Workforce training is a cornerstone of organizational success, as the value of intellectual capital increasingly rivals that of physical and financial assets in today’s knowledge-driven economy. The emergence of consumer-grade head-mounted display (HMD)-based virtual reality (VR) technology offers organizations innovative opportunities to meet evolving workforce training needs. This study employs a multi-method investigation to systematically examine HMD-based VR training and education, with a focus on presence as a core experiential quality of VR technology and an important determinant of learning effectiveness. First, we synthesize the literature on HMD-based VR in training and education through the Community of Inquiry (CoI) framework’s four interdependent components: cognitive, teaching, social, and emotional presence. The findings highlight the dynamic interplay among different dimensions of presence and their collective impact on learning outcomes, providing an integrated framework to inform the design of HMD-based VR training and education programs. To validate the real-world relevance of the CoI components and inform design practices, we conduct semi-structured interviews with a diverse group of stakeholders, including executives and managers from select Fortune 500 companies that have integrated HMDs into their workflows, professionals from companies specializing in VR training, pedagogical experts, VR application developers, and content creators. We identify 80 key design factors linked to different dimensions of presence. By bridging theory with practical insights, this study underscores the central role of presence in shaping immersive learning experiences and provides a foundation for designing impactful HMD-based VR training and education programs. • Presence is a core characteristic that is associated with improved learning outcomes in HMD-based VR training. • The impact of cognitive, teaching, social, and emotional presence varies by training goals, context, and learner traits. • Presence is not just a byproduct of HMD-based VR but a construct that can be deliberately designed to support learning. • Organizations should define training objectives and identify which presence domains are most critical for achieving them. • HMD-based VR should be selectively deployed where it affords learning value not easily achievable with traditional methods.

In this paper, the detailed analysis of a Multipole Permanent Magnet assisted Synchronous Reluctance Machine is presented which can cater to the needs of EV applications. The rotor of the machine has a 4-pole (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula>-pole) Synchronous Reluctance Machine (SynRM) structure, and a 12-pole (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$3n$</tex-math></inline-formula>-pole) Permanent Magnet (PM) structure. There exist two rotating magnetic fields in the air-gap of the machine with poles in the ratio of 1:3. A single stator houses two sets of 3-<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\phi$</tex-math></inline-formula> windings, corresponding to each pole. To ensure steady torque operation, the supply frequencies are also kept in the ratio of 1:3. While designing, base speed of the SynRM is kept at a lower speed (say <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula>), and that of PM is kept at the maximum speed (say <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N_{max}$</tex-math></inline-formula>). Both components are operated till an intermediate speed, after which, the excitation of SynRM is removed without the use of any switches, since the back-EMF in SynRM due to PM is negligible. Only the PM part is operated till maximum speed, without entering Field-Weakening (FW). Hence, the proposed machine can be used to obtain extended speed with improved efficiency due to the absence of extra FW current. The proposed concepts are used to design a 1kW 4pole-12pole PMaSynRM. The designed machine is manufactured and tested in closed loop for experimentation and validation.

Investigating narrative styles in animated patient stories to increase public awareness and genetic literacy in a multiethnic Asian population.

We explore the potential of visualization to support musicians in instrument practice through real-time feedback and reflection on their playing. Musicians often struggle to observe patterns in their playing and interpret them with respect to their goals. Our premise is that these patterns can be made visible with interactive visualization: we can make the unhearable visible. However, understanding the design of such visualizations is challenging: the diversity of needs, including different instruments, skills, musical attributes, and genres, means that any single use case is unlikely to illustrate the broad potential and opportunities. To address this challenge, we conducted a design exploration where we created and iterated on 33 designs, each focusing on a subset of needs, for example, only one musical skill. Our designs are grounded in our own experience as musicians and the ideas and feedback of 18 musicians with various musical backgrounds and we evaluated them with 13 music learners and teachers. This paper presents the results of our exploration, focusing on a few example designs as instances of possible instrument practice visualizations. From our work, we draw design considerations that contribute to future research and products for visual instrument education.

The oscillatory baffled reactor (OBR) is one of the most promising reactor types for oxidative desulfurisation (ODS) due to high proven sulfur removal efficiency and improved mass and heat transport steps through the oxidation reaction. In Part III of this literature review, we review OBR design, scale-up, industrial application and economic considerations. Practical application of OBR for ODS process is described, catalytic systems are presented and the remaining challenges are outlined.

Afghanistan faces progressively regular and severe floods that cause substantial loss of life, destroy the infrastructure, and interrupt livings. Structural mitigation measures mainly protective walls built from locally available boulders offer practical, low-cost solutions in this resource-constrained context. This review synthesizes national and international literature on flood risk reduction using protective walls, with special importance on boulder walls in Afghanistan's flood-prone provinces. We assess design considerations, efficiency, environmental impacts, and implementation challenges. Key results show that while boulder walls provide cost-effective, community-driven flood mitigation, they require technical design support and integration with non-structural measures such as early warning systems and land-use planning to attain sustainable risk reduction. This review offers evidence-based guidance for practitioners, policymakers, and communities working to improve flood resilience across Afghanistan.

The development of novel agents with curative intent for chronic hepatitis B (CHB) has accelerated, but few candidates transition to phase III trials. We evaluated trial design features associated with registry-defined trial completion and phase transition, and synthesized end-of-treatment hepatitis B surface antigen (HBsAg) decline as an on-treatment antiviral signal. We systematically reviewed clinical trial registries and bibliographic databases for CHB cure trials. Associations between trial design characteristics and trial performance were analyzed using Cox regression (time to registry-defined completion), logistic regression (phase I to phase II transition), and random-effects meta-analysis of end-of-treatment HBsAg decline. Larger sample size (HR = 0.819, 95% CI = 0.710-0.945) and longer treatment duration (HR = 0.897, 95% CI = 0.816-0.987) were associated with a lower likelihood of registry-defined completion in phase I trials. In phase II trials, larger sample size was associated with a lower likelihood of registry-defined completion (HR = 0.936, 95% CI = 0.879-0.997). Novel trial designs were not associated with faster completion. Among phase I trials with a definitive status, trials enrolling healthy volunteers were more often followed by phase II registration (adjusted OR = 2.82, 95% CI = 1.16-7.08). Meta-analysis showed that direct-acting antivirals were associated with marked end-of-treatment HBsAg decline (SMD = 1.28, 95% CI = 0.47-2.08). Smaller and shorter early-phase trials were more likely to complete, whereas novel trial designs were not associated with faster completion. Direct-acting antivirals showed marked end-of-treatment HBsAg decline, an on-treatment signal rather than durable functional cure.

Lipid nanoparticles (LNPs) have become the leading platform for nucleic acid delivery; yet, their therapeutic performance is strongly influenced by the membrane-based biological barriers they encounter in vivo. These barriers exist at multiple hierarchical levels, ranging from extracellular matrices and mucosal layers to endothelial interfaces, plasma membranes, and intracellular organelles. Each barrier imposes distinct physicochemical constraints that shape LNP transport, cellular entry, and cargo release. This review provides a mechanistic framework for understanding LNP-membrane interactions by integrating membrane biophysics, lipid chemistry, functional interfacial forces, and the acquired feature, i.e., protein corona. We also describe the interfaces encountered during major administration routes, including intramuscular, intravenous, intraperitoneal, inhaled, oral, and intratumoral delivery, and highlight how local architecture, fluid composition, and protein corona evolution influence biodistribution and targeting. At the cellular level, we compare the membrane properties of parenchymal, stromal, and immune cells, emphasizing how differences in surface chemistry, receptor expression, and endocytic pathways determine LNP binding and internalization. We then discuss the key organellar processes that regulate intracellular trafficking and mRNA release, including endosomal maturation, acid-triggered lipid protonation, fusion-driven membrane remodeling, and nonbilayer phase transitions. Moreover, we outline recent evidence for LNP transcytosis across restrictive barriers such as the blood-brain barrier, tumor vasculature, pulmonary epithelium, and intestinal mucosa, as well as the contribution of exocytosis and extracellular vesicles to secondary mRNA delivery. Finally, we highlight emerging experimental and computational tools for probing LNP-membrane interactions. By integrating molecular design considerations with membrane biophysics and nanobio interface chemistry, this review aims to provide a mechanistic and strategic perspective to guide the rational development of next-generation LNP delivery systems.

Abstract With the growing demand for long-span bridges in mountainous canyons, wind resistance has become a critical design consideration. Accurately characterizing the complex, terrain-specific wind characteristics—through probabilistic modeling—is essential. However, conventional parametric distribution models often fail to capture the full complexity of wind behavior in such environments. This study addresses this challenge by analyzing one year of high-resolution wind speed and direction measurements collected at a representative mountainous site. We employ an adaptive bandwidth-optimized Gaussian kernel density estimation (KDE) method to construct marginal distribution models for wind characteristics—bypassing restrictive parametric assumptions and effectively capturing multimodal and asymmetric features inherent in the observed data. Building upon these nonparametric marginals, we further apply Copula theory to model the dependence structure between wind speed and direction, enabling a flexible and decoupled representation of their joint probabilistic behavior. This approach accurately captures the nonlinear interdependence between the two variables, which is often overlooked in traditional bivariate analyses. The key findings are as follows: (1) Wind fields in complex mountainous terrain exhibit pronounced directional confinement and “wind-locking” phenomena, with prevailing wind directions strongly aligned with local topographic orientation (e.g., valley axis). (2) The Gaussian KDE method demonstrates superior capability in representing the multimodal and skewed nature of wind data in such environments, outperforming conventional parametric fits. (3) Copula-based modeling effectively characterizes the nonlinear dependence between wind speed and direction, offering a robust and versatile framework for joint distribution modeling in complex terrain.

Dwelling thermal comfortability varies with adaptive behaviour of community within similar climatic zone - prerequisite for consideration in residential building design.

As electronics proliferate through the rise of the internet of things (IoT) and artificial intelligence (AI), the need for sustainable, decentralized power source is growing. Energy harvesting-converting ambient sources such as vibration, heat, or electromagnetic waves into electricity-offers a promising solution for powering distributed, low-power, or wearable electronic systems. However, the practical deployment of most energy harvesters has been significantly limited by the processability issues associated with the inherent brittleness of conventional materials. In contrast, fiber-based energy harvesters offer superior flexibility and stretchability due to their intrinsic deformability and multidirectional bending capabilities, presenting a compelling alternative to conventional energy harvesters. This review systematically summarizes the fabrication processes and performance characteristics of fiber energy harvesters, categorizing them by the origin of the energy source-mechanical, optical, and thermal. In particular, various design considerations based on the working principles of fiber energy harvesters are retrospectively analyzed to provide guidelines for developing next-generation fiber energy harvesters. Additionally, current challenges and future research directions are discussed, highlighting the potential of fiber-based platforms to enable next-generation wearable electronics.

This study proposes a systematic approach for implementing discrete-time Linear Active Disturbance Rejection Control in the closed-loop regulation of power converters. The continuous-time Linear Extended State Observer was discretized using the zero-order hold method to obtain a current estimator-based Linear Extended State Observer that is suitable for real-time implementation. The design considerations for discrete-time Linear Active Disturbance Rejection Control, including the selection of observer and controller parameters and the sampling period, are addressed. For performance comparison, a PI controller was designed and implemented in discrete time. The control schemes were evaluated via MATLAB/Simulink (2025b) simulations and real-time closed-loop experiments on a microcontroller to assess the transient response, disturbance rejection capability, and steady-state accuracy of the buck converter. The simulation and experimental results demonstrate that the discrete-time Linear Active Disturbance Rejection Control incorporating a current-estimator-based Linear Extended State Observer significantly outperforms the PI controller in terms of transient response and disturbance rejection capability. From this perspective, this study provides a meaningful contribution to the limited literature on linear extended state observer-based discrete-time Active Disturbance Rejection Control methods.

Abstract We systematically investigated terahertz photoconductive antennas on GaAs incorporating coplanar striplines of varying widths and two types of contact metallizations (AuGe/Ni/Au and standalone Ti). Measurements of the emitted terahertz power show that AuGe contacts yield stronger emission under low-bias conditions, whereas Ti contacts - initially constrained by Schottky barriers - exhibit superior performance at high bias due to barrier lowering. Terahertz time-domain spectroscopy further confirms the expected classical antenna behavior: decreasing the stripline width shifts the resonance frequency to higher values. At the shortest dipole lengths, however, the emitted spectra of the two metallizations diverge, with Ti-metalized antennas exhibiting higher resonance frequencies. These findings demonstrate that the performance of terahertz photoconductive antennas is constrained by the impedance of metallic contacts, providing essential design considerations for next-generation devices intended to operate at higher terahertz frequencies.

Abstract Social interaction is widely recognised as a critical component of effective learning. However, as online learning becomes increasingly embedded within higher education, digital environments continue to face persistent difficulties in fostering meaningful social learning experiences. With more institutions than ever adopting online delivery, it is imperative to systematically investigate these challenges to inform pedagogical strategies that can better support collaboration and engagement. This article presents a systematic literature review of 36 recent studies examining the challenges faced by learners in online social learning contexts. The findings of the review were reported in accordance with PRISMA guidelines and organised and interpreted through the Community of Inquiry framework. The analysis demonstrates that the reported challenges are not isolated to a single dimension of the framework but instead exert a combined influence on cognitive presence, social presence, and teaching presence, underscoring the interdependent nature of these constructs in online learning. To extend this interpretation, we employed the activity‐centred analysis and design framework to examine how learning designs can constrain social interaction and collaborative inquiry. Additionally, the review highlights opportunities for improving learning design practices and identifies the potential of artificial intelligence in addressing barriers to online social learning. Drawing connections between challenges, design considerations, and emerging technological interventions, this study contributes to universal discussions on equity, inclusion, and collaboration, offering insights that are globally relevant to the design of a socially connected digital landscape of higher education. Context and implications Rationale for this study: This study addresses a gap by examining barriers to social learning in online higher education and how they stem from learning design. Why the new findings matter: Findings show these challenges are systemic and interconnected. Integrating CoI and ACAD provides a deeper explanation of their causes and informs more effective design interventions. Implications for practitioners, policy makers, researchers: The findings highlight the need for more intentional learning design to support meaningful social interaction in online environments. For practitioners, this means structuring collaborative tasks, roles, and facilitation strategies that promote engagement and psychological safety. For institutions and policymakers, the study underscores the importance of investing in staff capability, appropriate technologies, and inclusive design practices. For researchers, it provides a foundation for exploring design‐based and AI‐supported interventions to strengthen social learning. Overall, the study emphasises that improving tools alone is insufficient; aligning tasks, technologies, and social structures is essential for fostering connected, engaging, and equitable online learning experiences.

ABSTRACT This scoping review examines the design and effectiveness of AI coaching chatbots in light of recent advances in generative artificial intelligence. Following the emergence of large language models in 2022–2023, AI coaching has gained traction as a scalable and cost-effective intervention, yet evidence guiding effective design remains limited. Using a five-stage PRISMA-ScR methodology, 17 empirical studies were analysed through thematic synthesis. Three overarching themes emerged: chatbot design considerations, determinants of adoption, and the operationalisation of AI coaching. Findings indicate that generative AI enhances interaction quality, usability, and engagement, particularly for structured tasks such as goal setting, reflection, feedback, and intersessional support. However, limitations in relational depth, cultural sensitivity, and psychological nuance persist, positioning AI coaches as complementary rather than substitutive to human coaching.

Hydrogels restrict protein transport to different extents, with nanoporous synthetic polymer networks providing far less protein permeability compared to microporous biopolymer networks. To evaluate whether reduced permeability was a driving factor in reduced cell viability in synthetic hydrogels, we compared poly(ethylene glycol) vinyl sulfone (PEG-VS) hydrogels with Matrigel to quantify the influences of modulus, transport, and confinement on encapsulated cells. We observed extensive reductions in cell viability when encapsulated in PEG-VS gels compared to Matrigel. In transwell experiments that decouple hydrogel-restricted serum from cell-gel adhesion, serum restriction reduced cell viability, matching the cell viability observed in 3D cultures. Our unique combination of 2D and 3D hydrogel-based cell cultures provides a framework for investigating the intersecting effects of the cell microenvironment's properties on cell viability. This work demonstrates that biomaterial-restricted protein transport is a critical design consideration when using synthetic 3D cell culture hydrogels.

Student housing is a critical component of the higher education experience, influencing academic performance, mental well-being, and social integration. While factors like cost and location are often prioritized, the intrinsic spatial configuration and patterns of these dwellings play a fundamental, yet sometimes overlooked, role in determining resident satisfaction. This paper presents a systematic literature review synthesizing empirical research from the past two decades (2000-2025) to elucidate the relationship between specific spatial patterns and student housing satisfaction. Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, 36 peer-reviewed studies were identified and analyzed. Thematic analysis reveals that satisfaction is predominantly mediated through three overarching spatial dimensions: (1) Privacy and Personal Control, highly influenced by dwelling unit typology (corridor vs. suite/apartment), room occupancy (single vs. shared), and the provision of territorially defined, hierarchical spaces; (2) Social Interaction and Community Formation, facilitated by spatial patterns that balance shared communal areas with private retreats, incorporating design elements that encourage passive and active social encounters; and (3) Physical Environment and Functionality, encompassing aspects of layout efficiency, natural light, noise control (directly related to spatial adjacency and buffer zones), and furniture arrangement. Findings indicate that satisfaction is not a product of any single pattern but emerges from a complex negotiation, often involving trade-offs (e.g., social opportunity vs. auditory privacy). Modern trends like the commodification of student housing and post-COVID-19 design considerations are also examined. This review concludes that student housing satisfaction is profoundly spatial in nature and provides an evidence-based framework for architects, university planners, and policymakers to create more supportive and satisfactory living environments.

Reliable and real-time environmental monitoring is essential for controlling pollution and protecting public health. However, conventional station-based measurements are expensive and often lack spatial and temporal resolution. This paper proposes a low-cost multimodal environmental monitoring system. Experiments verified that thin-film thermocouples exhibit near-linear voltage–temperature characteristics (R2&gt;0.99). Integration of the AI data pipeline substantially enhances monitoring accuracy: the proposed fusion strategy reduces relative error to approximately 2.3% under typical noise conditions, with a correlation coefficient of 0.79 between predicted and observed PM2.5 values. This research provides a scalable blueprint for edge-deployable environmental monitoring. A thin-film thermocouple with a fast response time is used as a temperature sensor and is statically calibrated against a K-type reference. To improve dynamic tracking and reduce measurement noise, a Kalman filter-based fusion strategy is employed, which is then compared with weighted averaging and Bayesian fusion. Simulation-driven validation is performed for thermocouple linearity, PID-based temperature control, micro-signal filtering and system-level latency and robustness. The results demonstrate that thin-film thermocouples exhibit near-linear voltage–temperature characteristics (R2 &gt; 0.99) with Seebeck coefficients ranging from 40.92 to 42.08 μV/°C, close to the theoretical K-type value of 42.87 μV/°C. The proposed fusion strategy reduces relative error to ~2.3% under typical noise conditions, enabling stable, real-time processing with near-second latency for 10,000-point batches. This study summarizes the design considerations for selecting and calibrating sensors and for achieving AI robustness in the presence of drift and faults. It provides a scalable blueprint for edge-deployable environmental monitoring.

Spaceborne synthetic aperture radar (SAR) absolute radiometric calibration relies on point targets with a known radar cross-section (RCS), such as triangular trihedral corner reflectors (TTCRs). Traditionally, radiometric calibration using TTCRs requires precise alignment of the corner reflector (CR) boresight to the radar line-of-sight (LOS), leading to frequent field operations and high labor dependency. In this study, a novel compact bidirectional trapezoidal CR is proposed to eliminate such alignment reorientations. The novel CR adopts three design considerations: a scalene shape to optimize the boresight elevation angle and enhance the peak RCS; a bidirectional configuration with azimuth fine-tuning to align with the radar LOS for both ascending and descending passes; and trapezoidal plate trimming to reduce the volume and weight without sacrificing RCS performance. An in-orbit validation is conducted in Xi’an, China, using the SuperView Neo 2-03 satellite. The results demonstrate that the imaging quality of the bidirectional trapezoidal CRs is comparable to that of conventional TTCRs, with all the parameters meeting system specifications. The radiometric calibration constant of the bidirectional trapezoidal CR differs from that of the conventional TTCR by no more than 0.27 dB, with a total uncertainty of ~0.33 dB (1σ)—demonstrating that it achieves equivalent radiometric calibration accuracy to TTCRs. The experiment confirms the feasibility and engineering applicability of the bidirectional trapezoidal CR for X-band SAR radiometric calibration.

The evolution toward 6G wireless networks necessitates innovative solutions to support the massive Internet of Things (IoT) deployments with unprecedented computational and communication requirements, motivating this A comprehensive framework that integrates Reconfigurable Intelligent Surface (RIS) technology with hierarchical aerial computing networks by combining RIS-equipped Unmanned Aerial Vehicles (UAVs) operating as mobile edge computing nodes with High-Altitude Platforms (HAPs) to create a three-tier computing hierarchy addressing the limitations of conventional terrestrial infrastructure. The system model encompasses RIS-equipped UAVs serving terrestrial IoT devices with a single HAP providing high-capacity computational resources, where the RIS phase optimization is formulated as a Riemannian conjugate gradient problem on complex circle manifolds to maximize total system throughput while naturally handling unit modulus constraints through a three-stage sequential decomposition approach. Extensive Monte Carlo simulations demonstrate significant performance improvements over the comparable algorithm without RIS enhancement, with the RIS-enhanced system achieving 18% throughput improvement, near-linear scalability serving approximately 100% of available IoT devices compared to the algorithm In the comparable algorithm at 100 devices, a 95% task completion rate was maintained across all network loads versus 79-80% for the algorithm compared to the comparable algorithm. The results validate the potential of RIS-enabled aerial networks as a transformative solution for scalable and efficient 6G IoT services, with enhanced channel quality from intelligent phase configuration, enabling superior resource utilization and service provisioning in hierarchical computing architectures, establishing key contributions including novel RIS-aerial computing integration, advanced Riemannian manifold optimization with superior convergence properties, unified resource allocation combining stable matching theory with externality elimination, comprehensive performance analysis demonstrating practical viability, and real-world implementation considerations for future multi-UAV scenarios and energy-efficient designs.