Search Results (376)

The building sector is a major contributor to global energy consumption and greenhouse gas emissions, and multi-family residential buildings play an important role in urban decarbonization and the transition toward sustainable cities and societies. This study proposes decarbonization-oriented case studies for selecting heating, ventilation, and domestic hot water systems by integrating environmental, economic, and social criteria aligned with the Sustainable Development Goals (SDGs), particularly SDG 7 and SDG 11. This research compares selected conventional and low-carbon building-level heating, ventilation, and domestic hot water systems, including gas boilers and heat pumps integrated with renewable energy and heat recovery. The evaluation is based on a calculation-based energy performance assessment using a quasi-static monthly heat balance approach, economic indicator analysis, and environmental assessment based on primary, final, and useful energy demand and CO₂ emissions. Cooling energy demand was not included in the assessment because the analyzed scenarios were limited to heating, ventilation, and domestic hot water preparation. Furthermore, the social implications are examined, considering energy affordability, long-term operating costs, and the potential to mitigate energy poverty. The results indicate that low-carbon HVAC systems, particularly heat pump systems integrated with renewable energy sources, significantly reduce CO₂ emissions and primary energy consumption compared to conventional solutions. Although they require a higher initial investment, they can achieve lower life cycle costs over the building’s lifetime. The study concludes that holistic, decarbonization-oriented technologies can support cost-effective, socially responsible pathways toward low-carbon, energy-efficient multi-family residential buildings and sustainable urban development. Full article

Decarbonizing the building sector requires integrating on-site renewable generation with systematic energy management. Among the most widely adopted alternatives are photovoltaic (PV) systems in buildings; however, they are often implemented as a standalone technological intervention (size–install–estimate savings), without being formally incorporated into an Energy Management System (EnMS) aimed at continuous improvement. In this context, this research addresses this gap through an integrated methodological framework aligned with ISO 50001, in which PV is explicitly included in energy performance management through energy review, the definition of an Energy Baseline (EnB), and the monitoring of Energy Performance Indicators (EnPIs) within the PDCA cycle. The approach articulates the analytical sizing of the PV system based on electricity demand and solar resources; its validation through simulation to ensure operational consistency and a technical, economic, and environmental assessment that translates PV generation into a verifiable reduction in energy imported from the grid and, consequently, into traceable improvements in EnPI under an audit-compatible scheme. The methodology is demonstrated in a multi-family building in Chorrillos, Lima (Peru), where a 14.5 kWp rooftop PV system (25 modules of 580 Wp) is designed to maximize self-consumption during daylight hours. The results show technical performance consistent with the demand profile, economic viability under the conditions of the case, and environmental benefits from replacing grid electricity, along with offsets associated mainly with the manufacture of PV components. The residual gap between the Post-PV EnPIs and the ISO 50001 target confirms that PV integration is a necessary but not sufficient first-cycle action within a comprehensive building decarbonization strategy, with demand-side management and envelope improvements identified as subsequent PDCA cycle priorities. In summary, the central contribution is not the PV sizing itself, but its operational and traceable integration within ISO 50001, making PV a quantifiable, verifiable, and scalable energy improvement action for residential buildings in emerging economies. Full article

NanoArduSiPM represents a paradigm shift in the ArduSiPM (Architected Detection Unit for Silicon Photomultipliers) roadmap, evolving from a standalone instrument into a high-density modular building block (36 mm × 42 mm × 3 mm, 7 g). This revision does not merely pursue miniaturization; it re-engineers the signal-processing chain to maintain high performance within a scaled-down footprint, enabling the transition from single-unit detection to scalable, distributed multi-detector systems. NanoArduSiPM is based on a three-layer architecture comprising an external scintillator and Silicon Photomultiplier (SiPM) detection module, a dedicated high-speed discrete analog front-end, and a System-on-Chip (SoC) for embedded acquisition and processing. The physical implementation adopts high-integrity PCB routing and rigorous isolation techniques designed to suppress digital–analog coupling, a critical requirement in such a compact form factor. This deterministic layout strategy provides the architectural foundation for time-tagging capabilities, currently under quantitative characterization, by addressing the fundamental sources of signal interference at the hardware level. Beyond hardware integration, NanoArduSiPM introduces the capability for extended firmware functionality, including event tagging via external inputs and the implementation of coincidence and veto logic. This framework supports the acquisition of multiple correlated histograms and allows multiple units to be interconnected on a shared SPI bus. By shifting from standalone operation to a coordinated, hierarchical architecture, NanoArduSiPM enables distributed detection schemes where event selection and correlation are handled natively within the system, reducing the dependency on external data acquisition electronics. The compact modular architecture, together with the high-performance discrete analog front-end and embedded data handling, makes NanoArduSiPM suitable for applications where low mass and low power consumption are critical, targeting applications such as space-based payloads, laboratory instrumentation, remote sensing, and large-scale distributed multi-channel detection systems. While no radiation-tolerance qualification of the complete system has been performed in this work, the microcontroller family used in the design is also available in radiation-tolerant variants, which may support future implementations targeting more demanding radiation environments. Full article

Thermal modernization of existing buildings is an important part of sustainability-oriented retrofit practice because it can reduce operational energy demand, but its environmental effect depends partly on the insulation material selected and on the thermal assumptions used in design. This study examines how the use of declared thermal conductivity (λ_decl) and design conductivity (λ_design) affects the required insulation thickness and the A1–A3 global warming potential (GWP) of alternative insulation materials for an attic ceiling separating heated space from an unheated ventilated attic in a multi-family building. This study supports product-stage sustainability assessment; it does not constitute a comparison of the full life cycle climate effect of the selected material groups. The thickness needed to achieve U_target = 0.15 W/(m²·K) was determined for scenarios based on λ_decl, temperature-corrected λ_design, and a moisture sensitivity analysis for cellulose. Environmental assessment was based on European EN 15804+A2-compliant EPDs, with separate reporting of GWP_fossil and GWP_biogenic. In the analyzed case, differences between material groups were driven mainly by EPD data, whereas conversion from declared to design thermal properties had a smaller, but not negligible, effect. This effect became more important for moisture-sensitive materials. The results show that sustainability-oriented environmental comparisons based only on declared thermal conductivity may be misleading when functionally equivalent solutions are compared. In the analyzed case, the transition from λ_decl to temperature-corrected λ_design resulted in only a small change in the required insulation thickness and the corresponding GWP result. At the same time, the scenario-based sensitivity analysis for cellulose insulation and the variability of data reported in the EPDs indicate that moisture-related assumptions and the selection of input data may be of greater importance. The results show that, when interpreting the environmental performance of insulation solutions in sustainable retrofit design, consistency should be maintained between the adopted functional unit and the method used to define the thermal properties of the material after installation in the building envelope. Full article

Participatory housing design is widely associated with social sustainability because it can support community-building, strengthen user acceptance, and foster long-term place attachment. At the same time, participatory processes are organisationally demanding, as they involve multiple stakeholder groups, frequent design iterations, and the need to communicate spatial and technical implications to non-professional participants. While recent research has examined BIM in housing, collaboration, and digital participation, fewer studies begin with empirically documented workflow bottlenecks in non-BIM participatory housing projects and translate them into actionable BIM-supported strategies. This study addresses this gap by examining (1) recurring process-related challenges in participatory housing design conducted without BIM-based workflows, (2) BIM-supported workflows that could realistically mitigate these challenges, and (3) the implications for socially sustainable practice. A qualitative research design is applied to two participatory multi-family housing projects in Vienna, Austria. The cases were reconstructed from earlier semi-structured interviews with project architects and complemented by a follow-up structured questionnaire to validate key process aspects. Two independent BIM experts then interpreted the empirically identified challenges and proposed BIM-based responses. The results indicate that the most persistent difficulties are procedural rather than formal, centring on iteration and variant management, decision traceability, communication with lay participants, and coordination under time pressure. Expert interpretations suggest that BIM can strengthen participatory workflows through CDE-based information governance, structured issue and decision tracking, curated option management, and improved visual communication, while also introducing constraints related to costs, training, interoperability, organisational readiness, and potential cognitive overload. Overall, the paper positions BIM as a socio-technical infrastructure that can enhance procedural justice and transparency when embedded within carefully moderated participatory workflows. Full article

This study investigates the impact of the Urban Heat Island (UHI) effect on building energy performance and the optimization of passive design strategies in six Moroccan climate zones: Agadir, Tangier, Fez, Ifrane, Marrakech, and Errachidia. A computer simulation approach combined with multi-objective optimization using the NSGA-II algorithm was employed to improve energy efficiency while maintaining thermal comfort for a single-family house. The optimum solutions include several passive design parameters, such as insulation materials and thickness, glazing types, window-to-wall ratio (WWR), ventilation rates, shading devices, building orientation, and heating and cooling set point temperatures. The analysis was studied under both standard climate data and UHI scenarios to evaluate the impact of increased urban temperatures on building performance. The results show that under standard climate conditions, the optimal design can achieve up to 76% energy savings throughout all the climate zones, while Marrakech can save 67% and Errachidia 64%; however, under UHI scenarios, these energy savings dropped by 8–30% depending on the climate zone. For example, Agadir drops from 76% to 49% under a 5°C UHI scenario, and Marrakech drops from 67% to 56% under a 3.5 °C UHI scenario, highlighting the significant impact of urban overheating on buildings. These findings emphasize that integrating the UHI effect is essential for accurately assessing passive design performance and for ensuring that selected design solutions truly minimize energy consumption under realistic urban conditions, while also underscoring the importance of integrating passive design strategies into residential buildings. These strategies promote sustainable building practices in Morocco by reducing energy consumption and improving occupant thermal comfort. Full article

Tropical forests, despite their critical environmental and socio-economic roles, remain highly vulnerable to deforestation, forest degradation, and climate-related disturbances. There is a growing demand for robust and transparent forest monitoring systems, particularly under REDD+, the Paris Agreement’s Enhanced Transparency Framework (ETF), and emerging climate-finance mechanisms. Conventional approaches based on field inventories and traditional remote sensing are often constrained by limited or uneven field data, persistent cloud cover, complex forest conditions, and limited institutional and technical capacity. This review examines how artificial intelligence (AI) and machine learning (ML) are being integrated into remote sensing–based tropical forest monitoring to address these structural constraints. Using a semi-systematic synthesis of peer-reviewed studies, complemented by operational platforms and grey literature, the review assesses AI/ML approaches, remote sensing datasets, and applications relevant to national and large-scale monitoring. Evidence is synthesized across five analytical dimensions: AI/ML model families and workflows, multi-sensor datasets and training resources, operational monitoring platforms, application domains (including deforestation, degradation, and biomass/carbon estimation), and cross-cutting technical, institutional, and governance barriers. The review finds that AI/ML-enabled remote sensing, particularly those combining optical, radar, and LiDAR time series within cloud-based platforms, has substantially improved the automation, scalability, and speed of tropical forest monitoring. However, effective and equitable adoption remains constrained by limitations in training and validation data, dependence on proprietary platforms and data, uneven technical capacity, and unresolved governance and ethical challenges. Emerging solutions, including open and representative training datasets, platform-agnostic processing infrastructures, long-term capacity building, and inclusive data-governance frameworks, are identified as critical enablers of credible and nationally owned AI/ML-enabled forest-monitoring systems. The review highlights that AI/ML can play a transformative role in supporting climate mitigation, biodiversity conservation, and informed decision-making. This potential, however, depends on transparent data governance arrangements, long-term capacity building, and platform-agnostic infrastructures that support national ownership. Full article

Promoting the effective use of abandoned farmland has become a key policy priority for strengthening food security in China. However, disentangling the decision-making processes among diverse participating actors is a foundational prerequisite for addressing the governance challenge of abandoned farmland utilization. Building on this, the present study employs a four-actor evolutionary game model and sensitivity analysis of key parameters to systematically examine the interactions among four key actors—local governments, village collectives, family farms, and farmers—and to identify the corresponding evolutionarily stable strategies (ESSs) across different stages of abandoned farmland utilization. The results show that: (1) Multi-actor strategic interactions in abandoned farmland utilization exhibit a multi-stage evolutionary trajectory, in which all actors gradually shift their strategic choices under changing cost–benefit structures, regulatory intensity, and coordination conditions, leading to different evolutionary stable equilibria across governance stages. (2) The configuration in which local governments adopt loose regulation, the village collective plays an active coordinating role, family farms pursue long-term operations, and farmers choose recultivation is a key condition for achieving a Pareto-optimal equilibrium. (3) Although farmers’ production willingness and behavioral choices form the basis for the utilization of abandoned farmland, spontaneous individual action alone is insufficient to address the structural contradictions currently facing abandoned farmland utilization in China. To effectively promote the evolution of abandoned farmland governance toward a stable collaborative equilibrium and ultimately realize sustainable utilization, it is necessary to further optimize governmental administrative control models and incentive mechanisms, strengthen the organizational and coordinating functions of village collectives, and improve long-term operational support systems for family farms. This study systematically elucidates the underlying logic of China’s abandoned farmland utilization from the perspective of multi-actor behavioral decision-making, providing policy-referential insights for optimizing policy design, reducing coordination costs, and improving the efficiency of abandoned farmland utilization. Full article

In contemporary multi-family housing construction, the structural grid is often influenced or conditionally determined by the dimensional logic of underground parking garages. When transferred to above-ground storeys, it directly defines façade frontage, building depth, and possibilities for apartment organisation. Previous research has mostly examined housing typology, dimensional standards, and structural systems as separate domains, while the influence of parking-derived structural grids has not been systematically analysed within a unified framework. This paper applies an analytical-comparative approach, comparing typical structural grids derived from parking modules with the minimum façade frontages required for different apartment types. The method includes identifying characteristic grid dimensions, defining minimum façade frontages based on normatively prescribed room widths, calculating deviations between required and available dimensions, and analysing individual and combined apartment units according to the criterion of minimal positive deviation, within the Serbian regulatory framework. The results show that the structural grid is a relevant factor in apartment organisation and typological structure. Certain grids enable more rational layouts with minimal spatial adjustments, while others generate dimensional surplus, excessive depth, or typological constraints. The study establishes a link between parking modules, structural grids, and apartment organisation, and proposes an analytical framework for evaluating their dimensional compatibility in multi-family housing design. Full article

This paper develops a rigorous inferential framework for a class of Gaussian stochastic processes driven by white noise with constant drift, whose temporal evolution is governed by a Caputo fractional derivative of order ${\displaystyle \alpha \in (1/2,1)}$. The model belongs to the family of fractional Volterra processes, where memory is generated by the dynamics themselves rather than by correlated noise. We derive explicit analytical expressions for the mean, variance, and covariance structure of the solution, thereby characterizing in a precise manner how the fractional order ${\displaystyle \alpha }$ governs both variance growth and the strength of temporal dependence. In particular, the process exhibits correlated increments and a power-law variance scaling of order ${\displaystyle t^{2\alpha -1}}$, highlighting the dual role of ${\displaystyle \alpha }$ as a regularity and memory parameter. Building on this structural analysis, we address the statistical problem of estimating the parameter vector ${\displaystyle (\mu ,\sigma ,\alpha )}$ from discrete-time observations. Two complementary procedures are proposed for the estimation of the fractional order: a variance-growth method based on log–log regression of empirical variances, and a wavelet-based estimator exploiting multi-scale scaling properties of the process. For the drift and diffusion parameters ${\displaystyle (\mu ,\sigma )}$, we construct explicit Gaussian pseudo-maximum likelihood estimators derived from the Volterra covariance structure of the increment process. We establish unbiasedness, ${\displaystyle L^2}$-convergence, strong consistency, and asymptotic normality for all estimators. Furthermore, we derive Berry–Esseen type bounds that quantify the rate of convergence toward the Gaussian law, providing sharp distributional approximations in a genuinely fractional and non-Markovian setting. A Monte Carlo study is carried out, using high-resolution Volterra discretizations, large-scale simulation budgets, covariance-structured linear algebra, and multi-scale diagnostic tools. The numerical experiments confirm the theoretical convergence rates, demonstrate the finite-sample reliability of the estimators, and illustrate the sensitivity of the process dynamics to the fractional order ${\displaystyle \alpha }$: smaller values of ${\displaystyle \alpha }$ produce stronger memory effects and higher variability, while values closer to one lead to smoother and more stable trajectories. The proposed methodology unifies statistical inference for long-memory Gaussian processes with fractional differential stochastic dynamics, offering a coherent analytical and computational framework applicable in areas such as quantitative finance, anomalous diffusion in physics, hydrology, and engineering systems with hereditary effects. Full article

Increasingly shaped by heterogeneous on-chain activity rather than a single shared market process, this study investigates 7-day-ahead forecasting using 147 market and on-chain indicators across eight major blockchain ecosystems from October 2023 to April 2025. We benchmark statistical, deep-learning, and foundation-model baselines under multiple feature-selection pipelines using both error metrics and Diebold–Mariano tests. TiRex achieves the best average MAPE ($0.0428$) in a univariate setting without additional optimized covariates, while TFT remains slightly weaker even under its best feature-input configuration (MAPE: $0.0435$; $p=0.9359$ versus TiRex), suggesting a persistent practical advantage for TiRex. Importantly, TiRex’s zero-shot nature confers a substantial efficiency edge: by bypassing feature selection, it delivers comparable accuracy at a fraction of the computational cost. At the same time, feature selection materially affects many model families, with Boruta chosen in roughly 71.7% of best configurations. Taken together, the evidence supports a selective-feature principle: robust forecasting depends on validated, chain-specific features rather than larger feature sets. Feature-importance and overlap analyses further indicate a mixed structure of transferability, where broad market proxies provide baseline context while chain-specific variables drive marginal gains. Overall, this study highlights that effective multi-chain forecasting is primarily a feature selection problem under statistical uncertainty, while also showing that zero-shot designs like TiRex can achieve state-of-the-art accuracy with unmatched efficiency, offering practical implications for building leaner, more robust trading systems. Full article

Earthquakes remain one of the greatest threats to urban resilience, demanding innovative strategies that go beyond traditional earthquake-resistant design. Among emerging solutions, viscous fluid dampers stand out as one of the most effective mechanisms for controlling structural responses and reducing damage. This research analyzes the seismic performance of a 12-story multifamily building equipped with viscous fluid dampers, developed using a comprehensive Building Information Modeling (BIM) methodology. The architectural model was integrated into a BIM environment, ensuring precision, coordination, and digital consistency. A time-history analysis was conducted in ETABS comparing two configurations—with and without dampers—subjected to seismic records from Lima-Perú, Ica-Perú, and Tarapacá-Chile. The results show that incorporating dampers significantly improves structural behavior, reducing maximum displacements by 52.25% and inter-story drifts by 47.37%. These findings confirm the ability of dampers to effectively dissipate seismic energy. Likewise, BIM integration establishes a robust digital framework for sustainable, coordinated, and resilient seismic design in high-rise buildings. Full article

Air-to-water heat pumps (ASHPs) are a key technology for residential heating decarbonization; however, their seasonal performance is highly sensitive to outdoor temperature variability. Although thermal energy storage (TES) is widely recognized as a means of improving system efficiency, reported performance gains vary due to differences in climatic datasets, control strategies, and modeling assumptions. This study presents a systematic multi-year assessment of the impact of a water-based TES tank on the seasonal performance of a residential ASHP under measured microclimatic conditions. Hourly simulations were conducted for a single-family house at three locations in eastern Croatia using eight years (2018–2025) of measured meteorological data. Building characteristics, system configuration, and operating strategy were kept identical to isolate the influence of storage volume. TES integration reduced annual electricity consumption by 4.8–9.1%, with a multi-year average reduction of 7.02%, and consistently increased the seasonal coefficient of performance (SCOP) across all analyzed years and locations. The highest relative improvements occurred under less favorable microclimatic conditions, emphasizing the importance of diurnal temperature distribution rather than seasonal averages alone. A parametric analysis identified an optimal storage volume of approximately 1000–1500 L when both energy and economic indicators are considered. The results demonstrate that stable and reproducible seasonal efficiency gains can be achieved through a simple, non-predictive operating strategy under continental climatic variability. Full article

Anxiety and depression are among the most prevalent mental health conditions worldwide. Early and accurate automated detection from naturalistic conversations (e.g., those recorded with a remote chatbot) could eventually improve screening and, in turn, access to timely care. As a first step towards this goal, we aim to evaluate the efficacy of both traditional machine learning and large language models (LLMs) in classifying anxiety and depression from psychotherapy sessions using labels derived from clinician-annotated session metadata reflecting the primary presenting psychiatric concerns. While psychotherapy transcripts do not reflect the real-world domain of remote naturalistic conversation, we conduct this analysis as an “easy” starting point towards the eventual goal of building generalizable, clinician-assistive models that can infer mental health status from unstructured, non-directive conversations captured in the home setting as part of a remote digital assessment process. LLM underperformance on a psychotherapy benchmark would indicate that LLMs are most likely not yet ready to advance towards mental health classifications in more complex and less structured contexts, such as from remote conversations with a chatbot or family member. To study whether LLMs can classify anxiety and depression from psychotherapy transcripts, we fine-tuned both established transformer models (BERT, RoBERTa, Longformer) and more recent large models (Mistral-7B), trained a Support Vector Machine using engineered features, and assessed prompting GPT chatbots. We observe that (1) all machine learning approaches perform poorly and (2) state-of-the-art models fail to improve multi-label classification performance relative to traditional machine learning methods, indicating the current limitations of using LLMs for classification of psychiatric diagnoses from unstructured patient text as of 2026. Full article

HM Inspectorate of Probation is committed to building and utilising the evidence base for high-quality youth justice services, and to promoting excellence and having a positive impact upon those inspected and the wider sector. Research evidence and inspection findings are used to inform understanding of what helps and what hinders services and to consider system-wide change. In this article, the latest inspection and research findings in relation to the high-profile areas of serious youth violence and criminal exploitation are highlighted. The article encompasses insights from core and thematic inspections, including those from recent joint targeted area inspections (JTAIs) undertaken with other inspectorates. Alongside the JTAIs which examined multi-agency responses to serious youth violence, research was commissioned to hear directly from children and families about their experiences. Other research commissioned and published by the Inspectorate has emphasised the importance of implementing relational, child-centred and trauma-informed approaches and to optimising collaborative/partnership working across agencies and sectors. Reports have also drawn attention to the value of paying attention to the socio-ecological framework, systemic resilience, adultification biases, and both contextual and transitional safeguarding. Full article