Search Results (513)

Population aging poses significant challenges to the sustainability of pension systems. This study presents an integrated methodological approach that uniquely combines actuarial life-cycle modeling with agent-based simulation to assess the potential of Reverse Mortgage Loans (RMLs) as a dual lever for enhancing retiree welfare and supporting pension system resilience under demographic and financial uncertainty. We explore Reverse Mortgage Loans (RMLs) as a potential financial instrument to support retirees while alleviating pressure on public pensions. Unlike prior research that treats individual decisions or policy outcomes in isolation, our hybrid model explicitly captures feedback loops between household-level behavior and system-wide financial stability. To test our hypothesis that RMLs can improve individual consumption outcomes and bolster systemic solvency, we develop a hybrid model combining actuarial techniques and agent-based simulations, incorporating stochastic housing prices, longevity risk, regulatory capital requirements, and demographic shifts. This dual-framework enables a structured investigation of how micro-level financial decisions propagate through market dynamics, influencing solvency, pricing, and adoption trends. Our central hypothesis is that reverse mortgages, when actuarially calibrated and macroprudentially regulated, enhance individual financial well-being while preserving long-run solvency at the system level. Simulation results indicate that RMLs can improve consumption smoothing, raise expected utility for retirees, and contribute to long-term fiscal sustainability. Moreover, we introduce a dynamic regulatory mechanism that adjusts capital buffers based on evolving market and demographic conditions, enhancing system resilience. Our simulation design supports multi-scenario testing of financial robustness and policy outcomes, providing a transparent tool for stress-testing RML adoption at scale. These findings suggest that, when well-regulated, RMLs can serve as a viable supplement to traditional retirement financing. Rather than offering prescriptive guidance, this framework provides insights to policymakers, financial institutions, and regulators seeking to integrate RMLs into broader pension strategies. Full article

Outdoor access, often referred to as pop holes, is widely used to improve the production and welfare of hens. Such cage-free environments present an opportunity for precision flock management via best environmental control practices. However, outdoor access disrupts the integrity of the indoor environment, including properly planned ventilation. Moreover, complaints exist that hens do not use the holes to access the outdoor environment due to the strong incoming airflow through the outdoor access, as they behave as uncontrolled air inlets in a negative pressure ventilation system. As the egg industry transitions to cage-free systems, there is an urgent need for validated computational fluid dynamics (CFD) models to optimize ventilation strategies that balance animal welfare, environmental control, and production efficiency. We developed and validated CFD models of a cage-free hen house with outdoor access by specifying real-world conditions, including two exhaust fans, sidewall ventilation inlets, wire-meshed pens, outdoor access, and plenum inlets. The simulations of four ventilation scenarios predict the measured air flow velocity with an error of less than 50% for three of the scenarios, and the simulations predict temperature with an error of less than 6% for all scenarios. Plenum-based systems outperformed sidewall systems by up to 136.3 air changes per hour, while positive pressure ventilation effectively mitigated disruptions to outdoor access. We expect that knowledge of improved ventilation strategy will help the egg industry improve the welfare of hens cost-effectively. Full article

The research was initiated by the Plečnik House gallery (Ljubljana, Slovenia) and focuses on the 3D architectural reconstruction of the unrealised monument of the Czech military leader Jan Žižka, designed by the Slovenian architect Jože Plečnik. In addition, the experience with the 3D reconstructed monument in the exhibition “Plečnik and the Sacred” was analysed. Using the available references and interpretative approaches, a digital and 3D-printed reconstruction was created that retains Plečnik’s architectural style. The experimental phase included a detailed interpretation of the studied references, 3D modelling, 3D printing, exhibition and experience analysis. The dimensions of the finished 3D-printed model are 52.80 × 55.21 × 44.60 cm. It was produced using stereolithography (SLA) for figurative elements and fused deposition modelling (FDM) for architectural components. The reconstruction was evaluated using participant testing, including semantic differential analysis, comparative studies, and knowledge-based questionnaires. The results showed that architectural elements were reconstructed with an average similarity score of 1.97 out of 5. Statues followed with a score of 1.81, and props, though detailed, met audience expectations, scoring 1.61. Clothing received the lowest score of 1.40. This research emphasises the importance of a hypothetical digital 3D reconstruction of never constructed monument for broader understanding of Plečnik’s legacy. Full article

Since the advent of composite quantile regression (CQR), its inherent robustness has established it as a pivotal methodology for high-dimensional data analysis. High-dimensional outlier contamination refers to data scenarios where the number of observed dimensions (p) is much greater than the sample size (n) and there are extreme outliers in the response variables or covariates (e.g., p/n > 0.1). Traditional penalized regression techniques, however, exhibit notable vulnerability to data outliers during high-dimensional variable selection, often leading to biased parameter estimates and compromised resilience. To address this critical limitation, we propose a novel empirical likelihood (EL)-based variable selection framework that integrates a Bayesian LASSO penalty within the composite quantile regression framework. By constructing a hybrid sampling mechanism that incorporates the Expectation–Maximization (EM) algorithm and Metropolis–Hastings (M-H) algorithm within the Gibbs sampling scheme, this approach effectively tackles variable selection in high-dimensional settings with outlier contamination. This innovative design enables simultaneous optimization of regression coefficients and penalty parameters, circumventing the need for ad hoc selection of optimal penalty parameters—a long-standing challenge in conventional LASSO estimation. Moreover, the proposed method imposes no restrictive assumptions on the distribution of random errors in the model. Through Monte Carlo simulations under outlier interference and empirical analysis of two U.S. house price datasets, we demonstrate that the new approach significantly enhances variable selection accuracy, reduces estimation bias for key regression coefficients, and exhibits robust resistance to data outlier contamination. Full article

The real estate market significantly influences individual lives, corporate decisions, and national economic sustainability. Therefore, constructing a data-driven, interpretable real estate market prediction model is essential. It can clarify each factor’s role in housing prices and transactions, offering a scientific basis for market regulation and enterprise investment decisions. This study comprehensively measures the evolution trends of the real estate markets in Beijing, Shanghai, Guangzhou, and Shenzhen, China, from 2003 to 2022 through three dimensions. Then, various machine learning methods and interpretability methods like SHAP values are used to explore the impact of supply, demand, policies, and expectations on the real estate market of China’s first-tier cities. The results reveal the following: (1) In terms of commercial housing sales area, adequate housing supply, robust medical services, and high population density boost the sales area, while demand for small units reflects buyers’ balance between affordability and education. (2) In terms of commercial housing average sales price, growth is driven by education investment, population density, and income, with loan interest rates serving as a stabilizing tool. (3) In terms of commercial housing sales amount, educational expenditure, general public budget expenditure, and real estate development investment amount drive revenue, while the five-year loan benchmark interest rate is the primary inhibitory factor. These findings highlight the divergent impacts of supply, demand, policy, and expectation factors across different market dimensions, offering critical insights for enterprise investment strategies. Full article

The optimization of land development requires a deep understanding of end-user expectations to ensure that new residential environments are both market-responsive and socially sustainable. This paper presents a novel prioritization-based technique for identifying and ranking property features according to buyer preferences. Using the MoSCoW method in combination with conjoint analysis, the study evaluates the relative importance of various housing attributes, such as layout, number of rooms, access to transportation, and availability of parking or green areas. The results provide structured insights into demand-side priorities and offer actionable guidelines for developers, urban planners, and decision-makers engaged in land use planning. By linking individual housing preferences with broader planning strategies, the proposed framework contributes to the creation of better-aligned, user-centric urban developments. The approach is tested on a local property market, and its potential applications in strategic zoning, infrastructure placement, and residential density modeling are discussed. Full article

Rooster behavior and activity are critical for egg fertility and hatchability in broiler and layer breeder houses. Desirable roosters are expected to have good leg health, reach sexual maturity, be productive, and show less aggression toward females during mating. However, not all roosters are desirable, and low-productive roosters should be removed and replaced. The objectives of this study were to apply an object detection model based on deep learning to identify hens and roosters based on phenotypic characteristics, such as comb size and body size, in a cage-free (CF) environment, and to compare the performance metrics among the applied models. Six roosters were mixed with 200 Lohmann LSL Lite hens during the pre-peak phase in a CF research facility and were marked with different identifications. Deep learning methods, such as You Only Look Once (YOLO) models, were innovated and trained (based on a comb size of up to 2500 images) for the identification of male and female chickens based on comb size and body features. The performance matrices of the YOLOv5u and YOLOv11 models, including precision, recall, mean average precision (mAP), and F1 score, were statistically compared for hen and rooster detection using a one-way ANOVA test at a significance level of p < 0.05. For rooster detection based on comb size, YOLOv5lu, and YOLOv11x variants performed the best among the five variants of each model, with YOLOv5lu achieving a precision of 87.7%, recall of 56.3%, and mAP@0.50 of 60.1%, while YOLOv11x achieved a precision of 86.7%, recall of 65.3%, and mAP@0.50 of 61%. For rooster detection based on body size, YOLOv5xu, and YOLOv11m outperformed other variants, with YOLOv5xu achieving a precision of 88.9%, recall of 77.7%, and mAP@0.50 of 82.3%, while YOLOv11m achieved a precision of 89.0%, recall of 78.8%, and mAP@0.50 of 82.6%. This study provides a reference for automatic rooster monitoring based on comb and body size and offers further opportunities for tracking the activities of roosters in a poultry breeder farm for performance evaluation and genetic selection in the future. Full article

Retrofitting the existing housing stock to a high level of energy efficiency will not be limited to achieving the decarbonisation of 80.3 MtCO2e residential emissions and reducing fuel poverty in 4.16 million households, but also improving the health and well-being of UK residents and their overall quality of life. The current progress of housing retrofitting is poor, at less than 1%. The UK expects to achieve net zero by 2050, and the challenge is immense as there are more than 30 million houses. The challenge is similar in other global contexts. Even if the required technology, supply chain, skilled labour, and finance could have been provided, the retrofitting would not move forward without positive engagement from the clients. Proper strategies are required to retrofit at scale. Focusing on the challenges of stakeholder engagement in housing retrofitting, this study focused on developing a hybrid one-stop shop solution through design science research. A theoretical artefact and an empirical system requirement specification document were developed to propose a one-stop shop solution. This was tested through retrofit industry stakeholders. Findings reveal that the one-stop shop model will be a good answer to retrofitting at scale, providing the resident engagement of 30.1 million households. The model can support residents with or without computer literacy due to its hybrid approach. The proposed theoretical and industrial models can be used as base models for developing one-stop shops for housing retrofitting by adapting them for context-specific requirements. Full article

In Saudi Arabia’s hot and arid climate, residential buildings account for over half of national electricity consumption, with cooling demands alone responsible for more than 70% of this use. This paper explores the hypothesis that contemporary villa designs are inherently inefficient and that current building regulations fall short of enabling adequate thermal performance. This issue is expected to become increasingly significant in the near future as external temperatures continue to rise. The study aims to assess whether passive design strategies rooted in both engineering and architectural principles can offer substantial reductions in cooling energy demand under current and future climatic conditions. A typical detached villa was simulated using IES-VE to test a range of passive measures, including optimized window-to-wall ratios, enhanced glazing configurations, varied envelope constructions, solar shading devices, and wind-tower-based natural ventilation. Parametric simulations were conducted under current climate data and extended to future weather scenarios. Unlike many prior studies, this work integrates these strategies holistically and evaluates their combined impact, rather than in isolation while assessing the impact of future weather in the region. The findings revealed that individual measures such as insulated ceilings and reduced window-to-wall ratios significantly lowered cooling loads. When applied in combination, these strategies achieved a 68% reduction in cooling energy use compared to the base-case villa. While full passive performance year-round remains unfeasible in such extreme conditions, the study demonstrates a clear pathway toward energy-efficient housing in the Gulf region. Full article

The present paper describes a novel and user-friendly Decision Support System (e-DSS) designed to assist technicians in the preliminary design stage of a building renovation process based on the solutions developed in the innovation project e-SAFE, funded by the EU under the H2020 program. The e-DSS is engineered to rapidly assess key performance indicators, including energy performance before and after renovation, reduction in CO₂ emission for space heating, space cooling, and DHW preparation, seismic upgrade feasibility, expected costs, and payback time. To demonstrate its capabilities, the e-DSS was applied to an existing public housing building in Catania, southern Italy. The predicted thermal energy needs for space heating and cooling were compared to the results from detailed simulations using a professional-grade software tool, for both as-built condition and a proposed renovation generated by the e-DSS itself. The discrepancies identified through this comparison will inform the refinement of the e-DSS algorithms to increase their accuracy and reliability. More generally, this paper recommends suitable algorithms that can be effectively employed in the development of simplified decision-making tools specifically tailored for building professionals operating in the early phase of building renovation projects. Full article

Leonardite (LEO), a microbial derived product rich in humic and fulvic acids, has been tested, due to its beneficial properties for health and well-being, as a feed additive, mainly in non-ruminant species. Although there are some reports of LEO supplementation in ruminants fed with high-to medium-forage based diets, there is no information available of the potential effects of LEO in ruminants fed, under sub-tropical climate conditions, with high-energy diets during long-term fattening. For this reason, the objective of the present experiment was to evaluate the effects of LEO levels inclusion in diets for feedlot lambs finished over a long-term period. For this reason, 48 Pelibuey × Katahdin lambs (initial weight = 20.09 ± 3.55 kg) were fed with a high-energy diet (88:12 concentrate to forage ratio) supplemented with LEO (with a minimum of 75% total humic acids) for 130 days as follows: (1) diet without LEO, (2) diet supplemented with 0.20% LEO, (3) diet supplemented with 0.40% LEO, and (4) diet supplemented with 0.60% LEO. For each treatment, Leonardite was incorporated with the mineral premix. Lambs were blocked by weight and housed in 24 pens (2 lambs/pen). Treatment effects were contrasted by orthogonal polynomials. The average climatic conditions that occurred during the experimental period were 31.6 ± 2.4 °C ambient temperature and 42.2 ± 8.1% relative humidity (RH). Those values of ambient temperature and RH represent a temperature humidity index (THI) of 79.07; thus, lambs were finished under high heat load conditions. The inclusion of LEO in diet did not affect dry matter intake (p ≥ 0.25) and average daily gain (p ≥ 0.21); therefore, feed to gain ratio was not affected (p ≥ 0.18). The observed to expected dietary net energy averaged 0.96 and was not affected by LEO inclusion (p ≥ 0.26). The lower efficiency (−4%) of dietary energy utilization is an expected response given the climatic conditions of high ambient heat load presented during fattening. Lambs that were slaughtered at an average weight of 49.15 ± 6.00 kg did not show differences on the variables measured for carcass traits (p ≥ 0.16), shoulder tissue composition (p ≥ 0.59), nor in visceral mass (p ≥ 0.46) by inclusion of LEO. Under the climatic conditions in which this experiment was carried out, LEO supplementation up to 0.60% in diet (equivalent to 0.45% of humic substances) did not did not help to alleviate the extra-energy expenditure used to dissipate the excessive heat and did not change the gained tissue composition of the lambs that were fed with high-energy diets during long-term period under sub-tropical climate conditions. Full article

Studies have indicated that there was a disease interaction of pandemic influenza with respiratory tuberculosis (TB) in Malta, which could explain the heightened mortality recorded in young adults. We revisit the 1918/19 influenza and TB syndemic potential on the island of Malta. Borrowing from crisis studies that explore the harvesting effect, we used the method of assessing changes in pre-pandemic, pandemic, fallow, and post-pandemic mortality/life expectancy to reveal the syndemic experience. Pre-pandemic (1914–1917) life expectancy at birth was significantly higher, at 37.91 years, than during the pandemic (1918), when life expectancy dropped to 33.26 years (Z = 10.56, p < 0.0001). Post-pandemic (1919) life expectancy rose to 43.49 years, which was an even longer life expectancy than pre-pandemic (Z = 17.61, p < 0.0001). There were significant changes in TB mortality death rates during the four periods in those of reproductive age. Augmenting our framework for studies of syndemics involving short-term events, we proposed the identification of contributing, driving, and limiting factors. Underlying living conditions contributed to the syndemic. The exacerbation of housing conditions, the economy associated with the First World War, and meteorological measures—temperature, relative humidity, and rainfall—were driving factors. The early implementation of mitigation strategies, such as restrictions on mass gatherings, were limiting factors of the syndemic. Full article

The resettlement of development-induced migrants is a complex socioeconomic and cultural process. The levels of place identity among migrants may profoundly affect their long-term stability and sustainable development in host communities. For long-distance displaced migrants, their social relations undergo drastic changes, and the extent of social-relations reconstruction determines their place identity in the resettlement area. Previous studies mainly concentrated on migrant compensation schemes, housing, and land allocation, and livelihood restoration. However, insufficient attention has been paid to the mechanism by which social-relations reconstruction shapes place identity. Drawing on sample survey data collected during 2022–2023 from migrants displaced from the Three Gorges Dam (TGD) in China to various other provinces, this study conceptualizes place identity in three dimensions: group identity, permanent settlement intention, and expectations for children. A structural equation model (SEM) was employed to investigate how social relations, categorized as geographical, home-tied, and carried-over relations, mediate the influence of multiple factors on place identity. The findings are that: (1) among development-induced migrants, social relations exerted significant positive effects on place identity, along with personal characteristics, socioeconomic characteristics, land, and housing factors. Moreover, family features, socioeconomic characteristics, and land and housing factors all had a significantly positive impact on social relations; (2) social relations acted as a full mediator between family features and place identity, and a partial mediator between socioeconomic characteristics/land and housing factors and place identity; (3) among geographical, home-tied, and carried-over social relations, geographical social relations had the largest effect on place identity, followed by carried-over relations. However, continuous dependence on home-tied social relations negatively affected the migrants’ development of place identity in new resettlement areas. This study elucidates the role of social relations in the socioeconomic and cultural reconstruction during migrant resettlement, offering insight for improving resettlement policies and promoting sustainable community integration. Full article

As a typical World Cultural Heritage city, Macau has a special regional identity and outstanding cultural value in the built heritage and spatial identity of the city. As contemporary cultural industries have undergone rapid development to transform architectural heritage spaces into displays into showcases of heritage significance, an adaptive transformation strategy is not to be ignored. Although the current transformations demonstrate functional efficacy, they lack of a cultural environment, the Gestalt of the Genius Loci, and a limited visitor experience. For this research, we use the Genius Loci theory to identify constitutive spatial elements and derive theory-based evaluation criteria for the Mandarin’s House, which acts as the case study. It provides a comprehensive evaluation framework across four dimensions: spatial perception, cultural identity, emotional engagement, and functional attributes, each one comprising 20 specific indicators. This research reviews the factors that affect the recognition of cultural identity through quantitative analysis using Importance–Performance Analysis (IPA), evaluating the importance–performance relationships of these indicators. Critical gaps between visitor expectations and current spatial performance are found. Therefore, four optimization strategies are proposed accordingly. (1) Physical experience is enriched through reconstruction of spatial narrative; (2) spiritual experience is reinforced through cultural memory activation; (3) regional characteristics are strengthened through the contextualization of heritage values; and (4) sustainable development mechanisms for adaptive reuse are established. Taken as a systematic approach, it offers both theoretical and practical insight into the regeneration of the architectural heritage spaces in the World Cultural Heritage cities. Full article

Artificial retinal devices require a high-density electrode array and mechanical flexibility to effectively stimulate retinal cells. However, designing such devices presents significant challenges, including the need to conform to the curvature of the eyeball and cover a large area using a single platform. To address these issues, we developed a parylene-based multi-module retinal device (MMRD) integrating a complementary metal-oxide semiconductor (CMOS) system. The proposed device is designed for suprachoroidal transretinal stimulation, with each module comprising a parylene-C thin-film substrate, a CMOS chip, and a ceramic substrate housing seven platinum electrodes. The smart CMOS system significantly reduces wiring complexity, enhancing the device’s practicality. To improve fabrication reliability, we optimized the encapsulation process, introduced multiple silane coupling modifications, and utilized polyvinyl alcohol (PVA) for easier detachment in flip-chip bonding. This study demonstrates the fabrication and evaluation of the MMRD through in vitro and in vivo experiments. The device successfully generated the expected current stimulation waveforms in both settings, highlighting its potential as a promising candidate for future artificial vision applications. Full article