Search Results (2,942)

Renewable energy stands as a pivotal solution to environmental concerns, prompting substantial research and development endeavors to promote its adoption and enhance energy efficiency. Despite the recognized environmental superiority of renewable energy systems, there is a lack of globally standardized indicators specifically focused on renewable energy efficiency. This study aims to develop and apply a non-parametric data envelopment analysis (DEA) indicator, termed the Renewable Energy Indicator (REI), to measure environmental performance at the national level and to identify differences in renewable energy efficiency across countries grouped by development status and income level. The REI incorporates new factors such as agricultural methane emissions (thousand metric tons of ${\mathrm{CO}}_2$ equivalent), PM2.5 air pollution exposure (µg/m³), and aspects related to electricity, including consumption (as % of total final energy consumption), production from renewable sources, excluding hydroelectric (kWh), and accessibility in rural and urban areas (% of population with access), aligning with the emerging paradigm outlined by the United Nations. By segmenting the REI into global, developmental, and income group classifications, this study conducts the Mann–Whitney U test and the Kruskal–Wallis H tests to identify variations in renewable energy efficiency among different country groups. Our findings reveal top-performing countries globally, highlighting both developed (e.g., Sweden) and developing nations (e.g., Costa Rica, Sri Lanka). Central and North European countries demonstrate high efficiency, while those facing political and economic instability perform poorly. Agricultural-dependent nations like Australia and Argentina exhibit lower REI due to significant methane emissions. Disparities between developed and developing markets underscore the importance of understanding distinct socio-economic dynamics for effective policy formulation. Comparative analysis across income groups informs specific strategies tailored to each category. Full article

We present a corrosion-resistant quadrupole ion trap mass spectrometer (QIT-MS) designed for trace detection of volatiles in sulfuric acid aerosols, with a specific focus on phosphine (PH₃). Here, we detail the gas calibration methodology using permeation tube technology for generating certified ppb-level PH₃/H₂S/CO₂ mixtures, and report results from mass spectra with sufficient resolution to distinguish isotopic envelopes that validate the detection of PH₃ at a concentration of 62 ppb. Fragmentation patterns for PH₃ and H₂S agree with NIST data, and signal-to-noise performance confirms ppb sensitivity over 2.6 h acquisition periods. We further assess spectral interferences from oxygen isotopes and propose a detection scheme based on isolated phosphorus ions (P⁺) to enable specific and interference-resistant identification of PH₃ and other reduced phosphorus species of astrobiological interest in Venus-like environments. This work extends the capabilities of QIT-MS for trace gas analysis in chemically aggressive atmospheric conditions. Full article

Driven by the global energy transition and China’s dual-carbon targets, Passive ultra-low-energy buildings are a key route for carbon reduction in the construction sector. This study addresses the high energy demand of office buildings and the limited suitability of current efficiency codes in the hot-summer/cold-winter, high-humidity zone of central and southern Anhui. Using multi-year climate records and energy-use surveys from five cities and one scenic area (2013–2024), we systematically investigate climate-adaptive passive-design strategies. Climate-Consultant simulations identify composite envelopes, external shading, and natural ventilation as the three most effective measures. Empirical evidence confirms that optimized envelope thermal properties significantly curb heating and cooling loads; a Huangshan office-building case validates the performance of the proposed passive measures, while analysis of a near-zero-energy demonstration project in Chuzhou yields a coordinated insulation-and-heat-rejection scheme. The results demonstrate that region-specific passive design can provide a comprehensive technical framework for ultra-low-energy buildings in transitional climates and thereby supporting China’s carbon-neutrality targets. Full article

The construction sector presently consumes about 40% of global energy and generates 36% of CO₂ emissions, making facade retrofits a priority for decarbonising buildings. This review clarifies how ventilated facades (VFs), wall assemblies that interpose a ventilated air cavity between outer cladding and the insulated structure, address that challenge. First, the paper categorises VFs by structural configuration, ventilation strategy and functional control into four principal families: double-skin, rainscreen, hybrid/adaptive and active–passive systems, with further extensions such as BIPV, PCM and green-wall integrations that couple energy generation or storage with envelope performance. Heat-transfer analysis shows that the cavity interrupts conductive paths, promotes buoyancy- or wind-driven convection, and curtails radiative exchange. Key design parameters, including cavity depth, vent-area ratio, airflow velocity and surface emissivity, govern this balance, while hybrid ventilation offers the most excellent peak-load mitigation with modest energy input. A synthesis of simulation and field studies indicates that properly detailed VFs reduce envelope cooling loads by 20–55% across diverse climates and cut winter heating demand by 10–20% when vents are seasonally managed or coupled with heat-recovery devices. These thermal benefits translate into steadier interior surface temperatures, lower radiant asymmetry and fewer drafts, thereby expanding the hours occupants remain within comfort bands without mechanical conditioning. Climate-responsive guidance emerges in tropical and arid regions, favouring highly ventilated, low-absorptance cladding; temperate and continental zones gain from adaptive vents, movable insulation or PCM layers; multi-skin adaptive facades promise balanced year-round savings by re-configuring in real time. Overall, the review demonstrates that VFs constitute a versatile, passive-plus platform for low-carbon buildings, simultaneously enhancing energy efficiency, durability and indoor comfort. Future advances in smart controls, bio-based materials and integrated energy-recovery systems are poised to unlock further performance gains and accelerate the sector’s transition to net-zero. Emerging multifunctional materials such as phase-change composites, nanostructured coatings, and perovskite-integrated systems also show promise in enhancing facade adaptability and energy responsiveness. Full article

This work presents a multiscale, microstructure-aware framework for predicting fatigue strength distributions in additively manufactured (AM) alloys—specifically, laser powder bed fusion (L-PBF) AlSi10Mg and Ti-6Al-4V—by integrating density functional theory (DFT), instrumented indentation, and Bayesian inference. The methodology leverages principles common to all 3D printing (additive manufacturing) processes: layer-wise material deposition, process-induced defect formation (such as porosity and residual stress), and microstructural tailoring through parameter control, which collectively differentiate AM from conventional manufacturing. By linking DFT-derived cohesive energies with indentation-based modulus measurements and a MAP-based statistical model, we quantify the effect of additive-manufactured microstructural heterogeneity on fatigue performance. Quantitative validation demonstrates that the predicted fatigue strength distributions agree with experimental high-cycle and very-high-cycle fatigue (HCF/VHCF) data, with posterior modes and 95 % credible intervals of ${\widehat{\sigma }}_f^{\mathrm{AlSi}10\mathrm{Mg}}={86}_{-7}^{+8}\mathrm{MPa}$ and ${\widehat{\sigma }}_f^{\mathrm{Ti}–6\mathrm{Al}–4\mathrm{V}}={115}_{-9}^{+10}\mathrm{MPa}$, respectively. The resulting Woehler (S–N) curves and Paris crack-growth parameters envelop more than 92 % of the measured coupon data, confirming both accuracy and robustness. Furthermore, global sensitivity analysis reveals that volumetric porosity and residual stress account for over 70 % of the fatigue strength variance, highlighting the central role of process–structure relationships unique to AM. The presented framework thus provides a predictive, physically interpretable, and data-efficient pathway for microstructure-informed fatigue design in additively manufactured metals, and is readily extensible to other AM alloys and process variants. Full article

Environmental costs (carbon emissions) have come with China’s economic rise, and its forestry sector now faces difficulties in maintaining both its profit and the health of its ecosystems. This study assesses the impact of carbon emissions on forestry efficiency and total factor productivity (TFFP) in China’s 31 provinces between 2001 and 2021. Using the data envelopment analysis (DEA) model through the slack-based measure (SBM framework) and Malmquist–Luenberger index (MLI), we examine the efficiency and productivity growth of forestry, both with and without accounting for carbon emissions. The study reveals that when carbon emissions are not taken into account, traditional measures of productivity tend to overstate both efficiency and total factor forestry productivity (TFFP) growth, resulting in an average of 7.7 percent higher efficiency and 1.6 percent of additional TFFP growth per year. If we compare the regions, coast provinces with stricter technical regulations have improved efficiency in usage, but places like Tibet and Qinghai, with more vulnerable ecosystems, endure harsher consequences. Regardless of incorporating bad output into the TFFP estimation, China’s growth in forestry productivity primarily depends on efficiency change (EC) rather than technological change (TC). Full article

This paper presents a methodology for extracting and simulating arterial pulse waveform signals from Fabry–Perot interferometric measurements, emphasizing a practical approach for noninvasive cardiovascular assessment. A key novelty of this work is the presentation of a complete Python-based processing pipeline, which is made publicly available as open-source code on GitHub (git version 2.39.5). To the authors’ knowledge, no such repository for demodulating these specific interferometric signals to obtain a raw arterial pulse waveform previously existed. The proposed system utilizes accessible Python-based preprocessing steps, including outlier removal, Butterworth high-pass filtering, and min–max normalization, designed for robust signal quality even in settings with common physiological artifacts. Key features such as the rate of change, the Hilbert transform of the rate of change (envelope), and detected extrema guide the signal reconstruction, offering a computationally efficient pathway to reveal its periodic and phase-dependent dynamics. Visual analyses highlight amplitude variations and residual noise sources, primarily attributed to sensor bandwidth limitations and interpolation methods, considerations critical for real-world deployment. Despite these practical challenges, the reconstructed arterial pulse waveform signals provide valuable insights into arterial motion, with the methodology’s performance validated on measurements from three subjects against synchronized ECG recordings. This demonstrates the viability of Fabry–Perot sensors as a potentially cost-effective and readily implementable tool for noninvasive cardiovascular diagnostics. The results underscore the importance of precise yet practical signal processing techniques and pave the way for further improvements in interferometric sensing, bio-signal analysis, and their translation into clinical practice. Full article

The increasing demand for hydrogen has made it a promising alternative for decarbonizing industries and reducing CO₂ emissions. Although mainly produced through the gray pathway, the integration of carbon capture and storage (CCS) reduces the CO₂ emissions. This study presents a sustainability method that uses flare gas for hydrogen production through steam methane reforming (SMR) with CCS, supported by a techno-economic analysis. Data Envelopment Analysis (DEA) was used to evaluate the oil company’s efficiency, and inverse DEA/sensitivity analysis identified maximum flare gas reduction, which was modeled in Aspen HYSYS V14. Subsequently, an economic evaluation was performed to determine the levelized cost of hydrogen (LCOH) and the cost–benefit ratio (CBR) for Nigeria. The CBR results were 2.15 (payback of 4.11 years with carbon credit) and 1.96 (payback of 4.55 years without carbon credit), indicating strong economic feasibility. These findings promote a practical approach for waste reduction, aiding Nigeria’s transition to a circular, low-carbon economy, and demonstrate a positive relationship between lean and green strategies in the petroleum sector. Full article

Background/Objectives: Cutaneous melanoma is one of the aggressive forms of skin cancer originating from melanocytes. The high incidence of melanoma metastasis continues to rise, partly due to the complex nature of the molecular mechanisms driving its progression. While melanomas generally arise from melanocytes, we investigated whether aberrant keratinocyte differentiation pathways—like cornified envelope formation—discriminate primary melanoma from metastatic melanoma, revealing novel biomarkers in progression. Methods: In the present study, we retrieved four datasets (GSE15605, GSE46517, GSE8401, and GSE7553) associated with primary and metastatic melanoma tissues and identified differentially expressed genes (DEGs). Thereafter, an integrated meta-analysis and functional enrichment analysis of the DEGs were performed to evaluate the molecular mechanisms involved in melanoma metastasis, such as immune cell deconvolution and protein-protein interaction (PPI) network construction. Hub genes were identified based on four topological methods, including ‘Betweenness’, ‘MCC’, ‘Degree’, and ‘Bottleneck’. We validated the findings using the TCGA-SKCM cohort. Drug-gene interactions were evaluated using the DGIdb, whereas structural druggability was assessed using the ProteinPlus and AlphaFold databases. Results: We identified a total of eleven hub genes associated with melanoma progression. These included members of the keratin gene family (e.g., KRT5, KRT6A, KRT6B, etc.). Except for the gene CDH1, all the hub genes were downregulated in metastatic melanoma tissues. From a prognostic perspective, these hub genes were associated with poor prognosis (i.e., unfavorable). Using the Human Protein Atlas (HPA), immunohistochemistry evaluation revealed mostly undetected levels in metastatic melanoma. Additionally, the cornified envelope formation was the most enriched pathway, with a gene ratio of 17/33. The tumor microenvironment (TME) of metastatic melanomas was predominantly enriched in NK cell–associated signatures. Finally, several hub genes demonstrated favorable druggable potential for immunotherapy. Conclusions: Through integrated meta-analysis, this study identifies transcriptional, immunological, and structural pathways to melanoma metastasis and highlights keratin family genes as promising biomarkers for therapeutic targeting. Full article

As a core component of machining tools and vehicles, the load-bearing and transmission performance of rolling bearings is directly related to product processing quality and driving safety, highlighting the critical importance of fault detection. To address the nonlinearity, non-stationary modulation, and low signal-to-noise ratio (SNR) observed in bearing vibration signals, we propose a fault feature extraction method based on spectral kurtosis and Hilbert envelope demodulation. First, spectral kurtosis is employed to determine the center frequency and bandwidth of the signal adaptively, and a bandpass filter is constructed to enhance the characteristic frequency components. Subsequently, the envelope spectrum is extracted through the Hilbert transform, allowing for the precise identification of fault characteristic frequencies. In the fault diagnosis stage, a multidimensional feature vector is formed by combining the kurtosis index with the amplitude ratios of inner/outer race characteristic frequencies, and fault pattern classification is accomplished using a Least-Squares Support Vector Machine (LS-SVM). To evaluate the effectiveness of the proposed method, experiments were conducted on the bearing datasets from Case Western Reserve University (CWRU) and the Machine Failure Prevention Technology (MFPT) Society. The experimental results demonstrate that the proposed method surpasses other comparative approaches, achieving identification accuracies of 95% and 100% for the CWRU and MFPT datasets, respectively. Full article

Thermal energy storage plays a vital role in enhancing the efficiency of energy systems, particularly in building applications. Phase change materials (PCMs) have gained significant attention as a passive solution for energy management within building envelopes. This study examines the thermal performance of encapsulated PCMs integrated into bricks as a passive cooling method, taking into account the outdoor climate conditions to enhance indoor thermal comfort throughout summer and winter seasons. A computational fluid dynamics (CFDs) analysis is performed to compare three configurations: a conventional brick, a brick with a single PCM layer, and a brick with three PCM layers. Results indicate that the three-layer PCM configuration provides the most effective thermal regulation, reducing peak indoor temperature fluctuations by up to 4 °C in summer and stabilizing indoor temperature during winter. Also, the second and third PCM layers exhibit minimal latent heat absorption, with their liquid fractions indicating that melting does not occur. As a result, these layers primarily serve as thermal insulation—limiting heat ingress in summer and reducing heat loss in winter. During summer, the absence of the first PCM layer in the single-layer configuration leads to faster thermal penetration, causing the brick to reach peak temperatures approximately two hours earlier in the afternoon and increasing the temperature by about 5 °C. Full article

Wearable laser Doppler devices are susceptible to complex noise interferences, such as Gaussian white noise, baseline drift, and motion artifacts, with motion artifacts significantly impacting clinical diagnostic accuracy. Addressing the limitations of existing denoising methods—including traditional adaptive filtering that relies on prior noise information, modal decomposition techniques that depend on empirical parameter optimization and are prone to modal aliasing, wavelet threshold functions that struggle to balance signal preservation with smoothness, and the high computational complexity of deep learning approaches—this paper proposes an ISSA-VMD-AWPTD denoising algorithm. This innovative approach integrates an improved sparrow search algorithm (ISSA), variational mode decomposition (VMD), and adaptive wavelet packet threshold denoising (AWPTD). The ISSA is enhanced through cubic chaotic mapping, butterfly optimization, and sine–cosine search strategies, targeting the minimization of the envelope entropy of modal components for adaptive optimization of VMD’s decomposition levels and penalty factors. A correlation coefficient-based selection mechanism is employed to separate target and mixed modes effectively, allowing for the efficient removal of noise components. Additionally, an exponential adaptive threshold function is introduced, combining wavelet packet node energy proportion analysis to achieve efficient signal reconstruction. By leveraging the rapid convergence property of ISSA (completing parameter optimization within five iterations), the computational load of traditional VMD is reduced while maintaining the denoising accuracy. Experimental results demonstrate that for a 200 Hz test signal, the proposed algorithm achieves a signal-to-noise ratio (SNR) of 24.47 dB, an improvement of 18.8% over the VMD method (20.63 dB), and a root-mean-square-error (RMSE) of 0.0023, a reduction of 69.3% compared to the VMD method (0.0075). The processing results for measured human blood flow signals achieve an SNR of 24.11 dB, a RMSE of 0.0023, and a correlation coefficient (R) of 0.92, all outperforming other algorithms, such as VMD and WPTD. This study effectively addresses issues related to parameter sensitivity and incomplete noise separation in traditional methods, providing a high-precision and low-complexity real-time signal processing solution for wearable devices. However, the parameter optimization still needs improvement when dealing with large datasets. Full article

The European Union’s (EU) Common Agricultural Policy aims to promote sustainable farming practices that ensure the responsible use of natural resources, safeguard biodiversity, and uphold higher animal welfare standards. One pathway to achieving these objectives is through the encouragement of extensive farming. However, the dairy sector in EU countries as well as in Lithuania has shown a clear trend toward intensification. The aim of this study was to assess the technical efficiency (TE) of dairy farms employing extensive and intensive technologies. TE was evaluated using Data Envelopment Analysis (DEA) combined with meta-frontier analysis, which accounts for technological heterogeneity. Prior to the efficiency estimation, farms were grouped into two distinct categories—intensive and extensive—using the k-means clustering algorithm. The empirical results show that extensive dairy farms in Lithuania are smaller in land area and livestock units, rely more on internal resources, and exhibit lower productivity compared to intensive farms. Intensive farms achieved higher technical efficiency, narrower technological gaps, and more optimal scale efficiency, indicating superior resource management. The weaker performance of extensive farms is attributed to both less advanced technologies and production inefficiencies. Full article

This paper presents a comprehensive investigation to evaluate the impacts of air cavities between existing walls and insulated panels on the overall R-values of the retrofitted building envelope systems, addressing a key challenge in exterior envelope retrofitting. The effects of several factors are considered including the air cavity thickness (ranging from 0.1 cm to 5 cm), airflow velocity (varying between 0.1 m/s and 1 m/s), and surface emissivity (set between 0.1 and 0.9), in addition to the thickness of the insulated panels (ranging from 1 cm to 7 cm). It is found that any increase in the air cavity thickness increases the overall R-values of the building envelope assemblies when air is trapped within the sealed cavity. However, when air convection is prevalent, the overall R-value of the retrofitted walls decreases with any increase in air velocity and air cavity thickness. For sealed air cavities, the analysis results show a 9% improvement in R-value of the retrofitted walls. However, the R-value of retrofitted walls with unsealed air cavities can degrade by 76% and 81% for natural and forced air flows, respectively. Emissivity adjustment is found to be the most effective in improving the thermal performance of building envelopes with sealed air cavities, increasing the R-value of retrofitted walls by 13.6% when reduced from 0.9 to 0.1. Full article

Problem: Agriculture in India faces pressing challenges related to water scarcity, excessive pesticide use, and inefficient energy consumption, impacting both economic sustainability and environmental health. Methodology: This study integrates Life Cycle Assessment (LCA), Data Envelopment Analysis (DEA), Intelligent Management Models (IMMs), and Multi-Criteria Decision Analysis (MCDA) to assess the economic and environmental benefits of UAV-based spraying in Indian agriculture. Data were collected from UAV service providers and field trials in Punjab, Haryana, and Rajasthan. Results: UAV spraying achieved a 70% reduction in water use, 40% reduction in pesticide consumption, and a 50% reduction in CO₂ emissions compared to conventional spraying. DEA results showed higher efficiency scores for UAVs, while IMM optimization achieved 95% pesticide coverage and reduced drift by 80%. Implications: MCDA ranked government subsidies as the most effective policy intervention. These findings support UAV spraying as a viable, scalable solution for climate-smart agriculture in India, offering both productivity and sustainability gains. Full article