Abstract: Opinion mining is the process of analyzing the content people create, such as product reviews or social media posts, to determine if the feelings expressed are positive, negative, or neutral. Twitter, one of the most popular social platforms for sharing opinions, provides a lot of data that can be used to understand public sentiment. In this project, we developed a system for classifying sentiments that begins with a detailed preprocessing step using natural language processing techniques. After the data has been processed, the tweets are represented using the traditional Term Frequency-Inverse Document Frequency (TF-IDF) model to highlight the most important text features.To make these features even more relevant, we introduced the Egret Swarm Optimization Algorithm (ESOA), a method for selecting important features inspired by how Great and Snowy Egrets hunt. ESOA uses three strategies—waiting patiently, actively searching, and making decisions based on differences—to find a good balance between exploring new areas and focusing on known ones. This creates a flexible framework that works well in different situations. For sentiment classification, we use a Multi-Head Attention Mechanism (MHAM) that can understand various meanings in user text. We fine-tuned the model’s settings using the Dwarf Mongoose Optimization (DMO) algorithm, along with a strategy that helps each part of the attention mechanism focus on different aspects of the text. Testing our approach on the Sentiment140 dataset shows it works very well, achieving almost 97% accuracy, which is better than other methods that usually reach between 92% and 95%.

Abstract: Mining activities generate vast quantities of waste each year, including mine tailings, bauxite residue, waste rock, and various metallurgical slags. Although these materials have traditionally been regarded as environmental liabilities, many possess physical and chemical properties that make them promising candidates for use in construction. This review synthesizes recent research on the utilization of major mining waste streams, with particular emphasis on pavement applications and other construction materials. The findings indicate that bauxite residue exhibits both pozzolanic and filler characteristics, demonstrating potential in asphalt mastics, asphalt mixtures, and other construction products. Nonetheless, its widespread adoption is constrained by issues such as high alkalinity, leaching risks, and concerns related to naturally occurring radioactivity. Mine tailings can substitute for fine aggregates and cement in a range of mixtures, though challenges including pronounced material variability and environmental risks persist. Waste rock offers favourable geotechnical properties for use in road bases and embankments, while metallurgical slags (e.g., copper, nickel, and lithium slags) provide functional pozzolanic activity and suitable aggregate qualities. Across all waste types, their incorporation into construction materials can conserve natural resources, reduce material costs, and support circular-economy and low-carbon development objectives. However, progress remains contingent upon advancements in material standards, pretreatment technologies, environmental protection measures, and large-scale field validation. Overall, this review underscores both the significant potential and the practical challenges associated with transforming mining waste into valuable and sustainable construction resources.

Abstract:

The increasing use of integrated renewable energy sources (RES) is undoubtedly reshaping the structure of power systems. In such conditions, achieving energy efficiency and sustainability requires the development and integration of digital solutions to manage energy flows and resources optimization. This paper aims to provide a comprehensive overview of the successful integration of artificial intelligence (AI) in the energy sector, particularly in relation to the increasing utilization of renewable energy. The paper presents trends and potential scenarios in the digitalization of energy, along with the associated challenges. It analyzes particular applications of AI tools in strategic areas of the energy sector. The article also attempts to summarize the current status, goals, key areas, and activities in the irreversible transformation of power structures into digital intelligent ones. Five key areas in the energy sector have been identified in which AI tools are applied.

Abstract:

The accelerated digitalization of industrial ecosystems has positioned the Internet of Things (IoT) as a critical enabler of corporate sustainability within Industry 4.0. However, evidence on how IoT contributes to environmental, social, and economic performance remains fragmented. This study conducts a systematic literature review following PRISMA 2020 guidelines to consolidate the scientific advances linking IoT with sustainable corporate management. The search covered 2009–2025 and included publications indexed in Scopus, EBSCO Essential, and MDPI, identifying 62 empirical and conceptual studies that met the inclusion criteria. Bibliometric analyses—such as keyword co-occurrence mapping and temporal heatmaps—were performed using VOSviewer to detect dominant research clusters and emerging thematic trajectories. Results reveal four domains in which IoT significantly influences sustainability: (1) resource-efficient operations enabled by real-time sensing and predictive analytics; (2) energy optimization and green digital transformation initiatives; (3) circular-economy practices supported by data-driven decision-making; and (4) the integration of IoT with Green Human Resource Management to strengthen environmentally responsible organizational cultures. Despite these advances, gaps persist related to Latin American contexts, theoretical integration, and longitudinal assessment. This study proposes a conceptual model illustrating how IoT-enabled technologies enhance corporate sustainability and offers strategic insights for aligning Industry 4.0 transformations with the Sustainable Development Goals, particularly SDGs 7, 9, and 12.

Abstract: In the study, a model was developed to calculate the power required for the circumferential cutting of solid wood in the longitudinal direction, considering the relevant technological parameters and mechanical properties of the wood. Based on measurements of different combinations and using the Response surface method (RSM) and the Central composite design (CCD), a model was created that, in its derived version, considers the cutting width and depth, the diameter and speed of the tool, the number of cutting edges and the sharpness of the cutting edge, the feed rate of the workpiece and the density and moisture content of the wood. The model can be used to calculate the cutting power of various tree species with densities ranging from 400 to 700 kg/m³, moisture contents from 8 to 16%, and a wide range of cutting-edge sharpness, from a sharp cutting edge with a tip radius of 5 µm to a blunt cutting edge with a tip radius of 35 µm. The model is designed for a rake angle of 20°, the value most frequently used in practise. An ANOVA analysis was used to determine the suitability of the model, which is highly significant with an R2 value of 0.93 and an average deviation of the calculated values from the measured values of 8.8%. The model is robust and therefore useful in the wood industry for predicting energy consumption in the processing of solid wood.

Abstract: High construction costs have plagued recent nuclear projects, and they hamper the widespread deployment of nuclear technology. The Nuclear Cost Reduction Tool is a reactor economic framework that quantifies the impact that various reactor design and construction attributes have on construction costs and cost overruns and shows the positive effects of learning over a series of deployments. However, a downside of the current model is that all model output and capabilities are deterministic. To provide a more comprehensive view, this study evaluated the impact of model parameter uncertainty through sensitivity analysis applied to 18 model parameters. This approach quantified the impact of model uncertainty on the output variables of Net Overnight Capital Cost (Net OCC), Construction Duration (CD), and Levelized Cost of Electricity (LCOE). Monte Carlo analysis revealed uncertainty distributions for these variables, showing that absolute uncertainty decreases over a series of deployments. A local sensitivity analysis showed that even small parameter perturbations (5%) can have a significant impact on project execution, highlighting areas that could reduce costs by billions across an order book of reactors. The results of this study have improved the understanding of the model and identified the most impactful model parameters and construction attributes.

Abstract: This paper proposes an integrated numerical–experimental methodology for the durability assessment and optimisation of a passenger-car rear axle. A dedicated rear-suspension durability test bench was designed to impose a controlled cyclic vertical excitation on a dependent axle, reproducing service-like translational and rotational amplitudes of the beam and stabilizer bar. A detailed flexible multibody model of the bench–axle system was developed in MSC ADAMS and used to tune the kinematic excitation and determine an equivalent design load at the wheel spindles, consistent with the stiffness of the suspension assembly. Experimental strain measurements at nine locations on the axle, acquired with strain-gauge instrumentation on the bench, were converted into stresses and used to validate an explicit dynamic finite element model in ANSYS. The FE predictions agree with the experiments within about 10% at the beam mid-span and correctly identify a critical region at the junction between the side plate and the arm, where peak von Mises stresses of about 104 MPa occur. The validated model then supports a response-surface-based optimisation of the safety-critical wheel spindle, yielding a geometry that lowers spindle-fillet stresses to around 180–185 MPa under the maximum admissible wheel load, with only a modest mass penalty.

Abstract: In Al–Fe alloys, the mechanical performance is determined by the morphology of iron-rich phases. In this work, AA8176(Al-1Fe)- nY (n = 0, 0.3, 0.5, 0.7, and 0.9 wt.%) alloys were prepared by the cast method. To systematically analyze the influence of Y on microstructure evolution and tensile behavior, a multi-scale characterization approach was employed, combining metallography, electron microscopy, X-ray diffraction, cooling curve analysis, and tensile tests. The results revealed that the optimal refinement effect was achieved when the amount of Y content was 0.5 wt.%. The micro-structure of the alloy was significantly modified by Y addition. The coarse needle-like Al13Fe4 phases were gradually transformed into short rod-like and particle morphology. And the average length was decreased from 10.01 μm to 2.65 μm. Meanwhile, some small size Al10Fe2Y phases were formed around the Al13Fe4 phases. Additionally, the secondary dendrite arm spacing (SDAS) of A8176 alloy was reduced from 31.33 μm to 20.24 μm. Furthermore, the mechanical properties of the AA8176 alloy were improved due to the modified microstructure. The tensile strength of the alloy was increased from 84.47 MPa to 96.86 MPa, and the elongation was increased from 18.6 % to 23.1 %. It is proposed that the growth of α-Al dendrite and Al13Fe4 phases were effectively inhibited by segregation of Y atoms around α-Al dendrite and Al13Fe4 phases during solidification. And the Al10Fe2Y phases were formed by these Y atoms with Al and Fe elements. However, the formation of coarse Al10Fe2Y phases was promoted by excessive Y content, which resulting in a substantial degradation in mechanical properties.

Abstract: A This paper presents a comprehensive review of recent advancements and challenges in road construction, focusing on efficiency improvements, digitization, and innovative technologies. The construction industry, particularly the road construction sector, has been identified as one of the least digitized sectors globally, lagging behind other industries in terms of efficiency improvements. To address this issue, researchers have proposed various approaches to enhance road construction processes. Key areas of focus include: Automation and digitization of construction monitoring tasks, including progress tracking, quality control, and quantity analysis; Implementation of Industry 4.0 technologies and Lean-based flow optimization principles. Integration of virtual reality, camera modeling, and artificial intelligence to optimize road construction; Development of cellular automation models to simulate road construction traffic flow in urban networks; Application of artificial neural networks to optimize fuel use in on-road construction equipment; Digitization, BIM- accelerating planning and approval processes. In terms of construction materials and technology- implementation of discrete flow distributors in hydraulic systems for multi-functional road construction machinery; Exploration of alternative materials and stabilization techniques, such as lateritic soils and enzyme-based stabilizers. The review highlights the potential for significant improvements in road construction efficiency, environmental impact, and cost-effectiveness through the adoption of these technologies and methodologies. However, challenges remain in terms of implementation, standardization, and adaptation to specific project requirements. The research indicates a trend towards more efficient, sustainable, and technologically advanced road construction practices, with a focus on overcoming traditional inefficiencies and environmental concerns. Future research should continue to focus on addressing these challenges and developing comprehensive, adaptable solutions for the road construction industry, while leveraging the latest findings in this area.

Abstract: The construction of mass concrete foundations for nuclear power plants faces significant challenges in controlling hydration heat and preventing early-age thermal cracking. This study develops an integrated framework combining high-fidelity thermal-mechanical simulation, real-time temperature monitoring, and construction process optimization to address these issues. Focusing on the VVER-1200 reactor raft foundation in the Xudapu NPP Phase II Project, an innovative center-to-periphery synchronous pouring method is proposed. Numerical simulations demonstrate that this method strategically utilizes construction stage time lags to moderate temperature distribution and reduce thermal stress. Field monitoring data show good agreement with simulated results, which provide conservative and safe estimates for curing guidance. Post-construction verification confirmed the absence of thermal cracks. The proposed methodology offers a reliable, science-based approach for thermal crack mitigation and can serve as a valuable reference for similar large-scale mass concrete structures in nuclear and other critical infrastructure projects.

Abstract: Very Low Earth Orbit (VLEO) satellites, operating at altitudes below 450km, provide tremendous potential in the domain of remote sensing. Their proximity to Earth of-fers high resolution, low latency, and rapid revisit rates, allowing continuous moni-toring of dynamic systems and real-time delivery of vertically integrated earth ob-servation products. Nonetheless, the application of VLEO is not yet fully realized due to numerous complexities associated with VLEO satellite development, considering atmospheric drag, short satellite lifetimes, and social, political and legal regulatory fragmentation. This paper reviews the recent technological developments supporting sustainable VLEO operations with regards to aerodynamic satellite design, atomic oxygen barriers, and atmospheric-breathing electric propulsion (ABEP). Furthermore, the paper pro-vides an overview of the identification of regulatory and economic barriers that extort additional costs for VLEO ranging from frequency band allocation and space traffic management to life-cycle cost and uncertain commercial demand opportunities. Nevertheless, the commercial potential of VLEO operations is widely acknowledged, and estimated to lead to an economic turnover in the order of 1.5 B$ by 2030. Learning from the literature and prominent past experiences such as the DISCOVERER and the CORONA program, the study identifies key gaps and proposes a roadmap to sustainable VLEO development. The proposed framework emphasizes modular and serviceable satellite platforms, hy-brid propulsion systems, and globally harmonized governance in space. Ultimately, public-private partnerships and synergies across sectors will determine whether VLEO systems become part of the broader space infrastructure unlocking new capabilities for near-Earth services, environmental monitoring, and commercial innovation at the edge of space.

Abstract: Remote sensing image super-resolution (RSISR) aims to reconstruct high-resolution images from low-resolution observations of remote sensing data to enhance the visual quality and usability of remote sensors. Real world RSISR is challenging owing to the diverse degradations like blur, noise, compression, and atmospheric distortions. We propose hierarchical multi-task super- resolution framework including degradation-aware modeling, dual-decoder reconstruction, and static regularization-guided generation. Speciffcally, the degradation-wise module adaptively characterizes multiple types of degradation and provides effective conditional priors for reconstruction. The dual-doder platform incorporates both convolutional and Transformer branches to match local detail preservation as well as global structural consistency. Moreover, the static regularizing guided generation introduces prior constraints such as total variation and gradient consistency to improve robustness to varying degradation levels. Extensive experiments on two public remote sensing datasets show that our method achieves performance that is robust against varying degradation conditions.

Abstract: In this study, a detection framework is presented and evaluated that integrates sensor data (e.g., temperature, humidity, gas readings) with machine learning (ML) models and computer vision-based smoke and fire detection systems, in an effort to increase overall accuracy, robustness, as well as false-alarm reduction. To this end, sixteen (16) ML and deep learning (DL) models are employed on an internet of things (IoT) sensor dataset. Moreover, a range of YOLO models, such as older versions (YOLOv5n, YOLOv8n), as well as newer versions (YOLOv10n, YOLOv11n, YOLOv12n) are employed on an image-label based dataset. Model selection initially prioritizes lightweight architectures that are suitable for resource-constrained edge devices. Afterwards, the selected models are evaluated via well-known metrics, such as parameter count, F1-score/mean average precision (mAP) and real-time inference latency. In the same context, explainable AI (XAI) techniques, such as SHAP (SHapley Additive exPlanations) for ML models and LIME (Local Interpretable Model-agnostic Explanations) for the YOLO detectors, are integrated to the platform as well. According to the presented results, the Explainable Sensor Fusion (ESF) achieves decent performance metrics on a resource-constrained hardware device, demonstrating a viable, explainable, and highly efficient solution for real-time smoke and fire emergency response in industrial environments.

Abstract: Ensuring the required microclimate parameters is the most critical task in hot climates. In pig farms, air cooling is provided by means of steam-compression chillers or water-evaporative cooling, which is the simplest way to cool the air. The implementation of water-evaporative cooling depends largely on the interaction of the media involved in this process. The paper considers the process of interaction of cooling water with the surface of a cellular polycarbonate heat exchanger. A mathematical model describing the process of wetting the sprayed surface of the heat exchanger is obtained. The dependence of theoretical water flow rate to provide air cooling in a given operation mode is determined. Production tests of recuperative heat recovery unit with heat exchanger made of cellular polycarbonate equipped with water evaporative cooling system were carried out. The efficiency of water-evaporative cooling of air has been determined, which was reflected in the temperature reduction by 6.3 °C. Characteristic operating modes of the unit, depending on the cooling water flow rate, providing effective air cooling are revealed.

Abstract: Many quality characteristics of products or services are commonly evaluated on ordinal scales with a finite number of categories. A systematic analysis of categorical variables collected over time may be very useful for a profitable management strategy. In order to measure customer satisfaction or quality improvement in a process, two or more quality characteristics are often conjointly measured and summarized by suitable indexes. A common practice suggests evaluating a synthetic index by mapping each outcome of a multivariate ordinal variable into numbers. This procedure is not always legitimate from the measurement theory point of view. Recently the author read the paper “Synthesis maps for multivariate ordinal variables in manufacturing” and found in it many ideas about Control Charts for Services. We analyse them from a “scientific point of view” to compare the authors findings with ours. The analysis shows the “same (=equivalent)” results for the “linguistic control charts” and different results for the “numeric Control Charts”: the cause is that they do not use correctly the Theory. The Control Limits in the Shewhart CCs are based on the Normal Distribution (Central Limit Theorem, CLT) and are not valid for non-normal distributed data: consequently, the decisions about the “In Control” (IC) and “Out Of Control” (OOC) states of the process are wrong. The Control Limits of the CCs are wrongly computed, due to unsound knowledge of the fundamental concept of Confidence Interval. Minitab and other software e (e.g. JMP, SAS) use the “T Charts”, claimed to be a good method for dealing with “rare events”, but their computed Control Limits of the CCs are wrong. The same happens for the Confidence Limits of the parameters of the distribution involved in the papers (Weibull, Inverse Weibull, Gamma, Binomial, Maxwell).

Abstract: The ground impedance (insulation resistance Risol) of photovoltaic (PV) modules is usually measured only in the dry state, even though arrays frequently operate under dew-wet or rain-wet conditions, when leakage paths can change. We measured dry insulation resistance Rdry and IEC 61215 MQT 15 wet leakage resistance Rwet for N = 37 field-aged crystalline-silicon modules from utility-scale plants and related the results to the IEC 40 MΩ·m² criterion (Rwet·A ≥ 40). The measurements used 1000 V DC and a 2 min dwell; Rwet was obtained in a salted bath with a solution resistivity < 3500 Ω·cm. The median Rdry was 42.4 GΩ, whereas the median Rwet was 462.5 MΩ, resulting in a median Rdry/Rwet ratio of ~110×. Three modules (8.1%) failed the 40 MΩ·m² limit already in the dry state, whereas eight modules (21.6%) failed under IEC-wet conditions; five were dry-pass/wet-fail cases that would have passed dry screening. For a representative area A = 1.8 m², a practical conservative dry triage threshold of approximately 55.5 GΩ identifies modules needing IEC-wet verification rather than serving as a stand-alone limit. Overall, combining dry and IEC-wet measurements improves safety and supports sustainability through resource-efficient repowering/revamping and end-of-life decisions in large PV fleets, particularly in hot climates.

Abstract: Geopolymer mortars were produced from recycled concrete powder (RCP) and recycled brick powder (RBP) at a 30/70 wt% ratio, activated with a hybrid alkaline solution (NaOH/Na₂SiO₃/KOH) and reinforced with sisal (Agave) fibers at 0–2 wt%. Mechanical performance (compression and flexural) and microstructure–phase evolution were as-sessed by XRD, FTIR, and SEM-EDS after low-temperature curing. Sisal addition de-livered a strength–toughness balance, with an intermediate dosage (~1–1.5 wt%) providing the best overall performance; higher dosages induced packing loss and fiber clustering. Microstructural evidence indicates the coexistence and co-crosslinking of N-A-S-H and C-(A)-S-H gels promoted by the RCP, which densifies the matrix and enhances fiber–matrix anchorage. Fractographic features support a crack-bridging/pull-out mechanism responsible for the improvement without penaliz-ing early-age strength. The study identifies a practical advantage of sisal reinforcement in RCP/RBP geopolymer mortars and links it to gel chemistry and interfacial phenom-ena, providing a reproducible pathway toward fiber-reinforced, low-embodied-carbon geopolymers derived from construction and demolition waste and suitable for durable construction applications.

Abstract: This work presents a time-domain analog-to-digital conversion method in which the amplitude of a sensor signal is encoded through its crossing instants with a periodic ramp. The proposed architecture departs from conventional ADC and PWM demodulation approaches by shifting quantization entirely to the time domain, enabling waveform reconstruction using only a ramp generator, an analog comparator, and a timer capture module. A theoretical framework is developed to formalize the voltage-to-time mapping, derive expressions for resolution and error, and identify conditions that ensure monotonicity and single-crossing behavior. Simulation results demonstrate high-fidelity reconstruction for both periodic and non-periodic signals, including real photoplethysmographic (PPG) waveforms, with errors approaching the theoretical quantization limit. A hardware implementation on a PSoC 5LP microcontroller confirms the practicality of the method under realistic operating conditions. Despite ramp nonlinearity, comparator delay, and sensor noise, the system achieves effective resolutions above 12 bits using only native mixed-signal peripherals and no conventional ADC. These results show that accurate waveform reconstruction can be obtained from purely temporal information, positioning time-encoded sensing as a viable alternative to traditional amplitude-based conversion. The minimal analog front end, low power consumption, and scalability of timer-based processing highlight the potential of the proposed approach for embedded instrumentation, distributed sensor nodes, and biomedical monitoring applications.

Abstract:

Multi-component polymer hydrogels present complex physiochemical interactions that make accurate compositional analysis challenging. This study evaluates three analytical techniques: Nuclear Magnetic Resonance (NMR), Advanced Polymer Chromatography (APC), and Thermogravimetric Analysis (TGA) to quantify polyvinyl alcohol (PVA) and polyethylene glycol (PEG) content in hybrid freeze-thaw derived PVA/PEG/PVP hydrogels. Hydrogels were synthesized using an adapted freeze–thaw method across a wide range of PVA:PEG ratios, with PVP included at 1 wt% to assess potential intermolecular effects. NMR and APC reliably quantified polymer content with low average errors of 2.77% and 2.01%, respectively, and were unaffected by phase separation or hydrogen bonding within the composite matrix. TGA enabled accurate quantification at PVA contents ≤62.5%, where PEG and PVA maintained distinct thermal decomposition behaviors. At higher PVA concentrations, increased hydrogen bonding and crystalline restructuring, confirmed by FTIR through shifts near 1140 cm⁻¹ and significant changes in the –OH region, altered thermal profiles and reduced TGA accuracy. Together, these findings establish APC as a high-throughput alternative to NMR for multi-component polymer analysis and outline critical thermal and structural thresholds that influence TGA-based quantification. This work provides a framework for characterizing complex polymer networks in biomedical hydrogel systems.

Abstract: In this paper, a wireless network architecture is considered that combines double Intelligent Reflecting Surfaces (IRSs), Energy Harvesting (EH), and Non-Orthogonal Multiple Access (NOMA) with cooperative relaying (C-NOMA), to leverage the performance of Non-Line-of-Sight (NLoS) communication and incorporate energy efficiency in next-generation networks. To optimize the phase shifts of both IRSs, we employ a machine learning model that offers a low-complexity alternative to traditional optimization methods. This lightweight learning-based approach is introduced to predict effective IRS phase configurations without relying on solver-generated labels or repeated iterations. The model learns from channel behaviour and system observations, which allows it to react rapidly under dynamic channel conditions. Numerical analysis demonstrates the validity of the proposed architecture in providing considerable improvements in terms of spectral efficiency and service reliability through the integration of energy harvesting and relay-based communication, compared to conventional systems, thereby facilitating green communication systems.