Search Results (358)

Building Information Modelling (BIM) workflows for prefabricated construction lack mechanisms that generate and compare alternative component configurations directly from a design model. Existing approaches define the optimisation search space manually and outside the model, address only one or two criteria, and treat the Work Breakdown Structure (WBS) as a post-design planning tool. This paper reinterprets the WBS as a generative decomposition mechanism. A WBS Engine decomposes the geometry of an existing BIM model into prefabricated subsystems before design decisions are fixed, producing the search space for optimisation without manual parametrisation. A Scenario Evaluator queries a database of 47 prefabricated components, and NSGA-II evaluates 60 configurations against four objectives. These are total cost, embodied carbon, assembly factor and number of lorry trips. Applied to a residential case study implemented in Dynamo, the prototype identified 16 non-dominated solutions. The best compromise configuration achieved a total cost of £150,444.01, 127,731.00 kgCO₂e, an assembly factor of 0.190 and 10 lorry trips. Wall module size accounted for 17.4% of cost variation, while floor module size governed assembly complexity. The findings show that BIM-WBS integration with multi-objective optimisation supports informed early-stage decisions in industrialised construction. Full article

Poor indoor air quality, inadequate ventilation, and unnoticed local disturbances can reduce occupant well-being and compromise practical safety in smart-home and small-building environments. Although low-cost Internet-of-Things (IoT) sensing technologies are widely available, many monitoring systems remain focused on single-modality sensing and do not jointly evaluate environmental conditions, vibration activity, communication reliability, and gateway-side interpretation within one framework. This study presents the design, implementation, and proof-of-concept evaluation of a low-cost, privacy-conscious, non-imaging IoT-based indoor environment and safety-awareness monitoring framework built with ESP32/Arduino sensor nodes and a Raspberry Pi gateway. The system integrates carbon dioxide, temperature, humidity, gas-resistance/VOC-trend indication, and vibration sensing with MQTT-based communication and edge-side analytics. Controlled subsystem experiments showed that CO₂ concentration differentiated ventilation conditions, increasing from 395.47 ppm in the valid empty/open-door baseline to 1083.16 ppm in the closed occupied condition. Vibration states were distinguished using root-mean-square acceleration features across calm, surface-disturbance, footstep, play, and jump conditions. MQTT evaluation using 1000-message batches showed no observed message loss or duplicates across the tested QoS/network combinations, although latency and throughput varied by network configuration and QoS level. QoS 1 provided a practical balance between low latency and protocol-level delivery assurance in the tested local/Wi-Fi setting. A final integrated validation run further demonstrated synchronized acquisition from indoor environmental, vibration, and outdoor CO₂ reference publishers through the same Raspberry Pi gateway, with zero missing or duplicate sequence flags across the three streams. Overall, the findings indicate that lightweight open-source IoT hardware can support a reproducible building-level sensing and edge-analytics prototype for indoor environment and safety-awareness monitoring. Broader deployment in standard-sized rooms, multi-room buildings, and smart-city infrastructure remains future work. Full article

Manipulation localization in Special Equipment Inspection (SEI) images is crucial for trustworthy industrial supervision. However, existing image manipulation localization models struggle to handle the compound degradation disturbances commonly encountered in SEI scenarios. To address this issue, we propose a Prototypical Contrastive-Synergistic Network (ProCoS). The proposed model enhances consistent discrimination between clean and degraded observations through the Degradation-Resilient Synergistic Learning architecture, while introducing a Prototypical Contrastive Learning mechanism to improve stable representation under compound degradation. Furthermore, we construct SEI-Asym, a manipulation localization dataset for SEI scenarios, and establish a compound degradation evaluation protocol based on orthogonal experimental design. Experimental results show that ProCoS achieves an F1 of 0.6531, an AUC of 0.9670, and an IoU of 0.5485 on SEI-Asym, and reduces $\overline{\Delta F1}$ and ${(\Delta F1)}_{\max }$ under compound degradation to 0.0873 and 0.1966, respectively. The proposed model provides an effective technical pathway for trustworthy perception, anomaly discrimination, and industrial supervision based on SEI images. Full article

Buildings in warm–humid and hot–arid coastal climates experience continuous cooling demand due to high solar radiation, humidity, and extended cooling seasons. Reducing operational energy use and carbon emissions through improved early-stage design is therefore essential. This study investigates a simulation-based evolutionary optimization framework to evaluate energy-efficient design strategies for residential buildings across representative warm–humid and hot–arid climates. A prototype residential building was modeled in DesignBuilder using EnergyPlus and evaluated across four locations: Singapore, Miami, Rio de Janeiro, and Jeddah. Key variables included the window-to-wall ratio, glazing type, wall and roof constructions, cooling setpoint, and HVAC system configuration. An evolutionary search process based on the NSGA-II algorithm was applied to systematically explore high-performing building configurations using energy use intensity (EUI) and operational carbon indicators. The results indicate a consistent tendency toward boundary values within the defined parameter ranges. The window-to-wall ratios consistently approached the minimum tested value (20%), while the cooling setpoints approached the upper bound (26 °C) within the defined parameter ranges. This behavior highlights the influence of solar gains and operational temperature settings on cooling demand. Low-emissivity glazing and insulated envelope assemblies were frequently associated with improved performance. Miami achieved the lowest EUI among the high-performing configurations (75.08 kWh/m²·yr; 27.55 kgCO₂/m²·yr), while other locations showed higher demand due to climatic conditions. These findings emphasize the importance of parameter range selection and demonstrate the effectiveness of simulation-based evolutionary search methods in identifying high-performing configurations within defined constraints. Full article

Protected horticulture moves fragile pots, plug trays, seedlings, harvested products, and carriers through narrow, humid, and crowded spaces. Transport robots must therefore integrate locomotion, perception, localization, handling, placement, scheduling, and human–robot interaction rather than operate as simple carts. This structured narrative review reorganizes evidence from seedling transplanting, nursery operations, harvest support, manipulation, perception, and autonomous navigation around the complete transport chain: target recognition, pickup, loading, loaded navigation, docking, unloading or placement, payload protection, and workflow feedback. The synthesis covers mobile platforms, payload support, perception and localization, motion control, gentle handling, digital support, and fleet coordination. Three barriers remain: short laboratory tests rarely provide season-long evidence; many prototypes are too specialized for variable workflows; and benchmarks seldom combine motion accuracy, handling reliability, payload quality, and resilience. Progress will require modular platforms, robust sensing, payload-safe control, standardized interfaces, and closer co-design between robotics and horticultural operations. Full article

Drawing on mobile foot reflexology in Taiwan, this article examines innovation readiness in small-scale wellness services where formal R&D resources, standardized workstations, and organizational support systems are limited. It conceptualizes readiness as a staged service-design condition comprising problem-recognition readiness, practitioner-agency readiness, co-creation readiness, and implementation-fit readiness. The empirical design integrated workplace observation, a survey of 59 therapists, semi-structured interviews with 10 therapists, expert consultation with 7 specialists, and two rounds of prototype evaluation (n = 17 and n = 19). Rather than treating ergonomic symptoms as an isolated occupational health outcome, the analysis traces how discomfort, posture constraints, psychosocial resources, practitioner narratives, and expert judgment were translated into design parameters and two chair prototypes for mobile service delivery. Three cross-phase mechanisms emerged: constraint visibility, practitioner-mediated translation, and implementation-fit testing. Shoulder, wrist/hand, and low-back discomfort signaled unresolved operational friction; high meaning and competence scores pointed to a practitioner resource base for adaptive participation; and staged prototype testing identified portability, adjustability, stability, and bodily comfort as the central adoption conditions. The article contributes to Administrative Sciences by showing that grassroots service innovation readiness is not simply an attitudinal state but an enacted process through which field constraints are made visible, jointly interpreted, and converted into a deployable service-support solution. Beyond this case, the staged readiness logic may also inform mobile wellness, community-care, rehabilitation-support, personal-care, and other low-resource service organizations that must convert frontline constraints into feasible service-support interventions. Full article

Urban stormwater is often viewed as a drainage problem rather than a local water resource, even in areas where runoff capture could simultaneously reduce flooding and promote the reuse of non-potable water. This study develops, installs, and field-tests a decentralized, school-scale stormwater harvesting system that relocates permeable concrete, transforming it from a passive infiltration surface into a purpose-built capture and pretreatment interface. The system integrates a 3 m × 3 m permeable concrete slab with load-bearing sections, an impermeable underlayer to ensure controlled flow, a double-compartment sump for staged sedimentation and hydraulic damping, sequential filtration with sand/gravel and activated carbon, and a 5000 L storage tank. The prototype was implemented at CETis 105 in Querétaro, Mexico, and evaluated during its commissioning and operation in the 2023 rainy season. Field operations demonstrated reduced ponding in the catchment area and a reliable flow of runoff to the pretreatment units. In the sump compartments, apparent color decreased from 221 to 59 Pt-Co, turbidity from 46.8 to 12.9 NTU, and COD from approximately 30–35 to 15–18 mg·L⁻¹, corresponding to approximate pretreatment reductions of 73.3%, 72.4%, and 40–57%, respectively, before post-filtration. Conversely, the elevated pH, electrical conductivity, and total dissolved solids indicated interaction with fresh cementitious materials and dissolved ionic residues during initial operation, highlighting the need for curing, initial washing, and post-filtration verification before declaring compliance with reuse requirements. Therefore, the results support the feasibility of the proposed configuration as a decentralized, low-infrastructure architecture for localized runoff control and pretreatment, while confirming that full reuse validation still requires microbiological and post-filtration evaluation. The study provides a field-proven system design adaptable to school campuses and similar institutional environments for distributed stormwater management and non-potable water storage. Full article

Background/Objectives: Adolescent mental health problems emerge early, remain undertreated, and are shaped by diverse contextual stressors. In response to calls for more youth-centered prevention, school-based health promotion, and participatory intervention design, this study explored which mental health-related problems internationally mobile adolescents prioritize and which solution ideas they generate in a structured co-creation setting, including where movement- and sport-related elements are embedded. Methods: A qualitative, participatory study was conducted during a 24 h social hackathon embedded in the Youth Empowerment Seminar for exchange students. Hackathon materials from 43 projects were analyzed using content-structuring qualitative content analysis following Kuckartz. Results: Adolescents most frequently framed problems in terms of self-image, stress and anxiety, belonging, and harassment. Solutions clustered around low-threshold group formats, while implementation segments focused strongly on staffing, funding, barriers, and feasibility. Cross-domain analyses suggested recurring problem-solution matches, such as loneliness with hobby or interest groups. Conclusions: Social hackathons can surface adolescent-prioritized mental health concerns and translate them into context-sensitive prevention ideas. The findings mainly point to social and psychosocial solution pathways, while some proposals additionally positioned shared activity or movement contexts as potentially supportive for well-being. These results provide a starting point for subsequent school-based prototyping and feasibility work. Full article

To activate conscious reflection regarding personal identity and identity-building processes in our daily lives is an increasing social concern. With this aim, we designed an Alternate Reality Game that invites participants to collectively explore these themes. Participants played with a prototype, evoking themes of identity through emergent dynamics from gameplay and interpersonal interactions. We analyzed participants’ appropriation of the prototype through logged activity, direct observation and interviews. The identified dynamics enabled iterative redesign and further exploration of the players’ interaction and behaviors. From this process, we synthesized four design insights as our main findings that may guide further research in the field: (1) how to explore design–play–reflect as a co-design process supported on individual appropriation, (2) how ARGs generate reflective social phenomena, such as varied social identity and power narratives, (3) how ARG design can open doors to balance power dynamics, and (4) how ARG designs can become generative social theories. Our main contributions, alongside the designed prototype, are these four insights, and their potential scalability to other ARG designs that seek to provoke social phenomena and collaborative interventions. Full article

This work examines the contribution of additive manufacturing as an enabling technology in the design and development of smart and sustainable construction systems, with particular emphasis on nature-based solutions. While the existing literature has devoted considerable attention to the material properties of additive manufacturing, much less emphasis has been placed on its role in design processes, prototyping, and decision-making in construction and urban systems. To address this gap, this study presents a comprehensive bibliometric analysis of the intersection between smart city frameworks and 3D printing technologies, utilizing a dataset of 103 peer-reviewed publications retrieved from the Scopus database. Using keyword co-occurrence analysis and network mapping through VOSviewer, this study identifies dominant thematic structures, core research hubs, and evolving trends within the field. Complementing this bibliometric analysis with qualitative synthesis, it also reveals a significant convergence of digital design, smart cities, and sustainability strategies. This work further highlights the contribution of additive manufacturing to design processes through rapid prototyping, customization, and the exploration of design alternatives. Rather than framing additive manufacturing as a replacement for conventional design practices, this study positions it as a complementary design capability that can enhance the design process, while also acknowledging important challenges related to scaling, regulation, and integration into construction workflows. This review concludes by outlining future research directions for strengthening the design-oriented integration of additive manufacturing within smart construction systems. Full article

A compact millimeter-wave radar system operating at 33 GHz is presented for integration on small unmanned aerial systems (UAS) and for ground-based counter-UAS reconnaissance. The design is specifically motivated by civil-sector agricultural applications, where large-payload crop-dusting and precision-spraying drones operating under FAA 14 CFR Part 137 require lightweight sense-and-avoid radar that conforms aerodynamically to existing aircraft or ground vehicles. The system is based on a 36-element hemispherical conformal phased array of crossed half-wave dipole radiators that generate right-hand circular polarization (RHCP) on transmit and selectively receives left-hand circular polarization (LHCP) echoes from targets, providing passive first-stage suppression of co-polarized rain and ground clutter. A Linearly Constrained Minimum Variance (LCMV) digital beamformer, applied to per-element analog-to-digital converter (ADC) outputs, delivers closed-form beam weights that enforce a distortionless response at each scan direction while globally minimizing sidelobe power. The formulation resolves the main-beam drift caused by the ill-conditioned re-scaling step in iterative Chebyshev tapering, achieving sidelobe levels below $-20$ dB with main-beam peaks within $0.1$° of their commanded angles across all evaluated positions. Mutual coupling between array elements is modeled analytically using the induced-EMF method, yielding a $36\times 36$ impedance matrix whose off-diagonal entries are at most 8.2% of the element self-impedance at the minimum inter-element separation of 2.70 $\lambda$. A closed-form decoupling matrix is applied to the receive manifold prior to LCMV weight computation. Seven simultaneous independent receive beams covering $0$°–$60$° elevation are formed from a single data snapshot. A Scaled Conjugate Gradient neural network classifier, trained on radar-equation-scaled micro-Doppler features following Swerling I–IV radar cross-section (RCS) fluctuation statistics, achieves overall classification accuracy above 85% across five target classes. The five classes comprise two bird-signature classes (SW-I and SW-II), two UAV-signature classes (SW-III and SW-IV), and a clutter class. The design is entirely simulation-based; experimental validation using a sub-array prototype is identified as the primary direction for future work. Full article

With the escalating demand for high reliability, low power consumption, and high throughput baseband processing capabilities in 5G and future 6G communication systems, the channel coding and decoding IP core serves as a critical module in baseband chips, where its hardware architecture and numerical system design exert a decisive influence on system performance. This paper proposes a SOVA-Turbo codec IP core architecture tailored for FPGA/SoC system integration. By reconstructing the Turbo iterative decoding data flow, a pipelined architecture is designed to facilitate the parallel operation of component decoders, as well as the storage and distribution of extrinsic information. Furthermore, addressing the numerical complexity of the SOVA algorithm in hardware implementation, a customized fixed-point representation and quantization co-design scheme is proposed to support hardware-efficient implementation while preserving the relative reliability relationships required for soft-decision decoding. Additionally, bit-level soft reliability information is generated at the decoder output to support iterative reliability evaluation under controlled noisy conditions. Simulation and implementation results demonstrate the feasibility of the proposed prototype in terms of timing closure, hardware cost, and decoder-side robustness under controlled perturbation. Full article

Human Activity Recognition (HAR) from inertial sensors must run continuously on battery-powered wearables under tight latency, memory, and energy budgets. While tiny Transformers can be effective on inertial time series, end-to-end co-design across quantized inference and heterogeneous low-power platforms remains underexplored. We present STORM (Small Transformer for On-node Recognition of Motion), a deployment-oriented 19.7k-parameter 1D Transformer co-designed with X-HEEP, an open-source low-power single-core RISC-V SoC, and a tightly coupled streaming CGRA for nonlinear primitives (e.g., softmax). We build a cross-source 8-class benchmark by harmonizing 3 public datasets under a stringent, deployment-aligned protocol that exposes both cross-subject and cross-source shift. Using 1.280 s windows with 0.640 s stride, the protocol models continuous on-node HAR under cross-dataset generalization. After quantization-aware training and INT8 C inference export, STORM achieves 0.799/0.801 accuracy/macro-F1 on this benchmark. Deployed on an FPGA prototype of X-HEEP with the streaming CGRA backend, STORM requires 67.4 ms per inference at 100 MHz, while activity-based power analysis estimates a total inference energy of 632.4 $\mathsf{\mu }$J, satisfying the stride-driven real-time constraint. These results support the practical viability of compact attention-based HAR on low-power wearable-class embedded platforms. Full article

A broadband low-RCS circularly polarized (CP) antenna based on a bi-functional, single-layer polarization conversion metasurface (PCM) is proposed in this manuscript. The designed bi-functional PCM unit cell achieves a polarization conversion ratio (PCR) exceeding 90% across an ultra-wideband from 15.8 GHz to 31.2 GHz. According to the principle of phase cancellation, they are configured as a checkerboard array to reduce the monostatic RCS. A co-design strategy was employed for the design of the feeding structure. Analysis reveals that the slot has a significant impact on the subarray PCR, leading to multiple zeros that affect the RCS reduction. Notably, further analysis indicates that an appropriate feed structure can compensate for the zeros caused by the slot, achieving a balance between radiation performance and scattering performance. The array exhibits an RCS reduction exceeding 6 dB over a wide frequency band from 15.9 to 31.3 GHz and realizes a circularly polarized far-field pattern with an axial ratio (AR) below 0.5 from 16.3 to 17 GHz and a maximum gain of 10.38 dBi. Measured results of the antenna prototype match the simulations well. The proposed integrated design offers a viable solution for stealth platforms. Full article

Lightweight Unmanned Aerial Vehicles (UAVs) have limited space, low payload capacity, and constrained power supply capabilities. Therefore, their payloads are constrained by size, weight, and power (SWaP). Thus, designing edge-side signal processing architectures for the payloads of UAVs faces severe challenges. Traditional ASIC design based on manual optimization struggles to meet the demands of low latency and low resource occupancy in edge-side applications. To address this challenge, this paper proposes a signal processing hardware accelerator architecture design framework with algorithm-hardware co-design. The framework employs a cross-level dataflow graph representation to formally capture task characteristics. Reconfigurable dataflow templates and reusable operator IP components are systematically constructed based on this representation. Through multi-objective design space exploration, the framework achieves Pareto-optimal mapping from algorithmic specifications to hardware implementations. Finally, automatic generation of top-level hardware descriptions enables rapid FPGA-based prototyping and functional validation. Taking synthetic aperture radar (SAR) imaging as a study example, compared with non-reconfigurable architectures, this scheme reduces the equivalent gate count by 51.4% without increasing processing latency. Compared with a conventional reconfigurable dataflow architecture, the design improves energy efficiency from 12.8 MS/J to 16.0 MS/J, representing a 25.4% enhancement, while also scaling the supported data processing size by a factor of 4×. It provides a high-performance and scalable hardware acceleration solution for lightweight edge-side computing platforms. Full article