Search Results (166)

This study evaluates the degree to which small and medium sized enterprises (SMEs) are prepared to adopt smart manufacturing in contrast to large enterprises, a transition that depends on the effective use of the Internet of Things, artificial intelligence (AI), and advanced analytics. While many large multinational companies have already integrated such technologies, smaller firms still struggle because of tight budgets, limited technical expertise, and difficulties in scaling new systems. To capture these realities, the investigation refines the Initiative Mittelstand-Digital für Produktionsunternehmen und Logistik-Systeme (IMPULS) Industry 4.0 readiness model, which was initially developed to help German SMEs, so that it aligns with the circumstances faced by smaller manufacturers. A thorough review of published work first surveys existing readiness and maturity frameworks, highlights their limitations, and guides the selection of new, SME-specific indicators. The framework gauges readiness across six dimensions: strategic planning and organizational design, smart factory infrastructure, lean operations, digital products, data-driven services, and workforce capability. Each dimension is operationalized through a questionnaire that offers clear benchmarks and actionable targets suited to the current resources of each enterprise. Weaving strategic vision, skill growth, and cooperative support, the approach offers managers a direct path to sharper competitiveness and lasting innovation within a changing industrial landscape. Additionally, a separate Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis is provided for each dimension based on survey data offering decision-makers concise guidance for future investment. The proposed adaptation of the IMPULS framework, validated through empirical data from 31 SMEs, introduces a novel readiness index, diagnostic gap metrics, and actionable cluster profiles tailored to developing-country industrial ecosystems. Full article

The global energy landscape is transforming, driven by the urgent need to address climate change, reduce dependency on fossil fuels, and promote sustainable economic growth. Renewable energy sources (RESs) have emerged as a cornerstone of this transition, offering environmental benefits and significant potential to catalyze economic development. By harnessing inexhaustible natural resources, such as solar, wind, hydro, and biomass, renewable energy systems provide a pathway to achieving energy security, fostering innovation, and generating new economic opportunities. In this article, the economic effect on the RES sector development was examined. The authors defined the set from seven indicators: real GDP growth, unemployment rate, inflation rate, exports of goods and services, government debt, foreign direct investments, and labor cost index, which allowed them to evaluate the EU countries’ economic situation and rank the countries by economic stability level. The results, which were obtained using a multi-criteria evaluation method, show that the EU countries whose economies are the strongest according to the evaluated macroeconomic indicators are Luxembourg, Malta, Estonia, and Ireland. The countries with the lowest scores are Greece, Italy, and Spain. Seeking to evaluate the development level of the RES sector in all ranked EU countries, the analysis of RES sector development during the 2012–2022 period, using these RES indicators—share of renewable energy in gross final energy consumption by sector—in general, in transport, in electricity, and in heating and cooling, was carried out and, through a different multi-criteria method, the countries were ranked by RES development. After the analysis was carried out, it could be stated that the economic situation stability in the country does not directly affect the growth of the RES sector development, and the two rankings by different indicators are heavily uncorrelated. RES sector development can be affected by many other circumstances. RES development is still stagnating in some countries, despite macroeconomic stability, for several reasons: institutional and political barriers, differences in the availability of finance, infrastructure limitations, and technological and human resource shortages. Full article

Kirchhoff’s current law was originally derived for systems such as telegraphs that switch in 0.1 s. It is used widely today to design circuits in computers that switch in ~0.1 nanoseconds, one billion times faster. Current behaves differently in one second and one-tenth of a nanosecond. A derivation of a current law from the fundamental equations of electrodynamics—the Maxwell equations—is needed. Here is a derivation in one line: $\mathbf{d}\mathbf{i}\mathbf{v} \mathbf{c}\mathbf{u}\mathbf{r}\mathbf{l}\mathbf{B}/{\mathsf{\mu }}_0=0=\mathbf{d}\mathbf{i}\mathbf{v}\left(\mathbf{J}+({\mathsf{\epsilon }}_{\mathit{r}}-1){\mathsf{\epsilon }}_0\mathbf{\partial }\mathbf{E}/\mathbf{\partial }\mathbf{t}+{\mathsf{\epsilon }}_0\mathbf{\partial }\mathbf{E}/\mathbf{\partial }\mathbf{t}\right)=\mathbf{d}\mathbf{i}\mathbf{v}{\mathbf{J}}_{\mathbf{t}\mathbf{o}\mathbf{t}\mathbf{a}\mathbf{l}}$. Maxwell’s ‘true’ current is defined as ${\mathbf{J}}_{\mathbf{t}\mathbf{o}\mathbf{t}\mathbf{a}\mathbf{l}}$. The universal displacement current found everywhere is ${\mathsf{\epsilon }}_0\partial \mathbf{E}/\partial \mathrm{t}$. The conduction current $\mathbf{J}$ is carried by any charge with mass, no matter how small, brief, or transient, driven by any source, e.g., diffusion. The second term $({\mathsf{\epsilon }}_r-1){\mathsf{\epsilon }}_0\partial \mathbf{E}/\partial \mathrm{t}$ is the usual approximation to the polarization currents of ideal dielectrics. The dielectric constant ${\mathsf{\epsilon }}_r$ is a dimensionless real number. Real dielectrics can be very complicated. They require a complete theory of polarization to replace the $({\mathsf{\epsilon }}_r-1){\mathsf{\epsilon }}_0\partial \mathbf{E}/\partial \mathrm{t}$ term. The Maxwell current law $\mathrm{d}\mathrm{i}\mathrm{v}{\mathbf{J}}_{\mathbf{t}\mathbf{o}\mathbf{t}\mathbf{a}\mathbf{l}}=0$ defines the solenoidal field of total current that has zero divergence, typically characterized in two dimensions by streamlines that end where they begin, flowing in loops that form circuits. Note that the conduction current $\mathbf{J}$ is not solenoidal. Conduction current $\mathbf{J}$ accumulates significantly in many chemical and biological applications. Total current ${\mathbf{J}}_{\mathbf{t}\mathbf{o}\mathbf{t}\mathbf{a}\mathbf{l}}$ does not accumulate in any time interval or in any circumstance where the Maxwell equations are valid. ${\mathbf{J}}_{\mathbf{t}\mathbf{o}\mathbf{t}\mathbf{a}\mathbf{l}}$ does not accumulate during the transitions of electrons from orbital to orbital within a chemical reaction, for example. ${\mathbf{J}}_{\mathbf{t}\mathbf{o}\mathbf{t}\mathbf{a}\mathbf{l}}$ should be included in chemical reaction kinetics. The classical Kirchhoff current law $\mathrm{d}\mathrm{i}\mathrm{v} \mathbf{J}=0$ is an approximation used to analyze idealized topological circuits found in textbooks. The classical Kirchhoff current law is shown here by mathematics to be valid only when $\mathbf{J}\gg {\mathsf{\epsilon }}_0\partial \mathbf{E}/\partial \mathrm{t},$ typically in the steady state. The Kirchhoff current law is often extended to much shorter times to help topological circuits approximate some of the displacement currents not found in the classical Kirchhoff current law. The original circuit is modified. Circuit elements—invented or redefined—are added to the topological circuit for that purpose. Full article

The rapid growth of electric vehicles (EVs) and new energy systems has put lithium-ion batteries at the center of the clean energy change. Nevertheless, to achieve the best battery performance, safety, and sustainability in many changing circumstances, major innovations are needed in Battery Management Systems (BMS). This review paper explores how artificial intelligence (AI) and digital twin (DT) technologies can be integrated to enable the intelligent BMS of the future. It investigates how powerful data approaches such as deep learning, ensembles, and models that rely on physics improve the accuracy of predicting state of charge (SOC), state of health (SOH), and remaining useful life (RUL). Additionally, the paper reviews progress in AI features for cooling, fast charging, fault detection, and intelligible AI models. Working together, cloud and edge computing technology with DTs means better diagnostics, predictive support, and improved management for any use of EVs, stored energy, and recycling. The review underlines recent successes in AI-driven material research, renewable battery production, and plans for used systems, along with new problems in cybersecurity, combining data and mass rollout. We spotlight important research themes, existing problems, and future drawbacks following careful analysis of different up-to-date approaches and systems. Uniting physical modeling with AI-based analytics on cloud-edge-DT platforms supports the development of tough, intelligent, and ecologically responsible batteries that line up with future mobility and wider use of renewable energy. Full article

The fast expansion of cloud computing has raised carbon emissions and energy usage in cloud data centers, so creative solutions for sustainable resource management are more necessary. This work presents a new algorithm—Carbon-Aware, Energy-Efficient, and SLA-Compliant Virtual Machine Placement using Deep Q-Networks (DQNs) and Agglomerative Clustering (CARBON-DQN)—that intelligibly balances environmental sustainability, service level agreement (SLA), and energy efficiency. The method combines a deep reinforcement learning model that learns optimum placement methods over time, carbon-aware data center profiling, and the hierarchical clustering of virtual machines (VMs) depending on resource constraints. Extensive simulations show that CARBON-DQN beats conventional and state-of-the-art algorithms like GRVMP, NSGA-II, RLVMP, GMPR, and MORLVMP very dramatically. Among many virtual machine configurations—including micro, small, high-CPU, and extra-large instances—it delivers the lowest carbon emissions, lowered SLA violations, and lowest energy usage. Driven by real-time input, the adaptive decision-making capacity of the algorithm allows it to dynamically react to changing data center circumstances and workloads. These findings highlight how well CARBON-DQN is a sustainable and intelligent virtual machine deployment system for cloud systems. To improve scalability, environmental effect, and practical applicability even further, future work will investigate the integration of renewable energy forecasts, dynamic pricing models, and deployment across multi-cloud and edge computing environments. Full article

This paper presents platforms as a solution to rethink how we build, addressing the pressing paradox between meeting growing housing demands. The construction sector has not fully grasped the advantages of platforms beyond standardisation and efficiency. In contrast, other sectors have begun acknowledging that platforms can capture increased value through interactions among firms within a networked ecosystem. Learning from other sectors, this paper investigates platforms in the construction context, aiming to define, classify, and assess their impact on the construction value chain. The research approach was abductive, involving a cross-sectoral review of 190 platforms across 16 Australian and New Zealand Standard Industrial Classification (ANZSIC) industries and semi-structured interviews with stakeholder groups of the construction value chain in Australia. The findings categorise platforms as physical, digital, or hybrid, highlighting their potential to move value-added activities upstream, facilitate collaboration, and foster innovation through data-driven insights. The paper’s novelty lies in the exhaustive cross-sectoral review, the classification of platforms in the construction context, and the proposition of a platform approach as a versatile framework tailored to diverse needs and circumstances that offers a fresh perspective on sustainable building practices. The practical contribution of this study lies in offering guidelines for industry practitioners aiming to develop or refine a platform-based approach tailored to the construction context. Full article

This review addresses the critical conservation challenges faced by Pinna nobilis, the noble pen shell, a keystone umbrella species in Mediterranean marine ecosystems. Since 2016, the species has experienced catastrophic population declines due to mass mortality events likely driven by protozoan, bacterial, and viral infections. Despite these severe circumstances, small resilient populations persist in select estuaries and coastal lagoons across the Mediterranean, offering potential for recovery. We provide a comprehensive overview on research dedicated to Pinna nobilis’ biology, genetic variation, disease dynamics, and environmental factors influencing its survival, with a focus on refugia where populations still endure. Remarkably, recent studies have revealed signs of resistance in certain individuals and the potential for hybridisation with Pinna rudis. In this context, the possible impact of the increasing occurrence of hybridisation between Pinna nobilis and Pinna rudis on the conservation of their genetic diversity should be carefully considered. This review highlights the importance of ongoing conservation efforts including habitat restoration, protection of remaining populations, assessment of past and present genetic variability, and the development of captive breeding programmes. We aim to elucidate the need for continued studies on Pinna nobilis’ biodiversity, particularly its evolutionary dynamics, genetic makeup, and the interplay of environmental variables influencing its survival and persistence. Full article

This study empirically examines whether Brazil’s remarkable economic growth from 2003 to 2010 was primarily driven by Lula’s policies or favorable global economic conditions using the Synthetic Control Method—a robust causal inference technique for assessing policy effects when randomized controlled trials are infeasible and only one treated unit exists. Our analysis suggests that Brazil’s economic performance was largely attributable to external circumstances, while the policies of Lula’s administration may not have significantly enhanced growth. This study demonstrates the robustness of the results through leave-one-out distribution, the ratio of postintervention-period root mean square prediction error (RMSPE) to preintervention-period RMSPE, and in-space placebo tests. Full article

Building Information Modeling (BIM) concepts are permeating the approach to the design of buildings and landscapes for the architectural, engineering, and construction sectors. Recent regulations require that even medium–small-size public works are managed through BIM-driven design. These circumstances have led to an increase in research on the topic. The expansion of the demand of BIM-skilled professionals urges higher education institutions to re-engineer their design programs. The aim of this paper is to evaluate this academic education transition in the Department of Agricultural Sciences at the University of Sassari, Italy. The method consists of a BIM academic education assessment framework based on ten criteria clustered into three macro-issues. The application of this framework to the assessment of three diploma final theses signals that some actions have been undertaken (i.e., introducing BIM basic concepts in rural building and landscape design, stimulating interest in students, clarifying the dimensions of BIM, and promoting the concept of 3D object design and management), but still, much work must be carried out. The work confirms typical barriers to the implementation of BIM concepts in the core curriculum and the need to mobilize the whole educational ecosystem to achieve satisfactory progress toward effective innovation in contemporary BIM-led design teaching. This work represents the first attempt to evaluate the progress of the Department of Agricultural Sciences, University of Sassari, toward the integration of BIM concepts in its courses and to position this transition in an international panorama. Full article

With the explosive growth in global lithium demand driven by the new energy industry, traditional lithium extraction methods face critical challenges such as resource scarcity, environmental pressure, and high energy consumption, necessitating sustainable alternatives. Under such circumstances, geothermal brine has emerged as a critical lithium resource, attracting significant attention due to advancements in efficient extraction technologies. This review establishes a comprehensive framework for analyzing geothermal lithium extraction technologies, with the following key contributions: an in-depth analysis of resource characteristics and development advantages, an innovative technical evaluation and performance comparison, and strategic pathways for technological synergy and industrial integration. This article reviews the global distribution and characteristics of lithium resources, analyzes the advantages and primary methods of geothermal lithium extraction, and examines key challenges such as high energy consumption and environmental impacts. Furthermore, future development directions are outlined. Currently, applicable technologies for geothermal lithium extraction include evaporation–crystallization, chemical precipitation, adsorption, solvent extraction, electrochemical methods, and membrane separation. Among these, membrane separation, particularly forward osmosis (FO), is identified as a pivotal research focus. The industrialization of geothermal lithium extraction and its integration with other industries are expected to shape future trends. This review not only provides critical insights and optimization strategies for geothermal lithium resource development, but also establishes a theoretical foundation for the green transition and sustainable utilization of resources in the global new energy industry. Full article

The sustainability of the natural gas-to-methanol (NGTM) and methanol-to-gasoline (MTG) processes are assessed in this systematic review as a potential substitute in the global energy transition. Methanol offers itself as a versatile and less carbon-intensive substitute for conventional gasoline in light of growing environmental concerns and the demand for cleaner fuels. This review’s rationale is to assess MTG’s ability to lessen environmental impact while preserving compatibility with current fuel infrastructure. The goal is to examine methanol and gasoline’s effects on the environment, society, and economy throughout their life cycles. This review used a two-phase systematic literature review methodology, filtering and evaluating studies that were indexed by Scopus using bibliometric and thematic analysis. A total of 25 documents were reviewed, in which 22 documents analyzed part of this study, and 68% employed LCA or techno-economic analysis, with the U.S. contributing 35% of the overall publications. A comparative analysis of the reviewed literature indicates that methanol-based fuels offer significantly lower greenhouse gas (GHG) emissions and life cycle environmental impacts than gasoline, particularly when combined with carbon capture and renewable feedstocks. This review also highlights benefits, such as improved safety and energy security, while acknowledging challenges, including high production costs, infrastructure adaptation, and toxicity concerns. Several drawbacks are high manufacturing costs, the necessity to adjust infrastructure, and toxicity issues. The report suggests investing in renewable methanol production, AI-driven process optimization, and robust legislative frameworks for integrating green fuels. The life cycle sustainability assessment (LCSA) of NGTM and MTG systems should be investigated in future studies, particularly in light of different feedstock and regional circumstances. The findings emphasize NGTM and MTG’s strategic role in aligning with several UN Sustainable Development Goals (SDGs) and add to the worldwide conversation on sustainable fuels. A strong transition necessitates multi-stakeholder cooperation, innovation, and supporting policies to fully realize the sustainability promise of cleaner fuels like methanol. Full article

Telework has become a crucial element of the modern business landscape, driven by transformations sparked by multiple global crises. The transition from traditional, in-office work to telework, sometimes mandated by revolutionary circumstances (such as the COVID-19 pandemic), has highlighted both the advantages and challenges associated with this mode of work organization. In this context, the present study examines the effects of telework as experienced by employees and managers during two key periods: the COVID-19 pandemic and the introduction of chatbots. Through 24 interviews conducted and analyzed across these two timeframes (2021 and 2024) using NVivo 14 Windows software, the data were organized and interpreted within the framework of the Job Demands–Resources (JD-R) model. The main findings focus on organizational communication, sustainability, and work efficiency, while also highlighting associated benefits and drawbacks. The results demonstrate the importance of adapting organizational resources to meet growing job demands in order to maintain desired levels of efficiency and effectiveness while avoiding burnout, productivity declines, or other negative outcomes in the context of telework. This research contributes to understanding the evolution of telework by offering practical insights for sustaining high levels of motivation and workforce engagement in achieving organizational objectives in the hybrid work era. This paper emphasizes the significance of the JD-R Model in analyzing dynamic work environments, providing relevant perspectives for organizations on the continuously evolving dimensions of job demands, job resources, and outcomes. Full article

Unconventional reservoirs are characterized by intricate fluid-phase behaviors, and physics-based shale oil well productivity prediction models often exhibit substantial deviations due to oversimplified theoretical frameworks and challenges in parameter acquisition. Under these circumstances, data-driven approaches leveraging actual production datasets have emerged as viable alternatives for productivity forecasting. Nevertheless, conventional data-driven architectures suffer from structural simplicity, limited capacity for processing low-dimensional feature spaces, and exclusive applicability to intra-sequence learning paradigms (e.g., production-to-production sequence mapping). This fundamentally conflicts with the underlying principles of mechanistic modeling, which emphasize pressure-to-production sequence transformations. To address these limitations, we propose a hybrid deep learning architecture integrating convolutional neural networks with bidirectional gated recurrent units (CNN-BiGRU). The model incorporates dedicated input pathways: fully connected layers for feature embedding and convolutional operations for high-dimensional feature extraction. By implementing a sequence-to-sequence (seq2seq) architecture with encoder–decoder mechanisms, our framework enables cross-domain sequence learning, effectively bridging pressure dynamics with production profiles. The CNN-BiGRU model was implemented on the TensorFlow framework, with rigorous validation of model robustness and systematic evaluation of feature importance. Hyperparameter optimization via grid searching yielded optimal configurations, while field applications demonstrated operational feasibility. Comparative analysis revealed a mean relative error (MRE) of 16.11% between predicted and observed production values, substantiating the model’s predictive competence. This methodology establishes a novel paradigm for machine learning-driven productivity prediction in unconventional reservoir engineering. Full article

The integration of machine learning (ML) into self-governing spraying systems is one of the major developments in digital precision agriculture that is significantly improving resource efficiency, sustainability, and production. This study looks at current advances in machine learning applications for automated spraying in agricultural mechanisation, emphasising the new innovations, difficulties, and prospects. This study provides an in-depth analysis of the three main categories of autonomous sprayers—drones, ground-based robots, and tractor-mounted systems—that incorporate machine learning techniques. A comprehensive review of research published between 2014 and 2024 was conducted using Web of Science and Scopus, selecting relevant studies on agricultural robotics, sensor integration, and ML-based spraying automation. The results indicate that supervised, unsupervised, and deep learning models increasingly contribute to improved real-time decision making, performance in pest and disease detection, as well as accurate application of agricultural plant protection. By utilising cutting-edge technology like multispectral sensors, LiDAR, and sophisticated neural networks, these systems significantly increase spraying operations’ efficiency while cutting waste and significantly minimising their negative effects on the environment. Notwithstanding significant advances, issues still exist, such as the requirement for high-quality datasets, system calibration, and flexibility in a range of field circumstances. This study highlights important gaps in the literature and suggests future areas of inquiry to develop ML-driven autonomous spraying even more, assisting in the shift to more intelligent and environmentally friendly farming methods. Full article

Despite overall improvements in the accessibility, quality, and outcomes of care in the U.S. health care system over the last 30 years, a large proportion of marginalized racial and ethnic minority (minoritized) groups continue to suffer from worse outcomes across most domains. Many of these health disparities are driven by inequities in access to and the scope of society’s health-affirming structural resources and opportunities commonly referred to as structural drivers or social determinants of health—SDoH. Persistently health-undermining factors in the social environment and the downstream effects of these inequities on neurocognitive and biological pathways exacerbate these disparities. The consequences of these circumstances manifest as behavioral, neurohormonal, immune, and inflammatory and oxidative stress responses, as well as epigenetic changes. We propose a theoretical model of the interdependent characteristics of inequities in the SDoH driven by race-based discriminatory laws, policies, and practices that eventually culminate in poor health outcomes. This model provides a framework for developing and validating multi-level interventions designed to target root causes, thereby lessening health disparities and accelerating improved health outcomes for minoritized groups. Full article