Search Results (113)

This study examines the integration of Environmental, Social, and Governance (ESG) principles within Romania’s residential real estate sector, concentrating on Constanța, a rapidly evolving urban centre in a transitional economy. Drawing on qualitative data from semi-structured interviews with local real estate professionals and secondary analysis of policy and market documents, the research uncovers inconsistencies in ESG implementation. Environmental compliance is advancing, largely driven by EU regulations such as the European Grean Deal, the Corporate Sustainability Reporting Directive and the Energy Performance of Buildings Directive. Voluntary certification schemes like BREEAM and LEED are emerging as benchmarks for environmental performance; however, their uptake remains limited and insufficiently tailored to local conditions. Meanwhile, the social and governance dimensions lag behind, characterised by inconsistent application and weak institutional backing. Key barriers to effective ESG integration in Romania’s residential real estate sector include weak regulatory enforcement, fragmented policies, limited green finance, low awareness, and a lack of standardised social value metrics. The study concludes that without moving beyond mere regulatory compliance to a framework embedding social inclusivity and adaptive governance, ESG efforts risk perpetuating existing inequalities. It calls for a reconceptualisation of ESG frameworks, developed for mature markets, to better suit transitional urban contexts and support long-term resilience in residential real estate. Full article

Mangroves form the dominant coastal plant community of low-energy tropical intertidal habitats and provide critical ecosystem services to humans and the environment. However, more frequent and increasingly powerful hurricanes and storm surges are creating additional pressure on the natural resilience of these threatened coastal ecosystems. Advances in remote sensing techniques and approaches are critical to providing robust quantitative monitoring of post-storm mangrove forest recovery to better prioritize the often-limited resources available for the restoration of these storm-damaged habitats. Here, we build on previously utilized spatial and temporal ranges of European Space Agency (ESA) Sentinel satellite imagery to monitor and map the recovery of the mangrove forests of the British Virgin Islands (BVI) since the occurrence of back-to-back category 5 hurricanes, Irma and Maria, on September 6 and 19 of 2017, respectively. Pre- to post-storm changes in coastal mangrove forest health were assessed annually using the normalized difference vegetation index (NDVI) and moisture stress index (MSI) from 2016 to 2023 using Google Earth Engine. Results reveal a steady trajectory towards forest health recovery on many of the Territory’s islands since the storms’ impacts in 2017. However, some mangrove patches are slower to recover, such as those on the islands of Virgin Gorda and Jost Van Dyke, and, in some cases, have shown a continued decline (e.g., Prickly Pear Island). Our work also uses a linear ANCOVA model to assess a variety of geospatial, environmental, and anthropogenic drivers for mangrove recovery as a function of NDVI pre-storm and post-storm conditions. The model suggests that roughly 58% of the variability in the 7-year difference (2016 to 2023) in NDVI may be related by a positive linear relationship with the variable of population within 0.5 km and a negative linear relationship with the variables of northwest aspect vs. southwest aspect, island size, temperature, and slope. Full article

Ransomware is a group of malware that aims to make computing resources unavailable, demanding a ransom amount to return control back to users. Ransomware can be classified into two types: crypto-ransomware and locker ransomware. Crypto-ransomware employs strong encryption and prevents users’ access to the system. Locker ransomware makes access unavailable to users either by locking the boot sector or the user’s desktop. The proposed solution is an anomaly-based ransomware detection and prevention system consisting of post- and pre-encryption detection stages. The developed IDS is capable of detecting ransomware attacks by monitoring the usage of resources, triggered by anomalous behavior during an active attack. By analyzing the recorded parameters after recovery and logging any adverse effects, we were able to train the system for better detection patterns. The proposed solution allows for detection and intervention against the crypto and locker types of ransomware attacks. In previous work, the authors introduced a novel anti-ransomware tool for Windows platforms, known as R-Locker, which demonstrates high effectiveness and efficiency in countering ransomware attacks. The R-Locker solution employs “honeyfiles”, which serve as decoy files to attract ransomware activities. Upon the detection of any malicious attempts to access or alter these honeyfiles, R-Locker automatically activates countermeasures to thwart the ransomware infection and mitigate its impact. Building on our prior R-Locker framework this work introduces a multi-stage detection architecture with resource–behavioral hybrid analysis, achieving cross-platform efficacy against evolving ransomware families not addressed previously. Full article

The 2018 Lombok (Indonesia) earthquake caused widespread destruction, significantly affecting both infrastructure and the socio-cultural fabric of local communities. While rehabilitation and reconstruction efforts primarily focus on restoring physical assets, the social and cultural dimensions critical to fostering community resilience are often overlooked. This research explores the concept of Cultural Resilience in promoting post-disaster recovery, with a particular focus on the Sasak Tribe in Lombok. By examining how cultural values, practices, and social networks contribute to adaptive capacity, the study seeks to integrate cultural resilience into disaster recovery strategies. The research employs a mixed-method approach, involving the identification of key characteristics of cultural resilience, mapping the levels of resilience within the community, and analyzing the social networks of cultural actors involved in post-disaster recovery. Through this, a “Build-Back Better” scenario is developed, which aligns rehabilitation plans with local cultural values. The findings are expected to enhance culture-based resilience and offer policy implications for more holistic disaster recovery interventions that strengthen both physical and cultural aspects of community resilience.) Full article

Oil-in-water emulsions (O/W emulsions) are generally used to lubricate the cold rolling process of low-carbon steel. In addition to the obvious advantages of efficient lubrication and cooling of the process, there are also some disadvantages, mainly related to emulsion bath maintenance, subsequent production steps and waste disposal. In some application areas, Oil-Free Lubricants (OFL’s) have been shown to be at least equally effective in decreasing friction and wear as conventional oil-based lubricants, while resulting in benefits related to waste disposal. In 2023, a project named “Transfer of aqueous oil free lubricants into steel cold rolling practice” (acronym ‘RollOilFreeII’) began, with it receiving funding from the Research Fund for Coal and Steel (RFCS). This project aims at an industrial application of Oil-Free Lubricants in the steel cold rolling process. The project builds on the work of the ‘RollOilFree’ project (also carried out in the RFCS-framework). This article briefly recapitulates the findings in the RollOilFree project and describes the objectives, benefits, activities and first results of the RollOilFreeII project. Notably, a pilot mill trial at high speed has been carried out, showing a good performance of the investigated OFLs. Back-calculated friction values were equal to, or even slightly lower than, reference O/W emulsions. The strip cleanliness with OFLs is much better than it is with the reference O/W emulsions. Only for a very thin product, as is the case in tinplate rolling, does the direct application of a conventional O/W dispersion (a high-particle-sized O/W emulsion) give a better performance than the investigated OFLs. Further development of OFLs should focus on this aspect. Full article

Post-disaster resettlement often faces abandonment and modification; yet, the factors influencing long-term residential satisfaction, especially within diverse communities, remain underexplored. This study examines how satisfaction evolves over time in relation to socio-economic status and community participation comparing the underprivileged Thami community in Panipokhari and the privileged Brahmin community in Jillu Integrated Settlement of Nepal. Using a mixed-method case study approach, this research integrates quantitative satisfaction scores with qualitative insights from surveys, interviews, and observations over three years. Findings reveal diverging satisfaction trends: Jillu’s satisfaction remained stable (3.55 to 3.43 from 2021 to 2023), whereas Panipokhari’s declined (3.27 to 2.33) due to unmet housing needs and limited participation. Correlation tests and qualitative interviews confirmed that while all five key factors—housing design, thermal comfort, water, cultural appropriateness, and architectural aesthetics—influenced satisfaction, their importance varied. These findings challenge “one size fits all” top-down resettlement models, demonstrating that housing adaptability and participatory decision-making are more critical than structural adequacy alone. The study underscores the need for flexible, community-driven housing strategies within the Build Back Better (BBB) framework. By integrating the housing satisfaction theory, the housing mobility theory, and the BBB framework, it advances understanding of socio-economic agency in shaping post-disaster housing outcomes, providing insights for sustainable and inclusive resettlement policies. Full article

As wildfires become more frequent and severe in the face of global environmental change, it becomes crucial not only to assess, prevent, and suppress them but also to manage the aftermath effectively. Given the temporal interconnections between these issues, we explored the concept of the “wildfire science loop”—a framework categorizing wildfire research into three stages: “before”, “during”, and “after” wildfires. Based on this partition, we performed a systematic review by linking particular topics and keywords to each stage, aiming to describe each one and quantify the volume of published research. The results from our review identified a substantial imbalance in the wildfire research landscape, with the post-fire stage being markedly underrepresented. Research focusing on the “after” stage is 1.5 times (or 46%) less prevalent than that on the “before” stage and 1.8 (or 77%) less than that on the “during” stage. This discrepancy is likely driven by a historical emphasis on prevention and suppression due to immediate societal needs. Aiming to address and overcome this imbalance, we present our perspectives regarding a strategic agenda to enhance our understanding of post-fire processes and outcomes, emphasizing the socioecological impacts of wildfires and the management of post-fire recovery in a multi-level and transdisciplinary approach. These proposals advocate integrating knowledge-driven research on burn severity and ecosystem mitigation/recovery with practical, application-driven management strategies and strategic policy development. This framework also supports a comprehensive agenda that spans short-term emergency responses to long-term adaptive management, ensuring that post-fire landscapes are better understood, managed, and restored. We emphasize the critical importance of the “after-fire” stage in breaking negative planning cycles, enhancing management practices, and implementing nature-based solutions with a vision of “building back better”. Strengthening a comprehensive and balanced research agenda focused on the “after-fire” stage will also enhance our ability to close the loop of socioecological processes involved in adaptive wildfire management and improve the alignment with international agendas such as the UN’s Decade on Ecosystem Restoration and the EU’s Nature Restoration Law. By addressing this research imbalance, we can significantly improve our ability to restore ecosystems, enhance post-fire resilience, and develop adaptive wildfire management strategies that are better suited to the challenges of a rapidly changing world. Full article

The architecture in the title refers to physical buildings, spaces, and walls. Dominant architectural culture prefers minimalist environments that contradict the information setting needed for the infant brain to develop. Much of world architecture after World War II is therefore unsuitable for raising children. Data collected by technological tools, including those that use AI for processing signals, indicate a basic misfit between cognition and design. Results from the way AI software works in general, together with mobile robotics and neuroscience, back up this conclusion. There exists a critical research gap: the systematic investigation of how the geometry of the built environment influences cognitive development and human neurophysiology. While previous studies have explored environmental effects on health (other than from pathogens and pollutants), they largely focus on factors such as acoustics, color, and light, neglecting the fundamental role of spatial geometry. Geometrical features in the ancestral setting shaped neural circuits that determine human cognition and intelligence. However, the contemporary built environment consisting of raw concrete, plate glass, and exposed steel sharply contrasts with natural geometries. Traditional and vernacular architectures are appropriate for life, whereas new buildings and urban spaces adapt to human biology and are better for raising children only if they follow living geometry, which represents natural patterns such as fractals and nested symmetries. This study provides a novel, evidence-based framework for adaptive and empathetic architectural design. Full article

This study employed machine learning, specifically deep neural networks (DNNs) and long short-term memory (LSTM) networks, to build a model for estimating acid rain pH levels. The Yangming monitoring station in the Taipei metropolitan area was selected as the research site. Based on pollutant sources from the air mass back trajectory (AMBT) of the HY-SPLIT model, three possible source regions were identified: mainland China and the Japanese islands under the northeast monsoon system (Region C), the Philippines and Indochina Peninsula under the southwest monsoon system (Region R), and the Pacific Ocean under the western Pacific high-pressure system (Region S). Data for these regions were used to build the ANN_AMBT model. The AMBT model provided air mass origin information at different altitudes, leading to models for 50 m, 500 m, and 1000 m (ANN_AMBT_50m, ANN_AMBT_500m, and ANN_AMBT_1000m, respectively). Additionally, an ANN model based only on ground station attributes, without AMBT information (LSTM_No_AMBT), served as a benchmark. Due to the northeast monsoon, Taiwan is prone to severe acid rain events in winter, often carrying external pollutants. Results from these events showed that the LSTM_AMBT_500m model achieved the highest percentages of model improvement rate (MIR), ranging from 17.96% to 36.53% (average 27.92%), followed by the LSTM_AMBT_50m model (MIR 12.94% to 26.42%, average 21.70%), while the LSTM_AMBT_1000m model had the lowest MIR (2.64% to 12.26%, average 6.79%). These findings indicate that the LSTM_AMBT_50m and LSTM_AMBT_500m models better capture pH variation trends, reduce prediction errors, and improve accuracy in forecasting pH levels during severe acid rain events. Full article

Although the development of high-resolution remote sensing satellite technology has made it possible to reconstruct the 3D structure of object-level features using satellite imagery, the results from a single reconstruction are often insufficient to comprehensively describe the 3D structure of the target. Therefore, developing an effective 3D point cloud fusion method can fully utilize information from multiple observations to improve the accuracy of 3D reconstruction. To this end, this paper addresses the problems of shape distortion and sparse point cloud density in existing 3D point cloud fusion methods by proposing a 3D point cloud fusion method based on Earth mover’s distance (EMD) auto-evolution and local parameterization network. Our method is divided into two stages. In the first stage, EMD is introduced as a key metric for evaluating the fusion results, and a point cloud fusion method based on EMD auto-evolution is constructed. The method uses an alternating iterative technique to sequentially update the variables and produce an initial fusion result. The second stage focuses on point cloud optimization by constructing a local parameterization network for the point cloud, mapping the upsampled point cloud in the 2D parameter domain back to the 3D space to complete the optimization. Through these two steps, the method achieves the fusion of two sets of non-uniform point cloud data obtained from satellite stereo images into a single, denser 3D point cloud that more closely resembles the true target shape. Experimental results demonstrate that our fusion method outperforms other classical comparison algorithms for targets such as buildings, planes, and ships, and achieves a fused RMSE of approximately 2 m and an EMD accuracy better than 0.5. Full article

Electric vehicles (EVs) are becoming increasingly popular as an efficient transportation solution but they also present unique challenges for energy management. Bi-directional charging (BDC) is a solution that allows EVs to not only consume energy from the grid but also supply energy back to the grid. This facilitates vehicle-to-load (V2L) integration, where EVs can act as mobile power sources for homes, buildings, and the grid. V2L enables better energy management by utilizing EVs as a flexible resource to balance grid demand and supply in the proposed system. This is achieved through intelligent coordination between the EVs, charging stations, and the grid, using smart meters and communication networks. Integration of BDC and V2L also enables EVs to provide backup power during grid outages, reduce the need for costly grid infrastructure, and support renewable energy integration. BDC with V2L integration is a promising approach for optimal energy management in EVs and can play a significant role in the future of sustainable transportation and energy systems. The proposed model reached 95.13% charging efficiency, 95.03% energy management, 95.69% power rating, 96.28% voltage support and 87.99% temperature management. Full article

An English-Bengali machine translation (MT) application can convert an English text into a corresponding Bengali translation. To build a better model for this task, we can optimize English-Bengali MT. MT for languages with rich resources, like English-German, started decades ago. However, MT for languages lacking many parallel corpora remains challenging. In our study, we employed back-translation to improve the translation accuracy. With back-translation, we can have a pseudo-parallel corpus, and the generated (pseudo) corpus can be added to the original dataset to obtain an augmented dataset. However, the new data can be regarded as noisy data because they are generated by models that may not be trained very well or not evaluated well, like human translators. Since the original output of a translation model is a probability distribution of candidate words, to make the model more robust, different decoding methods are used, such as beam search, top-k random sampling and random sampling with temperature T, and others. Notably, top-k random sampling and random sampling with temperature T are more commonly used and more optimal decoding methods than the beam search. To this end, our study compares LSTM (Long-Short Term Memory, as a baseline) and Transformer. Our results show that Transformer (BLEU: $27.80$ in validation, $1.33$ in test) outperforms LSTM ($3.62$ in validation, $0.00$ in test) by a large margin in the English-Bengali translation task. (Evaluating LSTM and Transformer without any augmented data is our baseline study.) We also incorporate two decoding methods, top-k random sampling and random sampling with temperature T, for back-translation that help improve the translation accuracy of the model. The results show that data generated by back-translation without top-k or temperature sampling (“no strategy”) help improve the accuracy (BLEU $38.22$, +$10.42$ on validation, $2.07$, +$0.74$ on test). Specifically, back-translation with top-k sampling is less effective ($k=10$, BLEU $29.43$, +$1.83$ on validation, $1.36$, +$0.03$ on test), while sampling with a proper value of T, $T=0.5$ makes the model achieve a higher score ($T=0.5$, BLEU $35.02$, +$7.22$ on validation, $2.35$, +$1.02$ on test). This implies that in English-Bengali MT, we can augment the training set through back-translation using random sampling with a proper temperature T. Full article

Immune checkpoint inhibitors (ICI) are a promising form of immunotherapy that have significantly changed the therapeutic landscape for many advanced cancers. They have shown unique clinical benefit against a broad range of tumour types and a strong overall impact on survival in studied patient populations. However, there are still many limitations holding back this immunotherapy from reaching its full potential as a possible curative option for advanced cancer patients. A great deal of research is being undertaken in the hope of driving advancements in this area, building a better understanding of the mechanisms behind immune checkpoint inhibition and ultimately developing more effective, safer, and wider-reaching agents. Taking into account the current literature on this topic, this review aims to explore in depth the basis of the use of ICIs in the treatment of advanced cancers, evaluate its efficacy and safety, consider its current limitations, and finally reflect on what the future holds for this very promising form of cancer immunotherapy. Full article

Ultra-high-molecular-weight polyethylene (UHMWPE) components for orthopedic implants have historically been integrated into metal backings by direct-compression molding (DCM). However, metal backings are costly, stiffer than cortical bone, and may be associated with medical imaging distortion and metal release. Hybrid-manufactured DCM UHMWPE overmolded additively manufactured polyetheretherketone (PEEK) structural components could offer an alternative solution, but are yet to be explored. In this study, five different porous topologies (grid, triangular, honeycomb, octahedral, and gyroid) and three surface feature sizes (low, medium, and high) were implemented into the top surface of digital cylindrical specimens prior to being 3D printed in PEEK and then overmolded with UHMWPE. Separation forces were recorded as 1.97–3.86 kN, therefore matching and bettering the historical industry values (2–3 kN) recorded for DCM UHMWPE metal components. Infill topology affected failure mechanism (Type 1 or 2) and obtained separation forces, with shapes having greater sidewall numbers (honeycomb-60%) and interconnectivity (gyroid-30%) through their builds, tolerating higher transmitted forces. Surface feature size also had an impact on applied load, whereby those with low infill-%s generally recorded lower levels of performance vs. medium and high infill strategies. These preliminary findings suggest that hybrid-manufactured structural composites could replace metal backings and produce orthopedic implants with high-performing polymer–polymer interfaces. Full article

Background and Objectives: From the year 1 anno Domini until 1855, with the third plague, major pandemics occurred on average every 348 years. Since then, they have occurred on average every 33 years, with coronavirus disease 2019 (COVID-19) now underway. Even though current technologies have greatly improved the way of life of human beings, COVID-19, with more than 700,000,000 cases and 6,950,000 deaths worldwide by the end of 2023, reminds us that much remains to be done. This report looks back at 18 months of COVID-19, from March 2020 to August 2021, with the aim of highlighting potential solutions that could help mitigate the impact of future pandemics. Materials and Methods: COVID-19 data, including case and death reports, were extracted daily from the Worldometer platform to build a database for the macroscopic analysis of the spread of the virus around the world. Demographic data were integrated into the COVID-19 database for a better understanding of the spatial spread of the SARS-CoV-2 virus in cities/municipalities. Without loss of generality, only data from the top 30 (out of 200 and above) countries ranked by total number of COVID-19 cases were analyzed. Statistics (regression, t-test (p < 0.05), correlation, mean ± std, etc.) were carried out with Excel software (Microsoft^® Excel^® 2013 (15.0.5579.1001)). Spectral analysis, using Matlab software (license number: 227725), was also used to try to better understand the temporal spread of COVID-19. Results: This study showed that COVID-19 mainly affects G20 countries and that cities/municipalities with high population density are a powerful activator of the spread of the virus. In addition, spectral analysis highlighted that the very first months of the spread of COVID-19 were the most notable, with a strong expansion of the SARS-CoV-2 virus. On the other hand, the following six months showed a certain level of stability, mainly due to multiple preventive measures such as confinement, the closure of non-essential services, the wearing of masks, distancing of 2 m, etc. Conclusion: Given that densely populated cities and municipal areas have largely favored the spread of the SARS-CoV-2 virus, it is believed that such a demographic context is becoming a societal problem that developed countries must address in a manner that is adequate and urgent. COVID-19 has made us understand that it is time to act both preventatively and curatively. With phenomenological evidence suggesting that the next pandemic could occur in less than 50 years, it may be time to launch new societal projects aimed at relieving congestion in densely populated regions. Full article