Search Results (3,410)

Borehole sealing materials have drawn significant research attention for their applications in mine disaster prevention, efficient utilization of coalbed methane resources and green mine construction. However, it is still an enormous challenge to simultaneously achieve sealing materials with lower material consumption, lower expense, and lower labor intensity for high-performance long-term borehole sealing. Meanwhile, drilling cuttings (DC) possess large production amounts, low granularity, a large workload for cleaning out the alley, high labor intensity, and high transportation cost. Herein, a composite with universal applicability to DC has been developed, which can be combined with different DC to produce a low-cost sealing material with adjustable strength, fulfilling the sealing requirements of various boreholes. The properties of the sealing material can be adjusted as required by regulating the water/cement ratio and DC content to meet the sealing requirements of different boreholes. Consequently, the DC-derived materials, featuring adjustable strengths and lower usage, can reduce cement usage, material costs, and labor intensity dramatically, displaying great promise in high-performance borehole sealing, coalbed methane extraction and utilization, timely mining waste reutilization, gas disaster prevention, and green mine construction. Full article

The shadow economy weakens fiscal sustainability, hampers the financing of public goods, and impedes the achievement of sustainable development goals. The informal sector remains a persistent challenge for policymakers, as it distorts competition, reduces transparency, and undermines the effectiveness of economic and fiscal policies. The aim of this article is to identify the key factors determining the size of the shadow economy in European Union countries and to provide policy-relevant insights. The analysis covers data on the share of the informal economy in GDP and macroeconomic variables such as GDP per capita, consumer price index, average wages, household consumption, government expenditure, and unemployment, as well as indicators of digital development in society and the economy (DESI, IDT), the share of cashless transactions in GDP, and information on the implementation of digital tax administration tools and restrictions on cash payments. Five hypotheses (H1–H5) are formulated concerning the effects of income growth, labour market conditions, digitalisation, cashless payments, and tax administration tools on the shadow economy. The research question addresses which factors—macroeconomic conditions, economic and social digitalisation, payment structures, and fiscal innovations in tax administration—play the most significant role in determining the size of the shadow economy in EU countries and whether these mechanisms have broader implications for fiscal sustainability and sustainable development. The empirical strategy is based on multilevel models with countries as clusters, complemented by correlation and comparative analyses. The results indicate that the most significant factor in limiting the size of the shadow economy is the level of GDP per capita and its growth, whereas the impact of card payments appears to be superficial, reflecting overall increases in wealth. Higher wages, household consumption, and digital development as measured by the DESI also play an important role. The implementation of digital solutions in tax administration, such as SAF-T or e-PIT/pre-filled forms, along with restrictions on cash transactions, can serve as complementary measures. The findings suggest that sustainable strategies to reduce the shadow economy should combine long-term economic growth with digitalisation and improved tax administration, which may additionally foster the harmonisation of economic systems and support sustainable development. Full article

The 2020 Europe Strategy was designed as a comprehensive framework to promote smart, sustainable and inclusive growth in the European Union (EU), particularly emphasising the ‘20/20/20’ targets related to climate protection and energy policy. This study provides an ex post evaluation of the extent to which the strategy’s objectives were achieved in the member states of the EU in the period 2010–2020. The analysis is based on Eurostat data and uses Hellwig’s multidimensional comparative analysis to construct a synthetic indicator of progress. The results show that EU countries have made significant advances in reducing greenhouse gas emissions and increasing the share of renewable energy in gross final energy consumption, with Sweden and Finland identified as leaders, while Malta and Hungary lagged behind. Primary energy consumption overall decreased, although only a minority of the member states reached the planned thresholds. Progress was less evident in research and development (R&D) expenditure, where the average value of the EU remained below the 3% GDP target, and strong disparities persisted between innovation leaders and weaker performers. Improvements in higher education attainment were observed, contributing to the long-term goal of a knowledge-based economy, although labour market difficulties, especially among young people, remained unresolved. The findings suggest that, although the Strategy contributed to tangible progress in several areas, uneven achievements among member states limited its overall effectiveness. The study is limited by the reliance on aggregate statistical data and a single methodological approach. Future research should extend the analysis to longer time horizons, include qualitative assessments of national policies, and address implications for the implementation of the European Green Deal and subsequent EU development strategies. Full article

Intelligent transportation systems (ITS) play a crucial role in building sustainable and resilient urban mobility by improving traffic efficiency, reducing energy consumption, and lowering emissions. The integration of IoT technologies, particularly long-range low-power networks such as LoRaWAN, enables energy-efficient communication between vehicles and road infrastructure, supporting the sustainability goals of smart cities. However, the widespread deployment of IoT devices also introduces significant cybersecurity risks that may compromise the safety, reliability, and long-term sustainability of transportation systems. To address this challenge, we propose a method for generating synthetic network data that simulates normal traffic and DDoS attacks by randomly selecting distribution parameters for features like packets per second and unique device addresses, enabling evaluation of machine learning algorithms (e.g., Gradient Boosting, Random Forest, SVM, XGBoost) using F1-score and AUC metrics in a controlled environment. By enhancing cybersecurity and resilience in ITS, our research contributes to the development of safer, more energy-efficient, and sustainable transportation infrastructures. Full article

Hydraulic fracturing is a core stimulation technology for enhancing hydrocarbon production. However, it faces significant technical bottlenecks in unconventional reservoirs. These bottlenecks include poor adaptability to high-temperature and high-salinity environments, water-sensitive formation damage, and insufficient long-term fracture conductivity. Nanotechnology leverages unique properties of nanomaterials, such as surface effects, quantum size effects, and designability. Nanotechnology offers systematic solutions for optimizing fracturing fluids, enhancing proppant performance, and innovating waterless fracturing techniques. This review outlines the current status of fracturing technology, exploring the role of nanoparticles in improving fluid rheology, proppant strength, and interface regulation, and discusses future challenges. Studies show that nanomodified fracturing fluids can increase high-temperature viscosity retention by over 300%. Meanwhile, waterless fracturing reduces water consumption by 80%. Despite challenges in particle agglomeration and cost, nanotechnology demonstrates significant potential in boosting recovery and reducing environmental impact. Nanotechnology is positioned as a transformative technology for future unconventional resource development. Full article

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

This study examines the alignment between the European Union’s climate policy rhetoric and the actual fossil fuel consumption behaviours of its Member States. By combining long-term and short-term time-series data with machine learning classification techniques, the analysis captures dynamic national energy trends and decarbonisation signals. Key innovations include the use of slope acceleration metrics and the identification of label reversals to detect volatility, acceleration, or stagnation in transition trajectories. The results show that, while some countries such as France and Denmark demonstrate consistent structural progress, others show deceleration or reversal, particularly in the use of gas and liquid fuels. This indicates that the relationship between EU-level policy ambition and national implementation is asymmetric and conditionally aligned. This study concludes that ongoing empirical monitoring and targeted diagnostics are essential to prevent conflating symbolic commitments with material change, and provides practical insights for improving climate policy accountability and adaptability across the EU. Full article

Caffeine is one of the most widely consumed psychoactive substances globally and is a common component of daily diets, particularly among women of reproductive age. Numerous in vitro and in vivo studies have indicated potential adverse effects of prenatal caffeine exposure, including disturbances in fetal growth, metabolic dysregulation, organ malformations, and neurodevelopmental alterations. These findings suggest that caffeine may influence multiple physiological pathways during gestation, including epigenetic modifications and metabolic programming. However, evidence from human studies remains heterogeneous and often inconclusive. Recent cohort studies and meta-analyses have reported that moderate maternal caffeine intake is not significantly associated with increased risks of gestational diabetes mellitus, gestational hypertension, or preeclampsia, although higher intake levels have been linked to anemia, preterm birth, and low birth weight in some populations. Furthermore, emerging data suggest potential associations between prenatal caffeine exposure and early neurodevelopmental outcomes, including behavioral changes, subtle structural brain differences, and alterations in offspring metabolic health and obesity risk. Despite these findings, the magnitude and clinical relevance of these effects remain uncertain, partly due to variability in caffeine sources, dosages, study designs, and reliance on self-reported intake. This review aims to synthesize current evidence on maternal caffeine consumption, its impact on pregnancy complications, fetal development, and long-term child health outcomes. By integrating experimental and clinical data, the study provides a comprehensive overview that may assist clinicians and healthcare professionals in counseling pregnant women regarding caffeine intake and potential risks. Full article

Municipal solid waste (MSW) refers to solid and semi-solid waste generated during human production and daily activities. The process of incinerating such waste, known as municipal solid waste incineration (MSWI), serves as a critical method for reducing waste volume and recovering resources. Automatic online recognition of flame combustion status during MSWI is a key technical approach to ensuring system stability, addressing issues such as high pollution emissions, severe equipment wear, and low operational efficiency. However, when manually selecting optimized features and hyperparameters based on empirical experience, the MSWI flame combustion state recognition model suffers from high time consumption, strong dependency on expertise, and difficulty in adaptively obtaining optimal solutions. To address these challenges, this article proposes a method for constructing a flame combustion state recognition model optimized based on reinforcement learning (RL), long short-term memory (LSTM), and parallel differential evolution (PDE) algorithms, achieving collaborative optimization of deep features and model hyperparameters. First, the feature selection and hyperparameter optimization problem of the ViT-IDFC combustion state recognition model is transformed into an encoding design and optimization problem for the PDE algorithm. Then, the mutation and selection factors of the PDE algorithm are used as modeling inputs for LSTM, which predicts the optimal hyperparameters based on PDE outputs. Next, during the PDE-based optimization of the ViT-IDFC model, a policy gradient reinforcement learning method is applied to determine the parameters of the LSTM model. Finally, the optimized combustion state recognition model is obtained by identifying the feature selection parameters and hyperparameters of the ViT-IDFC model. Test results based on an industrial image dataset demonstrate that the proposed optimization algorithm improves the recognition performance of both left and right grate recognition models, with the left grate achieving a 0.51% increase in recognition accuracy and the right grate a 0.74% increase. Full article

The equilibrium between electricity demand and consumption is vital to ensure the stability of the transmission and distribution systems grid (TS & DS) and to ensure the stable operation of the electrical system. The aim of this review study is to highlight the current legislative and technical situation and the possibilities for managing peak loads, decentralization, sharing, storage, and sale of electricity generated from renewable sources in Slovakia. The European Union′s (EU) goal of achieving carbon neutrality by 2050 and a minimum of 42.5% renewable energy consumption by 2030 brings with it obligations for individual member states. These are transposed into national strategies. The current share of renewable sources in Slovakia is approximately 24% and the EU target by 2030 is probably unrealistic. Water resources are practically exhausted; other possibilities for increasing the share of renewable energy sources (RES) are in photovoltaics, wind, and thermal sources. Due to long-term geographical and historical development, electricity production in Slovakia is based on large-scale solutions. The move towards decentralization requires legislative and technical support. The review article examines the possibilities of increasing the share of RES and energy sharing in Slovakia, and examines the legislative, economic, and social barriers to their wider application. At the same time as the share of renewable sources in electricity generation increases, the article examines and presents solutions capable of ensuring the stability of electricity networks across Europe. The study formulates diversified strategies at the distribution network level and the consumer and building levels, and identifies physical (various types of electricity storage, electromobility, electricity liquidators) and virtual (electricity sharing, energy communities, virtual batteries) solutions. In conclusion, it defines the necessary changes in the legislative, technical, social, and economic areas for the most optimal improvement of the situation in the area of increasing the share of RES, supporting the decentralization of the electric power industry, and sharing electricity in Slovakia, also based on experience and good examples from abroad. Full article

Prefabrication in a controlled factory setting may improve the energy performance of modular buildings compared to traditional site-built facilities. However, few studies report empirical evidence to support this premise in full-scale operational buildings. Since energy efficiency standards in the United States are driven by building code, the compliance path chosen and field verification through site inspection, an investigation of how site-built and modular projects satisfy code requirements is critical to understanding long-term energy consumption. Therefore, this study investigated and compared Energy Code Compliance (ECC) among 55 commercial multifamily buildings (25 modular and 30 site-built) in four American Society of Heating, Refrigerating and Air-Conditioning Engineers climate zones (3B, 3C, 4A and 4C). For climate zone 3, ECC analyses indicated that modular slightly exceeded site-built construction. For zone 4, site-built construction slightly exceeded modular. Nearly all buildings met or exceeded the prescriptive energy code requirements for each climate zone regardless of whether a performance or trade-off compliance path was utilized. Field observations suggest that envelope construction quality in modular buildings could be higher. Results provide insights for researchers exploring energy use in buildings, as well as the basis for a nuanced understanding of normalized operational energy consumption in an ongoing longitudinal study of the same 55 multifamily buildings. Full article

This study evaluates media-level filtration behaviour and short-term fuel consumption outcomes for five spin-on lubricating oil filters operated under real driving conditions at high altitude. To improve interpretability, filters are reported using parameter-based identifiers (media descriptors and equivalent circular diameter, ECD) rather than internal codes. Pore-scale morphology was quantified by microscopy and expressed as ECD, and bulk fluid cleanliness was summarised using ISO 4406 codes. Trials were conducted over representative urban and extra-urban routes at altitude; fuel consumption was analysed using ANCOVA. The results indicated clear media-level differences (tighter pore envelopes and cleaner ISO codes, particularly for two OEM units). However, fuel-consumption differences were not statistically significant (ANCOVA, p = 0.29). Accordingly, findings are reported as short-term cleanliness and media characterisation under high-altitude duty rather than durability or efficiency claims. The parameter-based framing clarifies trade-offs across metrics and avoids over-generalisation from brand or part numbers. The work highlights the value of ECD as a comparative pore metric and underscores limitations of microscopy/cleanliness data for inferring engine wear or long-term consumption. Future work will incorporate formal multi-pass testing (ISO 4548-12), direct differential-pressure instrumentation, used-oil viscosity tracking, and wear-metal spectrometry to enable cross-vendor benchmarking and causal interpretation. Findings are presented as short-term cleanliness and media characterisation; no durability claims are made in the absence of direct wear measurements. Full article

Excessive energy consumption of communication protocols in IoT/IIoT systems constitutes one of the key constraints for the operational longevity of remote sensor nodes, where radio transmission often incurs higher energy costs than data acquisition or local computation. Previous studies have remained fragmented, typically focusing on selected technologies or specific layers of the communication stack, which has hindered the development of comparable quantitative metrics across protocols. The aim of this study is to design and validate a unified evaluation framework enabling consistent assessment of both wired and wireless protocols in terms of energy efficiency, reliability, and maintenance costs. The proposed approach employs three complementary research methods: laboratory measurements on physical hardware, profiling of SBC devices, and simulations conducted in the COOJA/Powertrace environment. A Unified Comparative Method was developed, incorporating bilinear interpolation and weighted normalization, with its robustness confirmed by a Spearman rank correlation coefficient exceeding 0.9. The analysis demonstrates that MQTT-SN and CoAP (non-confirmable mode) exhibit the highest energy efficiency, whereas HTTP/3 and AMQP incur the greatest energy overhead. Results are consolidated in the ICoPEP matrix, which links protocol characteristics to four representative RS-IoT scenarios: unmanned aerial vehicles (UAVs), ocean buoys, meteorological stations, and urban sensor networks. The framework provides well-grounded engineering guidelines that may extend node lifetime by up to 35% through the adoption of lightweight protocol stacks and optimized sampling intervals. The principal contribution of this work is the development of a reproducible, technology-agnostic tool for comparative assessment of IoT/IIoT communication protocols. The proposed framework addresses a significant research gap in the literature and establishes a foundation for further research into the design of highly energy-efficient and reliable IoT/IIoT infrastructures, supporting scalable and long-term deployments in diverse application environments. Full article

This study investigates the determinants of customers’ perception of business model innovation (BMI) and its impact on customer satisfaction (CS), customer loyalty (CL), and firm sustainability (FS) within Ecuadorian family economic groups (EFEGs). It also examines the moderating role of perceived BMI in the relationships between CS, CL, and FS. Data were collected through an online survey yielding 342 valid responses, using a structured instrument that included socio-demographic variables, perceived EFEG characteristics, and nine validated constructs. Reliability and validity were corroborated through exploratory and confirmatory factor analyses, while structural equation modeling (SEM) and multiple regression analyses were employed to test the proposed relationships. The results reveal that socially responsible consumption (SRC), technological/digital customer skills (TCS), value creation innovativeness (VCrI), value proposition innovativeness (VPI), and value capture innovativeness (VCI) significantly influence customers’ perception of BMI. In turn, BMI positively influences CS, CL, and FS, and moderates the relationships between CS and FS, and CL and FS, though it does not significantly moderate the CS–CL relationship. These findings are consistent with previous research on European family firms, emphasizing the relevance of innovation capabilities, entrepreneurial orientation, and socioemotional wealth in enhancing adaptability and performance in family-owned businesses. This study contributes novel empirical evidence on BMI in the context of an emerging economy dominated by family firms. It underscores BMI as a dynamic capability crucial for fostering customer engagement, improving competitiveness, and ensuring long-term sustainability. Managerial implications suggest that EFEG managers should prioritize digital integration, service innovation, and transparency to strengthen customer trust and loyalty. Future research should broaden the scope to include other Latin American contexts, integrate internal organizational perspectives, and explore intergenerational dynamics and digital transformation processes to deepen understanding of BMI in family business ecosystems. Full article

The increasing adoption of electric delivery fleets introduces significant challenges related to uneven energy utilization and suboptimal scheduling efficiency. Vehicle-to-Vehicle (V2V) energy sharing presents a promising solution, but its effectiveness critically depends on precise matching and co-optimization within dynamic urban traffic environments. This paper proposes a hierarchical optimization framework to minimize total fleet operational costs, incorporating a comprehensive analysis that includes battery degradation. The core innovation of the framework lies in coupling high-level path planning with low-level real-time speed control. First, a high-fidelity energy consumption surrogate model is constructed through model predictive control simulations, incorporating vehicle dynamics and signal phase and timing information. Second, the spatiotemporal longest common subsequence algorithm is employed to match the spatio-temporal trajectories of energy-provider and energy-consumer vehicles. A battery aging model is integrated to quantify the long-term costs associated with different operational strategies. Finally, a multi-objective particle swarm optimization algorithm, integrated with MPC, co-optimizes the rendezvous paths and speed profiles. In a case study based on a logistics network, simulation results demonstrate that, compared to the conventional station-based charging mode, the proposed V2V framework reduces total fleet operational costs by a net 12.5% and total energy consumption by 17.4% while increasing the energy utilization efficiency of EV-Ps by 21.4%. This net saving is achieved even though the V2V strategy incurs a marginal increase in battery aging costs, which is overwhelmingly offset by substantial savings in logistical efficiency. This study provides an efficient and economical solution for the dynamic energy management of electric fleets under realistic traffic conditions, contributing to a more sustainable and resilient urban logistics ecosystem. Full article