate preliminary predictions. These predictions were subsequently combined with the concentrations of the selected pollutants to form a new feature set, which was input into the RF model for secondary regression, thereby enhancing the overall model performance. The main findings are as follows: (1) The six major pollutants exhibit clear seasonal distribution patterns, with generally higher concentrations in winter and lower in summer, while O₃ shows the opposite trend. Moreover, the influence of pollutants on temperature demonstrates significant seasonal heterogeneity. (2) The CNN-LSTM-RF hybrid model shows excellent performance in temperature prediction tasks. The predicted values align closely with observed data in the test set, with a low prediction error (RMSE = 0.88, MAE = 0.66) and a high coefficient of determination (R² = 0.99), confirming the model’s accuracy and robustness. (3) In multi-scale forecasting, the model performs well on both daily (short-term) and monthly (mid- to long-term) scales. While daily-scale predictions exhibit higher precision, monthly-scale forecasts effectively capture long-term trends. A paired-sample t-test on annual mean temperature predictions across the two time scales revealed a statistically significant difference at the 95% confidence level (t = −3.5299, p = 0.0242), indicating that time granularity has a notable impact on prediction outcomes and should be carefully selected and optimized based on practical application needs. (4) One-way ANOVA and the non-parametric Kruskal–Wallis test were employed to assess the statistical significance of seasonal differences in daily absolute prediction errors. Results showed significant variation across seasons (ANOVA: F = 2.94, p = 0.032; Kruskal–Wallis: H = 8.82, p = 0.031; both p < 0.05), suggesting that seasonal changes considerably affect the model’s predictive performance. Specifically, the model exhibited the highest RMSE and MAE in spring, indicating poorer fit, whereas performance was best in autumn, with the highest R² value, suggesting a stronger fitting capability. Full article

As gesture-based interactions expand across traditional 2D screens and immersive XR platforms, designing intuitive input modalities tailored to specific contexts becomes increasingly essential. This study explores how users cognitively and experientially engage with gesture-based interactions in two distinct environments: a lean-back 2D television interface and an immersive XR spatial environment. A within-subject experimental design was employed, utilizing a gesture-recognizable smart ring to perform tasks using three gesture modalities: (a) Surface-Touch gesture, (b) mid-air gesture, and (c) micro finger-touch gesture. The results revealed clear, context-dependent user preferences; Surface-Touch gestures were preferred in the 2D context due to their controlled and pragmatic nature, whereas mid-air gestures were favored in the XR context for their immersive, intuitive qualities. Interestingly, longer gesture execution times did not consistently reduce user satisfaction, indicating that compatibility between the gesture modality and the interaction environment matters more than efficiency alone. This study concludes that successful gesture-based interface design must carefully consider the contextual alignment, highlighting the nuanced interplay among user expectations, environmental context, and gesture modality. Consequently, these findings provide practical considerations for designing Natural User Interfaces (NUIs) for various interaction contexts. Full article

This study validates a three-zone lumped-parameter airflow model for Urban Building Energy Modeling, focusing on its accuracy in estimating air change rates caused by natural ventilation, referred to here as air change rate. The model incorporates urban-scale variables like canyon geometry and roughness elements for the accurate prediction of building infiltration, which is an important variable in building energy consumption. Air change rate predictions from the three-zone lumped-parameter model are compared against results from a three-zone CONTAM model across a range of weather scenarios. The study also examines the impact of building level of detail on air change rates. Results demonstrate that the three-zone lumped-parameter model achieves reasonable accuracy, with a maximum Mean Absolute Error of 0.1 h⁻¹ in winter and 0.03 h⁻¹ in summer compared to three-zone CONTAM model, while maintaining computational efficiency for urban-scale energy consumption simulations. However, its applicability is limited to buildings within urban canyons rather than detached structures, due to the assumptions made in the methodology of the three-zone lumped-parameter model. The results also showed that the model had lower errors for low to mid-rise buildings since the simplification of a detailed high-rise building into a three-zone model alters the buoyancy effect; a 4-story building showed Mean Absolute Percentage Error of 7% and 5% for a typical winter and summer day respectively when a detailed and simplified three-zone models are compared, while the error for a 16-story building were 18% and 12%. The results of building air change rates are used as input data in an hourly energy consumption model at urban scale and validated against measured hourly consumption to test the effect of the calculated urban-scale hourly air change rates. Full article

Mesoscale air–sea interaction, which is active in Western Boundary Currents (WBCs), has a non-negligible effect on mid-latitude climate variability. The analysis and prediction of the mesoscale air–sea interaction rely on high-resolution observation datasets and mesoscale-resolving climate models, which often require long processing times to estimate future changes and have several limitations. Therefore, in this study, we used a newly developed iTransformer model, which integrates mesoscale sea surface temperature anomaly (SSTa) and latent heat flux anomaly (LHFa) coupling coefficient data to predict future changes in SSTa–LHFa coupling. First, we individually trained the model using data corresponding to 1–15 past winters from ERA5 dataset. Thereafter, we used the trained model to predict SSTa–LHFa coupling coefficient for the next 10 winters. Compared with the predictions using only the coupling coefficient, the prediction yields 3.0% relative improvements when SST data were incorporated. The iTransformer model also showed the ability to reproduce the linear trend and mean value of mesoscale SSTa–LHFa coupling coefficients. Furthermore, we chose the optimal input length for each WBC and used the model to predict changes in mesoscale SSTa–LHFa coupling in the future. The results thus obtained were comparable to those obtained using mesoscale-resolving climate models, indicating that the iTransformer model showed satisfactory prediction performance. Therefore, it provides a novel pathway for exploring mesoscale air–sea interaction variations and predicting future climate change. Full article

The fifth generation of wireless communication (5G) technology is becoming more innovative with the increasing need for high data rates because of the incremental rapidity of mobile data growth. In 5G systems, enhancing device-to-device communication, ultra-low latency (1 ms), outstanding dependability, significant flexibility, and data throughput (up to 20 Gbps) is considered one of the most essential factors for wireless networks. To meet these objectives, a sub-6 5G wideband multiple-input multiple-output (MIMO) array microstrip antenna for 5G Worldwide Interoperability for Microwave Access (WiMAX) applications on hotspot devices has been proposed in this research. The 1 × 4 MIMO array radiating element antenna with a partial ground proposed in this research complies with the 5G application standard set out by the Federal Communications Commission. The planned antenna configuration consists of a hollow, regular circular stub patch antenna shaped like a crescent with a rectangular defect at the top of the patch. The suggested structure is mounted on an FR-4 substrate with a thickness “h” of 1.6, a permittivity “${\epsilon }_r$” of 4.4, and a tangential loss of 0.02. The proposed antenna achieves a high radiation gain and offers a frequency spectrum bandwidth of 3.01 GHz to 6.5 GHz, covering two 5G resonant frequencies “$f_r$” of 3.5 and 5.8 GHz as the mid-band, which yields a gain of 7.66 dBi and 7.84 dBi, respectively. MIMO antenna parameters are examined and introduced to assess the system’s performance. Beneficial results are obtained, with the channel capacity loss (CCL) tending to 0.2 bit/s/Hz throughout the operating frequency band, the envelope correlation coefficient (ECC) yielding 0.02, a mean effective gain (MEG) of less than −6 dB over the operating frequency band, and a total active reflection coefficient (TARC) of less than −10 dB; the radiation efficiency is equal to 71.5%, maintaining impedance matching as well as good mutual coupling among the adjacent parameters. The suggested antenna has been implemented and experimentally tested using the 5G system Open Air Interface (OAI) platform, which operates at sub-6 GHz, yielding −67 dBm for the received signal strength indicator (RSSI), and superior frequency stability, precision, and reproducibility for the signal-to-interference-plus-noise ratio (SINR) and a high level of positivity in the power headroom report (PHR) 5G system performance report, confirming its operational effectiveness in 5G WiMAX (Worldwide Interoperability for Microwave Access) application. Full article

When implementing outside-the-window (OTW) visuals in aviation tactical simulators, maintaining terrain image color consistency is critical for enhancing pilot immersion and focus. However, due to various environmental factors, inconsistent image colors in terrain can cause visual confusion and diminish realism. To address these issues, a color correction technique based on a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) is proposed. The proposed WGAN-GP model utilizes multi-scale feature extraction and Wasserstein distance to effectively measure and adjust the color distribution difference between the input image and the reference image. This approach can preserve the texture and structural characteristics of the image while maintaining color consistency. In particular, by converting Bands 2, 3, and 4 of the BigEarthNet-S2 dataset into RGB images as the reference image and preprocessing the reference image to serve as the input image, it is demonstrated that the proposed WGAN-GP model can handle large-scale remote sensing images containing various lighting conditions and color differences. The experimental results showed that the proposed WGAN-GP model outperformed traditional methods, such as histogram matching and color transfer, and was effective in reflecting the style of the reference image to the target image while maintaining the structural elements of the target image during the training process. Quantitative analysis demonstrated that the mid-stage model achieved a PSNR of 28.93 dB and an SSIM of 0.7116, which significantly outperforms traditional methods. Furthermore, the LPIPS score was reduced to 0.3978, indicating improved perceptual similarity. This approach can contribute to improving the visual elements of the simulator to enhance pilot immersion and has the potential to significantly reduce time and costs compared to the manual methods currently used by the Republic of Korea Air Force. Full article

Durban Port in South Africa is the largest container port and the busiest shipping terminal in sub-Saharan Africa. Approximately 60% of the country’s containerised cargo and 40% of break-bulk cargo transit through Durban. The port is near the central business district, which has a positive spin-off in terms of tourism, recreation, and accessibility to transport and other business activities. The juxtaposition of industry, the port, and the community has resulted in sustained public health implications, a relic of the apartheid era. Like most ports in Africa, Durban Port lacks proper quantification of emissions from marine mobile sources. This study is aimed at estimating atmospheric emissions from ocean-going vessels (OGVs) in and around Durban Port for a period of one year from 1 January 2018 to 31 December 2018 using a mid-tier (activity-based) approach to supplement existing understandings of emissions from local industries. Emission estimates were then inputted to the AERMOD atmospheric dispersion model to allow for a comparison between ambient concentrations and national ambient air quality standards to assess potential health impacts. The study is an advancement in understanding the impact of mobile sources, particularly shipping, on air quality and health, and offers an example for other African ports to follow. Full article

Mid-gesture interfaces have become popular for specific scenarios, such as interactions with augmented reality via head-mounted displays, specific controls over smartphones, or gaming platforms. This article explores the use of a location-aware mid-air gesture-based command triplet syntax to interact with a smart space. The syntax, inspired by human language, is built as a vocative case with an imperative structure. In a sentence like “Light, please switch on!”, the object being activated is invoked via making a gesture that mimics its initial letter/acronym (vocative, coincident with the sentence’s elliptical subject). A geometrical or directional gesture then identifies the action (imperative verb) and may include an object feature or a second object with which to network (complement), which also represented by the initial or acronym letter. Technically, an interpreter relying on a trainable multidevice gesture recognition layer makes the pair/triplet syntax decoding possible. The recognition layer works on acceleration and position input signals from graspable (smartphone) and free-hand devices (smartwatch and external depth cameras), as well as a specific compiler. On a specific deployment at a Living Lab facility, the syntax has been instantiated via the use of a lexicon derived from English (with respect to the initial letters and acronyms). A within-subject analysis with twelve users has enabled the analysis of the syntax acceptance (in terms of usability, gesture agreement for actions over objects, and social acceptance) and technology preference of the gesture syntax within its three device implementations (graspable, wearable, and device-free ones). Participants express consensus regarding the simplicity of learning the syntax and its potential effectiveness in managing smart resources. Socially, participants favoured the Watch for outdoor activities and the Phone for home and work settings, underscoring the importance of social context in technology design. The Phone emerged as the preferred option for gesture recognition due to its efficiency and familiarity. The system, which can be adapted to different sensing technologies, addresses the scalability concerns (as it can be easily extended for new objects and actions) and allows for personalised interaction. Full article

The Beijing–Tianjin–Hebei region (BTH) is one of the crucial areas for economic development in China. However, rapid urban expansion and industrial development in this region have severely impacted the surrounding ecological environment. The air quality, water, and soil resources face significant pressure. Due to the close relationship between land utilization, population, investment, and industry, effective land use is a key factor in the coordinated development of the region. Therefore, clarifying the patterns of urban land use change and revealing its influencing factors can provide important scientific evidence for the coordinated development of the BTH region. This study aims to improve urban land use efficiency (ULUE) in the BTH region. Firstly, based on the input and output data of land elements for the 13 cities in the BTH region, the Data Envelopment Analysis (DEA) method is used to quantify the ULUE of the BTH urban agglomeration and analyze the spatiotemporal characteristics of ULUE. Input indicators includes capital, labor, and land. Output indicators includes economy, society, and environment. The results show that the overall ULUE in the BTH region has increased, albeit with notable fluctuations. Between 2000 and 2010, ULUE rose swiftly across all cities except Beijing and Tianjin, where changes were minimal. Post-2010, cities exhibited varied trends: steady growth, slow growth, sustained growth, step-wise growth, and initial growth followed by decline. Spatially, before 2010, the BTH showed a “high in the northeast and low in the southwest” pattern, transitioning post-2010 to a smoother “core-periphery” pattern. Mid-epidemic, high ULUE values reverted to the core area, shifting southward post-epidemic. Secondly, panel data analysis is conducted to explore the factors influencing ULUE. The results indicate that fiscal balance, the level of openness, the level of digitalization, industrial structure, and the level of green development are significant factors affecting ULUE. Finally, strategies are proposed to improve ULUE in the BTH region, including national spatial planning, industrial layout, existing land use, infrastructure construction, optimization of local fiscal revenue, and improvement in the business environment, aiming to enhance ULUE and promote the coordinated development of industries in the BTH region. Full article

Correcting inefficiencies in the supply chain requires us to reimagine manufacturing by recapturing processes—particularly material sourcing and end-use recycling, which create vast amounts of waste. Inefficiencies in the supply chain create massive waste and stifle innovation in manufacturing, both well-established concerns for the environment. Carbon-based fuels and products are detrimental to the land, air, and sea. Single-use products made from toxic materials flood the food and medical supply chains. Businesses are increasingly moving toward the single purchasing platform model (for example, Uber and Airbnb). Following that model, this paper proposes a platform as a service (PaaS) manufacturing sharing service that matches small- to mid-size manufacturers with production capacity as a solution to obtaining ethically sourced products at a competitive price while offering access to last-mile delivery locally on a single purchasing platform. The development of an Internet of Things (IoT) platform can achieve these four things: (1) provide better coordination of the sourcing and supply of materials, (2) ensure effective provisions of eco-friendly and recycled inputs, (3) provide efficient distribution of equipment and manufacturing resources, and (4) shorten the supply chain by centralizing and coordinating last-mile delivery. Full article