Search Results (1,786)

In response to the climate crisis, Colombia has committed to reducing greenhouse gas (GHG) emissions by 2030 through an energy transition strategy that promotes Non-Conventional Renewable Energy Sources (NCRES) and, increasingly, natural gas. Although natural gas is regarded as a transitional fuel with lower carbon intensity than other fossil fuels, existing reserves could be depleted by 2030 if no new discoveries are made. To assess this risk, a System Dynamics model was developed to project supply and demand under alternative transition pathways. The model integrates: (1) GDP, urban population growth, and adoption of clean energy, (2) the behavior of six major consumption sectors, and (3) the role of gas-fired thermal generation relative to NCRES output and hydroelectric availability, influenced by the El Niño river-flow variability. The novelty and contribution of this study lie in the integration of supply and demand within a unified System Dynamics framework, allowing for a holistic understanding of the Colombian natural gas market. The model explicitly incorporates feedback mechanisms such as urbanization, vehicle replacement, and hydropower variability, which are often overlooked in traditional analyses. Through the evaluation of twelve policy scenarios that combine hydrogen, wind, solar, and new gas reserves, the study provides a comprehensive view of potential energy transition pathways. A comparative analysis with official UPME projections highlights both consistencies and divergences in long-term forecasts. Furthermore, the quantification of demand coverage from 2026 to 2033 reveals that while current reserves can satisfy demand until 2026, the expansion of hydrogen, wind, and solar sources could extend full coverage until 2033; however, ensuring long-term sustainability ultimately depends on the discovery and development of new reserves, such as the Sirius-2 well. Full article

The paper contributes to the debate on the holistic sustainability assessment of real estate projects, integrating economic, financial, environmental, and social aspects. A methodological study is presented to support decision-making processes involving the preferability ranking of alternative investment scenarios: new building production vs. retrofitting the existing stock, in the context of urban transformation interventions. The study integrates life cycle approaches by introducing the social components besides the economic and environmental ones. Firstly, a composite unidimensional (monetary) indicator calculation is illustrated. The sustainability components are internalized in the NPV calculation through a Discounted Cash-Flow Analysis (DCFA). Life Cycle Costing (LCC) and Life Cycle Assessment (LCA) are suggested to assess the economic and environmental impacts, and the Social Return on Investment (SROI) to assess the intervention’s extra-financial value. Secondly, a methodology based on multicriteria techniques is proposed. The Hierarchical Analytical Process (AHP) model is suggested to harmonize various performance indicators. Focus is placed on the criticalities emerging in both the methodological approaches, while highlighting the relevance of multidimensional approaches in decision-making processes and for supporting urban policies and urban resilience. Full article

The successful establishment of battery electric vehicles (BEVs) is strongly linked to criteria such as cost and range. In particular, the need for air conditioning strains battery capacities and limits the availability of BEVs. Thermal energy storage systems (TESs) open up alternative paths for heat and cold supply with excellent scalability and cost efficiency. Previous TES concepts have largely focused on heat during cold seasons, but storage-based air conditioning systems for all seasons are still missing. To fill this gap, a concept based on a Brayton cycle allowing heat and cold supply and, simultaneously, an output of electrical energy at times when no air conditioning is needed was investigated. Central thermal components include water-based cold storage and electrically heated, high-temperature, solid-medium storage, both with innovative TPMS structures and flexible operation managements. With transient simulation studies a system was identified with effective storage densities of up to 100 Wh/kg, reaching a constant heat and cold supply of 5 kW and 2.5 kW, respectively, over 41 min. In addition, the underlying cycle allows an electrical output of up to 1.7 kW during times of inactive air conditioning requirements. Compared to a reference system designed only for winter operation, the moderately lower storage densities are compensated by proportionately longer discharging times. By combining a compact and dynamic Brayton cycle with a TES in BEVs, a storage-based air conditioning system with high utilization potential and high operational flexibility was developed. In addition to further optimizations, the knowledge for TES solutions can also be transferred to today’s air conditioning systems, extending the solution space for storage-supported thermomanagement options in BEVs. Full article

Bio-based lubricants are rapidly gaining prominence as sustainable alternatives to petroleum-derived counterparts, driven by their inherent biodegradability, low ecotoxicity, and strong alignment with global environmental and regulatory imperatives. Despite their promising tribological properties, their widespread adoption continues to confront significant challenges, particularly related to oxidative and thermal instability, cold-flow behavior, and cost competitiveness in demanding high-performance applications. This comprehensive review critically synthesizes the latest advancements in bio-based lubricant technology, spanning feedstock innovations, sophisticated chemical modification strategies, and the development of advanced additive systems. Notably, recent formulations demonstrate remarkable performance enhancements, achieving friction reductions of up to 40% and contributing to substantial CO₂ emission reductions, ranging from 30 to 60%, as evidenced by comparative life-cycle assessments and energy efficiency studies. Distinguishing this review from existing literature, this study offers a unique, holistic perspective by integrally analyzing global market trends, industrial adoption dynamics, and evolving regulatory frameworks, such as the European Union Eco-Label and the U.S. EPA Vessel General Permit, alongside technological advancements. This study critically assesses emerging methodologies for tribological evaluation and benchmark performance across diverse, critical sectors including automotive, industrial, and marine applications. By connecting in-depth technical innovations with crucial socio-economic and environmental considerations, this paper not only identifies key research gaps but also outlines a pragmatic roadmap for accelerating the mainstream adoption of bio-based lubricants, positioning them as an indispensable cornerstone of sustainable tribology. Full article

Cadmium (Cd), a pervasive and highly phytotoxic metal pollutant, poses severe threats to agricultural productivity, ecosystem stability, and human health through its entry into the food chain. Plants have evolved intricate defense mechanisms, among which the strategic manipulation of nutrient elements emerges as a critical physiological and biochemical strategy for mitigating Cd stress. This comprehensive review delves deeply into the multifaceted roles of essential macronutrient elements (nitrogen, phosphorus, potassium, calcium, magnesium, sulfur), essential micronutrient elements (zinc, iron, manganese, copper) and non-essential beneficial elements (silicon, selenium) in modulating plant responses to Cd toxicity. We meticulously dissect the physiological, biochemical, and molecular underpinnings of how these nutrients influence Cd bioavailability in the rhizosphere, Cd uptake and translocation pathways, sequestration and compartmentalization within plant tissues, and the activation of antioxidant defense systems. Nutrient elements exert their influence through diverse mechanisms: competing with Cd for root uptake transporters, promoting the synthesis of complexes that reduce Cd mobility, stabilizing cell walls and plasma membranes to restrict apoplastic flow and symplastic influx, modulating redox homeostasis by enhancing antioxidant enzyme activities and non-enzymatic antioxidant pools, regulating signal transduction pathways, and influencing gene expression profiles related to metal transport, chelation, and detoxification. The complex interactions between nutrients themselves further shape the plant’s capacity to withstand Cd stress. Recent advances elucidating nutrient-mediated epigenetic regulation, microRNA involvement, and the role of nutrient-sensing signaling hubs in Cd responses are critically evaluated. Furthermore, we synthesize the practical implications of nutrient management strategies, including optimized fertilization regimes, selection of nutrient-efficient genotypes, and utilization of nutrient-enriched amendments, for enhancing phytoremediation efficiency and developing low-Cd-accumulating crops, thereby contributing to safer food production and environmental restoration in Cd-contaminated soils. The intricate interplay between plant nutritional status and Cd stress resilience underscores the necessity for a holistic, nutrient-centric approach in managing Cd toxicity in agroecosystems. Full article

Pachysandra terminalis (P. terminalis), a plant belonging to the Buxaceae family, is known as a great source of aminosteroid alkaloids. In a previous communication, we reported on the isolation of a variety of aminosteroids from P. terminalis, which presented interesting activity against the protozoan pathogens, Trypanosoma brucei rhodesiense and Plasmodium falciparum. In the present study, variations in the alkaloid profile of P. terminalis related to seasonal changes as well as differences between plant organs (leaves and twigs) and between plant populations were investigated to prioritize candidates for targeted isolation in further studies. For this purpose, sample material of P. terminalis was collected from the two nearby populations in monthly intervals over one year. The ethanolic (75%) extracts were analyzed using UHPLC/+ESI-QqTOF-MS/MS, and the resulting data converted to variables encoding the intensity of MS signals in particular m/z and retention time (t_R) intervals over the chromatographic runs. The very large and complex data matrix of these <t_R:m/z> variables was evaluated using multivariate data analysis, especially principal component analysis (PCA) and volcano plot analysis of t-test data. The results of these analyses, for the first time, allowed a holistic analysis of variation in the alkaloid profiles in P. terminalis organs over the vegetation period. The evaluation of the PCA scores and loadings plots of principal components 1 through 3, as well as of volcano plots, highlighted 25 different compounds, mostly identified as aminosteroid alkaloids, that were most relevant for the differences between leaves and twigs and between the two populations and mainly determined the changes in their chemical profiles over the vegetation period. Full article

Potato leaf diseases are recognized as a major threat to agricultural productivity and global food security, emphasizing the need for rapid and accurate detection methods. Conventional manual diagnosis is limited by inefficiency and susceptibility to bias, whereas existing automated approaches are often constrained by insufficient feature extraction, inadequate integration of multiple leaves, and poor generalization under complex field conditions. To overcome these challenges, a ResNet18-SAWF model was developed, integrating a self-attention mechanism with a multi-scale feature-fusion strategy within the ResNet18 framework. The self-attention module was designed to enhance the extraction of key features, including leaf color, texture, and disease spots, while the feature-fusion module was implemented to improve the holistic representation of multi-leaf structures under complex backgrounds. Experimental evaluation was conducted using a comprehensive dataset comprising both simple and complex background conditions. The proposed model was demonstrated to achieve an accuracy of 98.36% on multi-leaf images with complex backgrounds, outperforming baseline ResNet18 (91.80%), EfficientNet-B0 (86.89%), and MobileNet_V2 (88.53%) by 6.56, 11.47, and 9.83 percentage points, respectively. Compared with existing methods, superior performance was observed, with an 11.55 percentage point improvement over the average accuracy of complex background studies (86.81%) and a 0.7 percentage point increase relative to simple background studies (97.66%). These results indicate that the proposed approach provides a robust, accurate, and practical solution for potato leaf disease detection in real field environments, thereby advancing precision agriculture technologies. Full article

Shell-like housing structures for motors and compressors can be found in everyday products. Consumers significantly evaluate acoustic emissions during the first usage of products. Unpleasant sounds may raise concerns and cause complaints to be issued. A prevention strategy is a holistic acoustic design, which includes predicting the emitted sound power as part of end-of-line testing. The hybrid experimental-simulative sound power prediction based on laser scanning vibrometry (LSV) is ideal in acoustically harsh production environments. However, conducting vibroacoustic testing with laser scanning vibrometry is time-consuming, making it difficult to fit into the production cycle time. This contribution discusses how the time-consuming sampling process can be accelerated to estimate the radiated sound power, utilizing adaptive sampling. The goal is to predict the acoustic signature and its uncertainty from surface velocity data in seconds. Fulfilling this goal will enable integration into a product assembly unit and final acoustic quality control without the need for an acoustic chamber. The Gaussian process regression based on PyTorch 2.6.0 performed 60 times faster than the preliminary reference implementation, resulting in a regression estimation time of approximately one second for each frequency bin. In combination with the Equivalent Radiated Power prediction of the sound power, a statistical measure is available, indicating how the uncertainty of a limited number of surface velocity measurement points leads to predictions of the uncertainty inside the acoustical signal. An adaptive sampling algorithm reduces the prediction uncertainty in real-time during measurement. The method enables on-the-fly error analysis in production, assessing the risk of violating agreed-upon acoustic sound power thresholds, and thus provides valuable feedback to the product design units. Full article

Breast cancer (BC) is the most frequently diagnosed malignancy in women. Currently, BC is treated with a holistic and multidisciplinary approach from diagnostic, surgical, radio-oncological, and medical perspectives, and advances including in early detection and treatment methods have led to improved outcomes for patients in recent years. Yet, BC remains the second most common cause of cancer-related deaths among women and there is an array of gaps to achieve optimal care. To close gaps in cancer care, here we describe a collaborative Request For Proposals (RFP) framework supporting independent initiatives for metastatic breast cancer (MBC) patients and aiming at improving their quality of care. We set up a collaborative framework between Pfizer and Sharing Progress in Cancer Care (SPCC). Our model is based on an RFP system in which Pfizer and SPCC worked together ensuring the independence of the funded projects. We developed a three-step life cycle RFP. The collaborating framework of the project was based on an RFP with a USD 1.5 million available budget for funding independent grants made available from Pfizer and managed in terms of awareness, selection, and monitoring by SPCC. Our three-step model could be applicable and scalable to quality improvement (QI) initiatives that are devoted to tackling obstacles to reaching optimal care. Through this model, seven projects from five different European countries were supported. These projects covered a range of issues related to the experience of patients with MBC: investigator communication, information, and shared decision-making (SDM) practices across Europe; development, delivery, and evaluation of a scalable online educational program for nurses; assessment of disparities among different minority patient groups; development of solutions to improve compliance or adherence to therapy; an information technology (IT) solution to improve quality of life (QoL) of patients with MBC and an initiative to increase awareness and visibility of MBC patients. Overall, an average of 171 healthcare professionals (HCPs) per project and approximately 228,675 patients per project were impacted. We set up and describe a partnership model among different stakeholders within the healthcare ecosystem―academia, non-profit organizations, oncologists, and pharmaceutical companies―aiming at supporting independent projects to close gaps in the care of patients with MBC. By removing barriers at different layers, these projects contributed to the achievement of optimal care for patients with MBC. Full article

Anxiety disorders represent a significant global health challenge, yet a substantial treatment gap persists, motivating the development of scalable digital health solutions. This study investigates the potential of integrating passive physiological data from consumer wearable devices with subjective self-reported surveys to predict state–trait anxiety. Leveraging the multi-modal, longitudinal LifeSnaps dataset, which captured “in the wild” data from 71 participants over four months, this research develops and evaluates a machine learning framework for this purpose. The methodology meticulously details a reproducible data curation pipeline, including participant-specific time zone harmonization, validated survey scoring, and comprehensive feature engineering from Fitbit Sense physiological data. A suite of machine learning models was trained to classify the presence of anxiety, defined by the State–Trait Anxiety Inventory (S-STAI). The CatBoost ensemble model achieved an accuracy of 77.6%, with high sensitivity (92.9%) but more modest specificity (48.9%). The positive predictive value (77.3%) and negative predictive value (78.6%) indicate balanced predictive utility across classes. The model obtained an F1-score of 84.3%, a Matthews correlation coefficient of 0.483, and an AUC of 0.709, suggesting good detection of anxious cases but more limited ability to correctly identify non-anxious cases. Post hoc explainability approaches (local and global) reveal that key predictors of state anxiety include measures of cardio-respiratory fitness (VO₂Max), calorie expenditure, duration of light activity, resting heart rate, thermal regulation and age. While additional sensitivity analysis and conformal prediction methods reveal that the size of the datasets contributes to overfitting, the features and the proposed approach is generally conducive for reasonable anxiety prediction. These findings underscore the use of machine learning and ubiquitous sensing modalities for a more holistic and accurate digital phenotyping of state anxiety. Full article

Evaluating and enhancing system resilience is essential to strengthen the regional ability to external shocks and promote the synergistic development of environment, economy and society. Taking the Upper Yellow River (UYR) as an example, this paper constructed a resilience evaluation index system for the environment—economy—society (EES) composite system. A three-dimensional space vector model was built to calculate the resilience development index (RDI) of three subsystems and the composite system from 2009 to 2022. Pathways supporting high resilience levels of the composite system were examined using the fuzzy-set qualitative comparative analysis (fsQCA) method from a configuration perspective. The results revealed that (1) the RDI of three subsystems and the composite system in the UYR showed an increasing trend; relatively, the environment and economy subsystems were lower, and their RDI fluctuated between 0.01 and 0.06 for most cities. (2) The emergence of high resilience is not absolutely dominated by a single factor, but rather the interaction of multiple factors. To achieve high resilience levels, all the cities must prioritize both environmental protection and economic structure as core strategic pillars. The difference is that eastern cities need to further consider social development and life quality, while western cities need to consider social development, life quality, and social security. Other cities including Lanzhou, Baiyin, Tianshui, and Ordos should focus on social construction and social security. Exploring the interactive relationship between various influencing factors of the resilience of the composite system from a configuration perspective has to some extent promoted the transformation from a single contingency perspective to a holistic and multi-dimensional perspective. These findings provide policy recommendations for achieving sustainable development in the UYR and other ecologically fragile areas around the world. Full article

The global expansion of Korean language education has created an urgent need for scalable, objective, and consistent methods for assessing the writing skills of non-native (L2) learners. Traditional manual grading is resource-intensive and prone to subjectivity, while existing Automated Essay Scoring (AES) systems often struggle with the linguistic nuances of Korean and the specific error patterns of L2 writers. This paper introduces a novel hybrid AES system designed specifically for Korean L2 writing. The system integrates two complementary feature sets: (1) a comprehensive suite of conventional linguistic features capturing lexical diversity, syntactic complexity, and readability to assess writing form and (2) a novel semantic relevance feature that evaluates writing content. This semantic feature is derived by calculating the cosine similarity between a student’s essay and an ideal, high-proficiency reference answer generated by a Large Language Model (LLM). Various machine learning models are trained on the Korean Language Learner Corpus from the National Institute of the Korean Language to predict a holistic score on the 6-level Test of Proficiency in Korean (TOPIK) scale. The proposed hybrid system demonstrates superior performance compared to baseline models that rely on either linguistic or semantic features alone. The integration of the LLM-based semantic feature provides a significant improvement in scoring accuracy, more closely aligning the automated assessment with human expert judgments. By systematically combining measures of linguistic form and semantic content, this hybrid approach provides a more holistic and accurate assessment of Korean L2 writing proficiency. The system represents a practical and effective tool for supporting large-scale language education and assessment, aligning with the need for advanced AI-driven educational technology systems. Full article

Heating and cooling (H&C) account for nearly half of the EU’s energy consumption, with significant potential for decarbonization through renewable energy sources (RES) integrated in district heating and cooling (DHC) systems. This study evaluates the environmental and social impacts of RES-powered DHC solutions implemented in three European small-scale demo sites (Bucharest, Luleå, Córdoba) under the Horizon 2020 WEDISTRICT project. Using the Life Cycle Assessment (LCA) and Social Life Cycle Assessment (S-LCA) methodologies, the research compares baseline fossil-based energy scenarios with post-implementation renewable scenarios. Results reveal substantial greenhouse gas emission reductions (up to 67%) and positive environmental trade-offs, though increased mineral and metal resource use and site-specific impacts on water and land use highlight important sustainability challenges. Social assessments demonstrate improvements in gender parity, local employment, and occupational safety, yet reveal persistent issues in wage equity, union representation, and inclusion of vulnerable populations. The findings emphasize that while renewable DHC systems offer significant climate benefits, social sustainability requires tailored local strategies and robust governance to avoid exacerbating inequalities. This integrated environmental-social perspective underscores the need for holistic policies that balance technical innovation with equitable social outcomes to ensure truly sustainable energy transitions. Full article

Underwater wireless communication (UWC) is an emerging technology crucial for automating marine industries, such as offshore aquaculture and energy production, and military applications. It is a key part of the 6G vision of creating a hyperconnected world for extending connectivity to the underwater environment. Of the three main practicable UWC technologies (acoustic, optical, and radiofrequency), acoustic methods are best for far-reaching links, while optical is best for high-bandwidth communication. Recently, utilizing reconfigurable intelligent surfaces (RISs) has become a hot topic in terrestrial applications, underscoring significant benefits for extending coverage, providing connectivity to blind spots, wireless power transmission, and more. However, the potential for further research works in underwater RIS is vast. Here, for the first time, we conduct an extensive survey of state-of-the-art of RIS and metasurfaces with a focus on underwater applications. Within a holistic perspective, this survey systematically evaluates acoustic, optical, and hybrid RIS, showing that environment-aware channel switching and joint communication architectures could deliver holistic gains over single-domain RIS in the distance–bandwidth trade-off, congestion mitigation, security, and energy efficiency. Additional focus is placed on the current challenges from research and realization perspectives. We discuss recent advances and suggest design considerations for coupling hybrid RIS with optical energy and piezoelectric acoustic energy harvesting, which along with distributed relaying, could realize self-sustainable underwater networks that are highly reliable, long-range, and high throughput. The most impactful future directions seem to be in applying RIS for enhancing underwater links in inhomogeneous environments and overcoming time-varying effects, realizing RIS hardware suitable for the underwater conditions, and achieving simultaneous transmission and reflection (STAR-RIS), and, particularly, in optical links—integrating the latest developments in metasurfaces. Full article

This study presents a case study of a holistic, psychosocial hazard management intervention program in a project-based, remote workforce in the Australian construction industry. There is a dearth of research on targeted, integrative, multi-level wellbeing interventions, and we seek to address this gap. Given the high rates of psychological distress and suicide in construction, understanding these hazards and the responses needed to manage them is critical for prevention. Data were collected from workers before and after the implementation of an intervention using an empirically validated measure of the work environment underpinned by the job demands–resources framework to evaluate exposure to psychosocial hazards, and mental health indicators, including psychological distress and suicidal ideation. Results revealed that job demands and resources improved following the change initiative, and workers reported significantly lower levels of psychological distress compared to workers on similarly diverse remote sites. The findings highlight the need for targeted mental health interventions addressing specific workplace psychosocial risks that adopt a holistic approach to change across all levels of an organisation. The study contributes to a nuanced understanding of psychosocial risks in construction and informs strategies to mitigate mental health harms in high-risk occupational settings. Full article