The Simple Modular Architecture Research Tool (SMART) is an online resource () used for protein domain identification and the analysis of protein domain architectures. Many new features were implemented to make SMART more accessible to scientists from different fields. The new ‘Genomic’ mode in SMART makes it easy to analyze domain architectures in completely sequenced genomes. Domain annotation has been updated with a detailed taxonomic breakdown and a prediction of the catalytic activity for 50 SMART domains is now available, based on the presence of essential amino acids. Furthermore, intrinsically disordered protein regions can be identified and displayed. The network context is now displayed in the results page for more than 350 000 proteins, enabling easy analyses of domain interactions.
As architecture, systems, and data management communities pay greater attention to innovative big data systems and architecture, the pressure of benchmarking and evaluating these systems rises. However, the complexity, diversity, frequently changed workloads, and rapid evolution of big data systems raise great challenges in big data benchmarking. Considering the broad use of big data systems, for the sake of fairness, big data benchmarks must include diversity of data and workloads, which is the prerequisite for evaluating big data systems and architecture. Most of the state-of-the-art big data benchmarking efforts target evaluating specific types of applications or system software stacks, and hence they are not qualified for serving the purposes mentioned above. This paper presents our joint research efforts on this issue with several industrial partners. Our big data benchmark suite-BigDataBench not only covers broad application scenarios, but also includes diverse and representative data sets. Currently, we choose 19 big data benchmarks from dimensions of application scenarios, operations/ algorithms, data types, data sources, software stacks, and application types, and they are comprehensive for fairly measuring and evaluating big data systems and architecture. BigDataBench is publicly available from the project home page http://prof.ict.ac.cn/BigDataBench. Also, we comprehensively characterize 19 big data workloads included in BigDataBench with varying data inputs. On a typical state-of-practice processor, Intel Xeon E5645, we have the following observations: First, in comparison with the traditional benchmarks: including PARSEC, HPCC, and SPECCPU, big data applications have very low operation intensity, which measures the ratio of the total number of instructions divided by the total byte number of memory accesses; Second, the volume of data input has non-negligible impact on micro-architecture characteristics, which may impose challenges for simulation-based big data architecture research; Last but not least, corroborating the observations in CloudSuite and DCBench (which use smaller data inputs), we find that the numbers of L1 instruction cache (L1I) misses per 1000 instructions (in short, MPKI) of the big data applications are higher than in the traditional benchmarks; also, we find that L3 caches are effective for the big data applications, corroborating the observation in DCBench.
Chimeric antigen receptor (CAR) T-cell therapy has achieved significant success in hematologic malignancies, but its efficacy in solid tumors remains limited, primarily due to the immunosuppressive tumor microenvironment (TME) that hinders CAR-T cell trafficking and function. NKG2D CAR-T cells, which target stress-induced NKG2D ligands (NKG2DLs) broadly expressed on tumor cells, have shown promising potential in overcoming the immunosuppressive barriers of the solid TME. This review highlights recent advances in NKG2D CAR-T cell strategies for solid tumors, including innovations in CAR architecture, signaling pathway engineering, combination immunotherapy, and the development of armored CAR constructs. We further discuss the therapeutic potential, current challenges, and future directions of these approaches to inform the design of more effective and durable CAR-T cell therapies for solid tumors.
Driven by the combined effects of global warming and the urban heat island (UHI) effect, building energy consumption has been rising steadily in recent years. The photovoltaic-cool roof (PVCR) system has emerged as an effective solution for urban energy conservation and carbon reduction. However, existing research on the energy-saving benefits of PVCR remains relatively limited, and none of these studies have considered the interaction between photovoltaic modules and high-reflectivity roofs (also called cool roof, CR). Therefore, field experiments were conducted to compare the thermal performance of the PVCR system against that of three conventional roof configurations, including photovoltaic roof (PVR), asphalt roof (AR), and CR. The results demonstrate that the PVCR system achieves a remarkable daytime cooling effect, with a maximum temperature reduction of 29 °C compared to the AR system, and maintains lower temperature fluctuations throughout the entire day. In addition, the findings reveal that the photovoltaic modules exhibit a lower average temperature when installed on the cool roof, with a temperature decrease of 0.15 °C relative to the asphalt roof. A numerical model incorporating the photothermal interaction between a high-reflectivity surface and PV modules was developed and validated with experimental data. The numerical model considers the interactions between the photovoltaic (PV) modules and the high reflectivity surface, including shortwave radiation reflection, longwave radiative exchange, and convective heat transfer. The sensitivity analysis indicates that a change in the spacing and height of the PV arrays from 0.3 m to 0.5 m increases the relative energy-saving efficiency of the system. The conclusions drawn in this paper can provide a reference for the application of the PVCR system in hot-summer and cold-winter areas.
Underwater photogrammetry presents unique challenges, including light attenuation, refraction, and turbidity, that affect the accuracy and quality of 3D reconstructions. This study investigates the performance of novel neural rendering techniques, Neural Radiance Fields (NeRF), SeaThru-NeRF, and 3D Gaussian Splatting (3DGS), in comparison to conventional Structure-from-Motion (SfM) workflows. Using a dataset acquired during the SIFET benchmark campaign on a submerged Roman archaeological site, we processed image data via Nerfacto, SeaThru, and Jawset Postshot (3DGS) and compared outputs against a reference model produced in Agisoft Metashape. Evaluation criteria included processing time, geometric accuracy (via M3C2 analysis), point cloud density and roughness, and point cloud completeness. Results show that radiance fields-based methods significantly reduce processing time while providing competitive visual results. SeaThru-NeRF demonstrated the highest geometric accuracy, benefiting from underwater-specific corrections, while 3DGS offered photorealistic rendering. These findings highlight the potential of neural methods for underwater cultural heritage documentation, though further improvements are needed in data fidelity and robustness under challenging underwater conditions.
ABSTRACT The purpose of this study is to gain knowledge about the coexistence of monument protection and energy in southern Germany's Old Town through resident intentions, laws, and permit decision criteria. First, there is a movement to allow rooftop PV on buildings in the Old Town. Next, there are two requirements for rooftop PV installation under the Old Town Protection Law: (1) building evaluation of monument protection and impact on surrounding buildings, etc. and (2) visibility from public spaces. It has become clear that the expansion of the interpretation of “public space” as a viewpoint is protecting historic buildings.
Architecture, Architectural engineering. Structural engineering of buildings
Randika K. Makumbura, Jagath Manatunge, Upaka Rathnayake
Accurate streamflow prediction is essential for effective water resource planning and management. Although physics-based hydrological models such as SWAT and WEAP are commonly used for streamflow simulation, they often encounter limitations due to structural complexity, rigid conceptual assumptions, and sensitivity to parameter calibration. In this study, LSTM models are utilized as a data-driven alternative for monthly streamflow prediction in the Kelani River Basin (KRB), Sri Lanka. Three variations of the LSTM architecture, Vanilla LSTM, Stacked LSTM, and Bidirectional LSTM (Bi-LSTM), are assessed and compared against conventional physics-based models, including SWAT and WEAP. Results illustrated that LSTM models consistently outperform SWAT and WEAP during both calibration and validation phases. During calibration, LSTM models achieved high accuracy with NSE values nearing 0.95, R² between 0.95 and 0.96, PBIAS ranging from 2.03 to 4.56, and RSR between 0.21 and 0.23. Physics-based models exhibited lower performance (NSE: 0.71–0.74; R²: 0.74–0.83; PBIAS:23.67 to 4.7; RSR: 0.51–0.54). Validation results confirmed this trend, with LSTM models maintaining strong performance (NSE: 0.82–0.84; R²: 0.84–0.88; PBIAS:11.5 to –15.60; RSR: 0.40–0.43), while physics-based models displayed weaker predictive capability (NSE: 0.50–0.61; R²: 0.66–0.81; PBIAS:16.33 to –42.14; RSR: 0.62–0.71). Among the LSTM variations, Bi-LSTM demonstrated the best performance during calibration, while Stacked LSTM proved to be more effective during validation. The study underscores the robustness and reliability of LSTM models for monthly streamflow prediction, presenting a valuable approach for long-term water resource management in the KRB.
The article explores the revitalization of Italy's "inner areas" - —fragile territories facing challenges such as depopulation, abandonment, and a lack of essential services, primarily due to limited mobility. The enhancement of secondary and disused railways is proposed as a tool for territorial rebalancing, to be achieved through multilevel governance that integrates spatial planning, interinstitutional cooperation, and the strategic allocation of financial and economic resources. This approach aims to move beyond sectoral perspectives on infrastructure networks.
The Axis Contract is introduced as an integrated framework linking urban planning and mobility, centered on the right to mobility and the empowerment of local communities.
The article is structured into three parts: the first examines the relationship between territory, mobility, and infrastructure policies; the second analyzes the French Contrat d’axe model and its applicability in Italy; and the third presents the results of an interdisciplinary study on the reactivation of the Civitavecchia -Capranica - Orte railway line.
The findings confirm that the Axis Contract is an effective tool for integrating urban and mobility planning, addressing accessibility needs, and promoting the sustainable rebalancing of territories.
Transportation engineering, Urbanization. City and country
Nisha Patel, Britta Jänicke, René Burghardt
et al.
ABSTRACT An increasing number of cities in Germany and Europe are formulating adaptation strategies to address the consequences of climate change. Nevertheless, quantifying whether these strategies contribute to alterations in urban infrastructure and promote climate‐sensitive urban development is challenging. This article aims to explore possible urban climate adaptation indicators (UCAIs) from literature suitable for assessing the implementation of heat‐ and water‐sensitive urban development measures in local municipalities, with a focus on Germany. In addition to a literature review, workshops and discussions with experts from Germany complemented and deepened the indicator selection process. As a result, we identified 27 indicators, which were grouped into 5 key areas: (1) surface and urban overheating indicators; (2) building type and structure indicators; (3) green infrastructure indicators; (4) soil‐sealing indicators; and (5) water‐sensitive urban development indicators. Only a few manage to map several adaptation measures, avoiding conflicts with other urban planning objectives, can be derived for cities at the national level and show promise for capturing small‐scale adaptation measures in the city. We concluded that, in particular, the green infrastructure and soil‐sealing indicators, such as green cover, access to greenery and green supply have a high potential to meet heat‐ and water‐sensitive urban development goals, while avoiding conflicts of objectives and trade‐offs. Overall, this review underscores the necessity for additional research and testing to formulate practical and effective indicators for capturing heat‐ and water‐sensitive aspects of urban development.
In a world facing displacement, violent conflicts, climate change, and urban sprawl, urban settings are plagued by
inequality, poverty, injustice, and racial and class divisions. These divisions exacerbate violence, crime, and racism. As
urban planners, it is crucial to recognize and develop solutions for present and emerging challenges. Evaluating diverse
social activities with stakeholder participation is essential for fulfilling public interests. This study aims to identify best
practices for socially sustainable urban environments by leveraging urban diversity and social participation. By
envisioning inclusive, resilient, and socially sustainable cities, this research investigates participatory urban design
through case studies of social-diversity-focused projects worldwide. The methodology includes selecting case studies
based on criteria like geographical representation and levels of community engagement. The objective is to derive
implementable recommendations for creating inclusive, resilient, and socially sustainable urban environments by
analyzing the impact of social diversity and participation on urban resilience.