Search Results (4,696)

The 1960s to early 1990s marked a pivotal era in Kazakhstan’s mass housing history, particularly in Almaty, which was driven by a sector-wide policy shift toward apartment-based occupancy, regulated space standards, full utility provision, and organised residential courtyards. Large-panel buildings from this period still comprise over one-third of Almaty’s housing stock, yet their alignment with modern comfort standards remains understudied. This study establishes a historical periodisation of Almaty’s large-panel housing and assesses its comfort levels at apartment and built-environment scales. The two-part methodology combined a theoretical strand (comfort-level theory, literature review, historical retrospective, and periodisation) with an analytical strand (comparative graphical-analytical assessment of series 1Kz-464-AS, 1Kz-464-DS, 69, E-147 and 158), converging in a Basic–Supplementary–Advanced comfort classification. The results of the current study showed that three sub-periods emerged. Series 1Kz-464-AS and 1Kz-464-DS used a narrow-bay (2.6–3.2 m), economy-driven two-bay system with minimal zoning. Series 69 widened bays to 3.6–5.4 m, improving spatial comfort and utility provision. Series E-147 and 158 introduced a three-bay scheme with an added 2.1 m bay and raised ceilings (3.0 m), yielding the most flexible layouts. At the urban scale, microdistrict planning enlarged planning units by 7–10 times, preserved green connectivity through mid-rise buildings, and shielded courtyards using perimeter high-rises, supporting walkable, infrastructure-rich neighbourhoods. Apartment-scale comfort improved incrementally through bay widening rather than layout diversification, leaving persistent redevelopment constraints from panel structures. Built-environment comfort, however, reached a comparatively advanced, durable standard. Adaptive reuse should prioritise structural refurbishment, reduced occupancy density, courtyard–microdistrict continuity, and diversified public-space functions. This study’s results can inform renovation strategies for Soviet-era housing across post-Soviet cities. Full article

In April 2024, the Hellenic Cadastre became one of Europe’s first land registries to use a generative AI model (a large language model served through Azure OpenAI) for the legal review of property deeds. Unlike similar European initiatives using classical NLP, Greece applied state-of-the-art generative AI to a massive legacy issue: 390 historical mortgage registries holding an estimated 600 million to one billion paper pages. By April 2026, the system had processed 310,000 acts, reducing the average per-act review time from about thirty minutes to under ten; a very large per-act cost reduction is also reported by the implementation partner, which we treat as a vendor-stated figure. Additionally, the cadastre’s geodetic infrastructure found a second use following the 2023 Tempi rail disaster. In 2026, the Hellenic Positioning System (HEPOS), a 98-station GNSS reference network, began providing corrections for Greece’s real-time train tracking platform. While satellite-based train positioning is not novel in Europe, where consortia such as CLUG have run a decade of research and pilots, this marks its operational deployment in Greece. The Greek case is unique institutionally rather than technically: it repurposed a national CORS network for a citizen-facing train tracking platform as a short-term crisis response, alongside an incomplete ETCS rollout. This paper documents both deployments, measures their impact, maps them onto the nine FELA pathways, and identifies transferable practices. Greece is not presented as a technological frontier, but as an example of how a country can put existing geospatial infrastructure and AI to rapid use in delivering build-back-better policies for the public, in line with the UN 2030 Agenda. Full article

Lithium-ion batteries are widely used in various energy sectors, and accurately predicting their early remaining useful life (RUL) is crucial for shortening battery evaluation time and accelerating battery commercialization. However, information on degradation during the early cycling stages of batteries is limited, and it is difficult to fully characterize their lifespan. This study proposes a CNN–Transformer–BiGRU-based method for predicting the early RUL of lithium-ion batteries using Grey Wolf Optimization (GWO). First, using only the first 100 cycles of each battery in the MIT dataset, early degradation features are extracted from the dimensions of capacity and internal resistance, and then standardized. Second, a CNN is employed to extract local degradation features, while the Transformer’s self-attention mechanism is used to capture global correlations, and BiGRU is utilized to further extract bidirectional temporal dependency information. Building on this foundation, GWO is introduced to perform joint optimization of the model’s key hyperparameters to obtain optimal network parameters. Finally, the effectiveness of the proposed method is validated through ablation and comparison experiments. The experimental results show that the proposed model achieved an R² of 0.9633, with RMSE, MAE, and MAPE values of 80.5608 cycles, 63.2524 cycles, and 7.29%, respectively, demonstrating overall prediction performance superior to that of the comparison models. This method can effectively mine degradation information related to battery life from limited early-cycle data, providing an effective approach for the accurate prediction of the early RUL of lithium-ion batteries. Full article

This study examines the long-run relationship between terms of trade (TOT) and real fishing-sector GDP in a small open economy, focusing on the Peruvian case. Despite the strategic importance of fisheries for exports, employment and foreign exchange generation, the extent to which external price conditions are associated with fishing-sector performance remains insufficiently explored in sector-level research. Building on the notion that TOT summarise opportunities and constraints arising from the international environment, the paper evaluates whether persistent external conditions are linked to the long-run trajectory of the fishing sector. The analysis employs the Johansen cointegration approach and a bivariate Vector Error Correction Model (VECM) using quarterly data for the period 2001–2025. Seasonal effects are incorporated through quarterly dummy variables, while robustness is assessed by controlling for extreme El Niño–Southern Oscillation (ENSO) episodes and the COVID-19 pandemic. The results reveal the existence of a unique long-run equilibrium relationship between TOT and fishing-sector GDP. The error-correction mechanism indicates that deviations from equilibrium are actively corrected over time, whereas the adjustment coefficient for TOT is statistically insignificant. Robustness tests further show that El Niño episodes are negatively and significantly associated with short-run sectoral performance, while no statistically significant association is detected for La Niña. The COVID-19 control does not materially alter the long-run relationship identified by the model. The findings contribute sector-level evidence for a resource-dependent economy and suggest that long-run equilibrium and sectoral adjustment dynamics are important elements for understanding the long-run behaviour of the fishing sector. Full article

The growing environmental impact of the construction sector is driving a shift toward sustainable design practices, in which digital technologies are integrated to enable designers to make informed decisions from the early design stages. In this study, a DSS is developed that combines BIM, VPL and TPL to identify the optimal wall stratigraphy for the building envelope. The process is structured into sequential phases, in which Autodesk Revit v2026.06.24.01, Dynamo v.3.6.1 and Python v3.9 are integrated within an end-to-end workflow. In the first phase, wall stratigraphies are modelled in BIM, and parametric variations in layers are allowed alongside customisation of the material database. In the second phase, an automated workflow calculates a set of indicators covering thermal performance, environmental assessments (LCA, MRc2 LEED and mandatory national requirements), and economic evaluations (LCC). In the third phase, indicators are imported into an automated Dynamo-based MCDM, where a hybrid AHP/PROMETHEE analysis is applied and results are directly integrated into BIM, thereby supporting sustainability-focused decisions. The tool is validated on different sustainable wall stratigraphies in warm-climate contexts. The hybrid solution is ranked first, followed by rammed earth, while platform frame and X-LAM are ranked lower. Full article

Urban informality in amphibious territories represents a critical yet understudied dimension of contemporary urbanisation in the Global South. This article analyses the interrelations between spatial transformation, social equity, and environmental change in Las Flores, an informal settlement located between the Mallorquín Lagoon and the Magdalena River in Barranquilla, Colombia. Drawing on a mixed-methods approach combining GIS interpretation, participatory mapping, and in-depth interviews, the study examines how processes of informal territorialisation have reshaped both the physical landscape and the social fabric of this amphibious environment. Results indicate that the settlement has expanded progressively over the past two decades, occupying areas of the wetland previously covered by mangroves and natural vegetation. This expansion has been accompanied by environmental degradation, soil infilling, and declining water quality. Residents face persistent infrastructural deficits, limited access to education and healthcare, and increasing social fragmentation between the formal and informal sectors. Yet, the community also exhibits strong organisational capacity, adaptive livelihoods, and a deep sense of place that sustains local identity and resilience. These dynamics exemplify the paradox of amphibious life: coexistence with water as both a resource and a source of vulnerability. Building on these findings, the study develops an urban socio-ecological conceptualisation of Amphibious Justice, a framework for interpreting equity, recognition, and governance in hybrid territories where urbanisation and land–water dynamics intersect. The article proposes a framework of equitable amphibious urbanism that integrates environmental restoration, social inclusion, and participatory governance. The findings suggest that sustainability in such territories cannot be achieved through technocratic restoration or forced resettlement, but through co-produced strategies that recognise local knowledge, tenure security, and ecological stewardship. Ultimately, the case of Las Flores offers insights into how cities in the Global South can pursue just and adaptive coexistence with water amid growing climate and urban pressures. Full article

Lithium, as a Li-ion battery constituent, is pivotal for the transition to clean energy. Western Australia (WA) has become a global leader in hard-rock lithium mining, realising 10-fold growth from 2010 to 2024 and with royalty receipts to the WA government surpassing $1 billion AUD. Given the sector’s economic significance, we analyse key performance metrics including resource/reserve build, production growth, cumulative capital deployed, capital intensity, and development timelines for the new-generation lithium mines. Several enabling factors supported the rapid build-out of capacity. These include an efficient mine permitting process to manage environmental impacts and competing land use issues, a stable royalty regime, energy and logistics infrastructure, availability of a skilled workforce, and mining services capability. Contrary to the standard industry narrative that new mineral projects are constrained by legislative delay, the new lithium projects achieved development timelines of 7 years or less from first resource to production. This has broader implications for critical mineral projects where success is likely to depend less on strategic classification and more on project quality, financing, and regional capability. Full article

For national statistics offices (NSOs), data sharing is essential for the production of official statistics, yet it must comply with stringent confidentiality and governance mandates. Blockchain technology has emerged as a promising means to enable trusted and auditable data exchange under these constraints, motivating a growing body of research across public-sector domains. This paper presents an up-to-date survey of blockchain-based data-sharing solutions in government and NSO contexts, reviewing studies published between 2018 and 2026. Given the limited research specific to official statistics, this survey evaluates public-sector solutions with particular attention to their alignment with NSOs’ strict privacy and data governance requirements. Departing from prior reviews that primarily organise the literature by technical architectures or application domains, the survey adopts the Five Safes framework as a unifying analytical lens. Through this governance-focused synthesis, the survey identifies recurring architectural patterns and design principles that support secure and accountable data sharing, including permissioned or consortium blockchains, hybrid on/off-chain storage, selective integration of privacy-enhancing technologies, and smart contracts to automate governance functions such as access control and workflow management. Building on these insights, the paper outlines two distinct reference architectures that distil best practices from existing studies for blockchain-enabled data sharing under public-sector constraints, and highlights open challenges—most notably scalability and integration with legacy systems—pointing to future research directions towards solutions that combine strong security assurances with practical governance compliance. Full article

Artificial intelligence has transformed dynamic pricing by enabling firms to forecast demand more accurately, respond to market uncertainty, and optimize prices in real time. Existing reviews remain fragmented, typically focusing on a single industry or on isolated methodological streams like reinforcement learning or time-series forecasting. To address this gap, this study provides a comprehensive, cross-industry synthesis of AI-based Dynamic Pricing through a systematic bibliometric analysis of 1301 Scopus-indexed publications from January 2005 to August 2025. E-commerce and digital platforms serve as the study’s central analytical lens because they frequently combine real-time transactional data, rapid price adjustment, customer-level behavioral information, platform competition, and algorithmic repricing. The analysis also extends to other digitally mediated pricing environments, including energy, mobility, electric-vehicle charging, hospitality, transportation, and retail, allowing the study to examine how methods, adoption patterns, and governance concerns vary across sectors. Using VOSviewer and CiteSpace, the study maps the intellectual structure of the field and identifies eight major research clusters. The findings reveal a clear methodological shift from rule-based and econometric approaches toward deep learning, multi-agent reinforcement learning, and simulation-driven decision systems. They also show that data-intensive and platform-mediated sectors are becoming increasingly prominent in the development and application of advanced AI-based pricing methods, while established revenue-management domains such as airlines and hospitality remain important foundations of the field. Building on these patterns, the study outlines future research opportunities centered on interpretable and uncertainty-aware pricing models, ethical and fair pricing mechanisms, and cross-industry transfer of methods and regulatory practices. This synthesis provides a structured foundation for advancing theory, methodology, and practice in AI-based DP. Full article

Risk assessment is critical for managing health threats at the human–animal–environment interface, yet sector-specific approaches can result in fragmented actions. To address this gap, the Joint Risk Assessment Operational Tool (JRA OT), an operational tool of the Tripartite Zoonoses Guide, was developed by the Food and Agriculture Organization of the United Nations, the World Health Organization, and the World Organization for Animal Health. The JRA OT provides a structured framework for joint qualitative risk assessments that integrate multisectoral expertise to identify risk pathways, assess likelihood and impact, and develop consensus-based risk management and communication options. Surveillance systems play a critical role in this process by providing the multisectoral data needed to inform risk assessments, while the JRA process helps identify information gaps and guide the strengthening of integrated One Health surveillance. Implemented in at least 52 countries, the JRA OT has informed national mandates in Indonesia, Viet Nam, and Tanzania, regional strategies in West Africa, and adaptations in Canada. Cascade training has been used to build subnational capacity to facilitate roll out. Sustained leadership commitment, multisectoral coordination, routine applications, local adaptation, and capacity building remain essential for global implementation. Full article

The global fermentation industry faces persistent bottlenecks in scaling laboratory innovations to industrial production, and the integration of synthetic biology (SynBio) and artificial intelligence (AI) within the Design–Build–Test–Learn (DBTL) loop has yielded inconsistent industrial outcomes. This review proposes that transformative impact requires a “scale-up-gated DBTL” framework, in which explicit decision gates constrain every iteration. At the Design phase, scale-down simulation data must inform genetic design choices. At the Test phase, downstream processing compatibility and industrial robustness metrics are enforced as non-negotiable evaluation criteria. At the Learn phase, techno-economic analysis (TEA) and life-cycle assessment (LCA) serve as the convergence criteria, replacing traditional titer plateaus. Through a qualitative cross-sectoral analysis of food, pharmaceutical, agricultural, and energy fermentation, the analysis reveals that workflows incorporating such constraints consistently bridge the valley of death, whereas unconstrained DBTL systematically converges on laboratory optima that are industrially unviable. Five strategic priorities are outlined—embedding TEA/LCA into DBTL, adopting scale-down simulation, building open fermentation data repositories, harmonizing regulatory frameworks, and fostering cross-disciplinary training—as prerequisites for progressing toward fully autonomous, scale-up-aware biomanufacturing. Full article

The convergence of Artificial Intelligence (AI) and Building Information Modeling (BIM) is reshaping how the Architecture, Engineering, and Construction (AEC) sector manages projects across their lifecycle, yet the systemic uptake of this convergence remains poorly understood in developing-economy contexts such as Morocco. This study examines the state, opportunities, and barriers of BIM–AI integration in Morocco’s AEC sector through a PRISMA-guided scoping review combined with a bibliometric analysis. An initial global search of Scopus and Web of Science identified 1842 records; after successive screening for relevance to BIM–AI integration and to the Moroccan context, six core studies were retained for detailed synthesis. The bibliometric analysis, covering the broader filtered corpus, shows a publication trend with three distinct growth phases between 2015 and 2025 and reveals that Moroccan research output on digital construction is disproportionately concentrated on energy-efficiency applications rather than construction management or structural engineering. The synthesis of the six core studies further indicates that BIM–AI integration in Morocco remains at an early, largely 3D-focused stage, with significant conceptual and empirical gaps. Building on these findings, this study proposes a three-tiered strategic roadmap, targeting policy, industry, and academia, to accelerate digital transformation and innovation in Morocco’s construction sector. Full article

Hospital buildings (HBs) are among the most energy-intensive public buildings, yet their life-cycle carbon characteristics, emission drivers, and long-term mitigation potential remain insufficiently quantified. This study establishes a comprehensive life-cycle carbon assessment framework for HBs based on life cycle assessment (LCA), using a Grade-A tertiary hospital in Shenzhen, China, as a case study. The framework quantifies carbon emissions across the materialization, operation, and demolition stages, and estimates operational emissions from public hospital buildings at the city scale. Logarithmic Mean Divisia Index (LMDI) decomposition and Long-range Energy Alternatives Planning (LEAP) modeling were subsequently applied to identify historical drivers and evaluate future mitigation pathways. The results show that the case hospital generated approximately 0.57 Mt CO₂e of gross life-cycle carbon emissions over a 50-year service life, with the operational stage dominating approximately 88% of net emissions. Electricity consumption accounted for 94% of operational energy-related emissions, while HVAC systems and the Diagnostic departments were identified as major carbon hotspots. At the city scale, the gross operational emissions of 73 public hospitals in Shenzhen were estimated at approximately 0.74 Mt CO₂e in 2020 within the defined accounting boundary. For the broader citywide hospital sector, LMDI analysis revealed that annual operational emissions increased from approximately 0.25 Mt CO₂e in 2006 to 0.91 Mt CO₂e in 2020, primarily driven by healthcare service demand and hospital infrastructure expansion, whereas the declining operational carbon emission coefficient provided a partial offset. LEAP scenario analysis further demonstrated that net operational emissions peaked in 2050 under BS and in 2030 under SI and SII. Under SIII, emissions declined continuously from the 2020 base-year level to approximately 0.29 Mt CO₂e in 2060, representing a reduction of approximately 68%. These findings highlight the necessity of coordinate building energy optimization, healthcare infrastructure development, and energy system decarbonization for low-carbon transformation of hospital buildings. Full article

The Artificial Intelligence Pilot Zone Policy is an important strategic initiative for building artificial intelligence (AI) innovation hubs. It provides new opportunities to enhance manufacturing firm resilience and promote the sustainable development of the manufacturing sector. The creation of the National New-Generation Artificial Intelligence Innovation and Development Pilot Zones (AI Pilot Zones) is viewed in this study as a quasi-natural experiment. Using data from Chinese A-share-listed manufacturing firms from 2015 to 2023, we employ a staggered DID model to evaluate the impact of the policy on manufacturing firm resilience. We find that the AI Pilot Zone policy increases manufacturing firm resilience by an average of 0.0282 units. The analysis of potential mechanisms shows that the policy significantly promotes digital talent agglomeration, stimulates urban innovation vitality, and improves firm-level supply chain efficiency. These findings are consistent with the theoretical expectations and provide supportive evidence that these factors may constitute potential mechanisms associated with the policy’s effect on manufacturing firm resilience. The heterogeneity analysis reveals a pronounced “weakness-compensating” effect. At the regional level, the resilience-enhancing effect is stronger for manufacturing firms located in areas with relatively weak digital infrastructure. At the industry level, the effect is more pronounced among firms in low-technology manufacturing industries. At the firm level, the effect is stronger for firms with lower levels of human capital, weaker innovation capacity, and lagging digital transformation. Overall, this study provides micro-level evidence on the resilience effects of the AI Pilot Zone policy and offers policy implications for integrating AI more effectively with the real economy. Full article

Green finance has emerged as a critical mechanism for aligning capital markets with climate and sustainability objectives, particularly as economies face mounting pressure to transition away from carbon-intensive growth models. In hydrocarbon-dependent regions such as the Gulf Cooperation Council (GCC), this transition poses distinctive challenges that require integrated institutional, policy, and financial frameworks. The global transition toward sustainable finance has gathered significant momentum, with green capital markets emerging as a central mechanism for channeling investment toward climate and development objectives. Hydrocarbon-dependent economies face a distinctive challenge in this transition, as they must reconcile resource-based growth models with rising pressures for environmental accountability and low-carbon diversification. This study develops an integrated theoretical framework to examine how Gulf Cooperation Council (GCC) financial systems are transitioning toward green capital markets, drawing on institutional theory, environmental policy pathway analysis, and climate-finance alignment models. Using descriptive statistics from regional stock exchanges covering 2015–2024, the study maps key trends in sustainable asset growth, institutional investor preferences, and regulatory evolution across the GCC. Findings indicate progressive alignment with global ESG norms; sustainable asset valuations grew at 23.5% CAGR (UAE) and 18.7% CAGR (Saudi Arabia). A fixed-effects panel regression with panel-corrected standard errors is estimated across all six GCC economies; regulatory framework maturity emerges as the strongest predictor of green bond issuance (β = 0.47, p < 0.01). Cumulative green bond issuances reached USD 52.6 billion (2015–2024), with renewable energy accounting for 58.1% of the sectoral allocation and green transportation recording a 55.9% CAGR (2020–2024). Policy recommendations focus on GCC-wide harmonization of mandatory ESG disclosure, adoption of a unified green bond taxonomy, and expansion of concessional green financing mechanisms. Substantial cross-country heterogeneity is documented, driven by differences in energy policy commitment, financial market maturity, and institutional capacity. The proposed framework offers specific policy guidance to accelerate green financial integration in the GCC, emphasizing regulatory harmonization, institutional capacity-building, and alignment with SDG targets 7 and 13. The study contributes to the limited evidence base on green finance in hydrocarbon-dependent economies and provides a foundation for future empirical research. Given the small panel dimensions (N = 6 cross-sectional units; T = 10 years), this study is positioned as exploratory rather than confirmatory: the panel-regression estimates and the hypothesized institutional-to-policy-to-finance sequence are interpreted as associational patterns consistent with the proposed framework rather than as definitive causal tests, and the reported coefficients are offered as indicative magnitudes to be re-examined as longer GCC green-finance time series become available. Full article