Search Results (4,220)

Osseodensification (OD) compacts trabecular bone during implant site preparation rather than removing it, potentially enhancing primary stability versus conventional drilling. This review critically appraised clinical and preclinical evidence for OD’s biological and biomechanical efficacy in implant dentistry. We conducted electronic searches in seven databases (PubMed, Scopus, Web of Science, ScienceDirect, SciELO, LILACS, DOAJ) for the period January 2014 to March 2024. Studies comparing osseodensification with conventional drilling in clinical and large-animal models were included. Primary outcomes were insertion torque, implant stability quotient (ISQ), bone-to-implant contact (BIC), bone area fraction occupancy (BAFO), and complications. Of 75 retrieved records, 38 studies (27 clinical, 11 preclinical) provided analysable data. Based on descriptive averages from the narrative synthesis, osseodensification increased mean insertion torque by around 45% (range 32–59%) and initial ISQ by 3–10 units compared with conventional drilling. These gains permitted immediate loading in 78% of cases and shortened operating time (mean reduction 15–20 min). Animal studies demonstrated 12–28% higher BIC and increased peri-implant bone density at 4–12 weeks. No serious adverse events were recorded. Postoperative morbidity was similar between techniques. The collated evidence indicates that osseodensification significantly improves primary stability and may accelerate healing protocols, particularly in low-density (Misch D3–D4) bone. However, the predominance of short-term data and heterogeneity in surgical parameters limit definitive conclusions. Long-term randomised controlled trials with standardised protocols are needed before universal clinical recommendations can be established. Full article

Background: The training impulse (TRIMP) method integrates exercise duration and intensity, yet its application in school-based health programs is limited. This study examined dose–response relationships between TRIMP and changes in body fat percentage (%BF), blood pressure, and cardiorespiratory fitness (CRF) in adolescents. Methods: 145 adolescents (69 males, 76 females; aged 15.01 ± 0.54 years) completed eight-week interval-based physical education programs: high-intensity interval training (HIIT) or high-intensity plyometric training (HIPT). Heart rate was continuously monitored, and TRIMP values were calculated across 16 sessions. Outcomes included %BF, systolic (SBP) and diastolic blood pressure (DBP), and maximal oxygen consumption (VO_2max), assessed pre- and post-intervention. Results: HIIT elicited higher TRIMP than HIPT in females (η²p = 0.043). Linear dose–response patterns were identified (r = 0.18–0.36) (e.g., SBP in males, r = −0.38, p = 0.001; %BF in males, r = −0.36, p = 0.002). In males, one TRIMP unit reduced SBP by 1.8 mmHg (HIIT) and DBP by 1.6 mmHg (HIPT). In females, HIPT-derived TRIMP predicted reductions of ~0.6% in %BF and~0.9 mmHg in DBP. No significant associations were found for VO_2max. Conclusions: TRIMP provides a feasible and sensitive tool for quantifying internal training load in adolescent interventions, linking exercise dose to measurable health benefits and supporting its application in preventive contexts. Full article

Manure-based anaerobic digestion (AD) systems serve multiple functions, including waste treatment, energy recovery, and nutrient cycling. However, they also entail additional energy consumption and pollutant emissions. Life cycle assessment (LCA) methodology is typically used to holistically quantify the actual environmental impacts of these systems. Nevertheless, comprehensive reviews synthesizing LCA studies in this field remain limited. Following PRISMA guidelines, this study conducted a systematic literature review of LCA studies on manure-based AD systems, focusing on advancements, inconsistencies, and limitations in LCA methodologies and environmental impact results. The findings indicate considerable variability in functional units, allocation methods, system boundaries, and inventory analysis methods across the literature. These methodological discrepancies and the lack of standardized protocols result in remarkable variability in environmental impact potentials. Additionally, there is lack of consensus on the environmental benefits of AD systems compared to traditional manure management, and co-digestion with energy crops or food waste compared to mono-digestion of manure. Consequently, the environmental impacts of manure-based AD systems remain inconclusive due to methodological heterogeneity and data inconsistencies. Future research should develop scientific and standardized approaches and focus on the completeness of system boundaries, selection of key environmental impact categories, environmental load allocation, inventory data quality, and the transparency of the analysis. Full article

Environmental pollution caused by shipping has always received great attention from the international community. Currently, due to the difficulty of fully electrifying medium- and large-scale ships, the hybrid energy ship power system (HESPS) will be the main type in the future. Considering the economic and long-term energy efficiency of ships, as well as the uncertainty of the output power of renewable energy units, this paper proposes an improved design for an integrated power system for large cruise ships, combining renewable energy and a hybrid energy storage system. An energy management strategy (EMS) based on time-gradient control and considering load dynamic response, as well as an energy storage power allocation method that considers the characteristics of energy storage devices, is designed. A bi-level power capacity optimization model, grounded in comprehensive scenario planning and aiming to optimize maximum return on equity, is constructed and resolved by utilizing an improved particle swarm optimization algorithm integrated with dynamic programming. Based on a large-scale cruise ship, the aforementioned method was investigated and compared to the conventional planning approach. It demonstrates that the implementation of this optimization method can significantly decrease costs, enhance revenue, and increase the return on equity from 5.15% to 8.66%. Full article

One of the main challenges faced by DC microgrid (DCMG) is their low inertia, which leads to rapid and significant voltage fluctuations during load or generation changes. These fluctuations can negatively impact sensitive loads and protection devices. Previous studies have addressed this by enabling battery converters to mimic the behavior of synchronous generators (SGs), but this approach becomes ineffective when the converters or batteries reach their current or energy limits, leading to a loss of inertia and potential system instability. In interconnected multi-microgrid (MMG) systems, the presence of multiple batteries offers the potential to enhance system inertia, provided there is a coordinated control strategy. This research introduces a hierarchical control method that combines decentralized and centralized approaches. Decentralized control allows individual converters to emulate SG behavior, while the centralized control uses Internet of Things (IoT) technology to enable real-time coordination among all Energy Storage Units (ESUs). This coordination improves inertia across the DCMMG system, enhances energy management, and strengthens overall system stability. IoT integration ensures real-time data exchange, monitoring, and collaborative decision-making. The proposed scheme is validated through MATLAB simulations, with results confirming its effectiveness in improving inertial response and supporting the integration of renewable energy sources within DCMMGs. Full article

As power systems decentralize, Virtual Power Plants (VPPs) offer a promising approach to coordinate distributed energy resources (DERs) and enhance grid flexibility. However, real-world validation of VPP performance in Indonesia remains limited, especially regarding internationally aligned test standards. This study presents the design and experimental validation of a cluster-based VPP framework integrated with a centralized VPP Management System (VMS). Each cluster integrates solar photovoltaic (PV) system, battery energy storage system (BESS), and controllable load. A Local Control Unit (LCU) manages cluster operations, while the VMS coordinates power export–import dispatch, cluster-level aggregation, and grid compliance. The framework proposes a scalable VPP architecture and presents the first comprehensive experimental verification of key VPP performance indicators, including response time, adjustment rate, and accuracy, in the Indonesian context. Testing was conducted in alignment with the IEC TS 63189-1:2023 international standard. Results suggest real time responsiveness and indicate that, even at smaller scales, VPPs may contribute effectively to voltage control while exhibiting minimal influence on system frequency in interconnected grids. These findings confirm the capability of the proposed VPP framework to provide reliable real time control, ancillary services, and aggregated energy management. Its cluster-based architecture supports scalability for broader deployment in complex grid environments. Full article

A hybrid control architecture is proposed for enhancing the stability and energy management of DC microgrids (DCMGs) integrating photovoltaic generation, batteries, and supercapacitors. The approach combines nonlinear Sliding Mode Control (SMC) for fast and robust DC bus voltage regulation with a Deep Q-Learning (DQL) agent that learns optimal high-level policies for charging, discharging, and load management. This dual-layer design leverages the real-time precision of SMC and the adaptive decision-making capability of DQL to achieve dynamic power sharing and balanced state-of-charge levels across storage units, thereby reducing asymmetric wear. Simulation results under variable operating scenarios showed that the proposed method significantly improvedvoltage stability, loweredthe occurrence of deep battery discharges, and decreased load shedding compared to conventional fuzzy-logic-based energymanagement, highlighting its effectiveness and resilience in the presence of renewable generation variability and fluctuating load demands. Full article

Origami structures exhibit significant potential for rapid deployment in post-disaster response and temporary architecture due to their ability to quickly fold and deploy. Further development of these structures into modular components that can be assembled into large-scale architectural systems holds great importance for the fields of architecture and civil engineering. In this study, a thin-walled foldable column was developed based on the “pillow box” origami pattern. This column maintains its three-dimensional configuration during folding, owing to its inherent self-locking characteristic. Two crease-weakening strategies (“dashed-line” and “slit-hole”) were proposed and experimentally validated. A systematic numerical study was conducted to investigate the axial compressive performance of pillow box columns with weakened curved creases. The results indicate that both weakening strategies effectively enable folding while preserving global integrity under compression. The pillow box column with “dashed-line” creases (OCC-D) demonstrated superior load-bearing capacity, with a load-to-weight ratio of up to 658.9, nearly twice that of the corresponding conventional square tube. Parametric analysis of the crease geometry further revealed that increasing the number of crease units enhances the load-bearing performance, and the optimal performance is achieved when the spacing between slit openings equals the slit length ($l_h=l_c$). These findings highlight the advantages of pillow box origami columns as thin-walled load-bearing components, offering new insights for the rapid construction and lightweight design of architectural structures. Full article

This paper presents a systematic approach to the development and validation of a monitoring and protection system based on the IEC 61850 standard, evaluated through hardware-in-the-loop (HIL) testing. The study utilized an already existing model of a modern power grid consisting of the IEEE 9-bus power system integrated with a large-scale wind power plant (LSWPP). The SEL-487B Relay was configured to protect the PCC using a low-impedance busbar differential monitoring and protection system equipped with adaptive setting group logic that automatically transitions between Group 1 and Group 2 based on system loading conditions. Significant steps were followed for selecting and configuring instrument transformers and implementing relay logic in compliance with IEEE and IEC standards. Real-time digital simulation using Real-Time Digital Simulator (RTDS) hardware and its software, Real-time Simulation Computer-Aided Design (RSCAD), was used to assess the performance of the overall monitoring and protection system, focusing on the monitoring and publishing of the selected electrical and mechanical measurements from a selected wind turbine generator unit (WTGU) on the LSWPP side through the IEC 61850 standard network, and on the behavior of the monitoring and protection system under initial and increased load conditions through monitoring of differential and restraint currents. The overall monitoring and protection system was tested under both initial and increased load conditions, confirming its capability to reliably publish analog values from WTGU13 for availability on the IEC 61850 standard network while maintaining secure protection operation. Quantitatively, the measured differential (operate) and restraint currents were 0.32 PU and 4.38 PU under initial loading, and 1.96 PU and 6.20 PU under increased loading, while total fault clearance times were 606.667 ms and 706.667 ms for faults under initial load and increased load demand conditions, respectively. These results confirm that the developed framework provides accurate real-time monitoring and reliable operation for faults, while demonstrating a practical and replicable solution for monitoring and protection at transmission-level PCCs within renewable-integrated networks. Full article

Pollution and freshwater scarcity, coupled with the energy sector’s continued dependence on fossil fuels, constitute a dual challenge to sustainable development. A promising response is biosystems that jointly address wastewater treatment and the production of renewable products. This review centers on a managed consortium of aquatic macrophytes and microalgae, in which the spatial architecture of plant communities, rhizosphere processes, and the photosynthetic activity of microalgae act in concert. This configuration simultaneously expands the spectrum of removable pollutants and yields biomass suitable for biorefinery, thereby linking remediation to the production of energy carriers and bioproducts within a circular bioeconomy. The scientific novelty lies in treating the integrated platform as a coherent technological unit, and in using the biomass “metabolic passport” to align cultivation conditions with optimal valorization trajectories. The work offers a practical framework for designing and scaling such consortia that can reduce the toxicological load on aquatic ecosystems, return macronutrients to circulation, and produce low-carbon energy carriers. Full article

This article presents an integrated assessment of pasture ecosystem degradation under conditions of extreme aridity in the Atyrau Region, where high livestock density, limited grazing capacity, and institutional fragmentation of land tenure exacerbate degradation risks. The study aimed to conduct a spatio-temporal analysis of pasture conditions and identify critical load zones to support sustainable management strategies. The methodology was based on a multi-factor Anthropogenic Load (AL) model integrating (1) calculation of pasture load (PL) using 2023 agricultural statistics with livestock numbers converted into livestock units; (2) spatial analysis of grazing concentration through Kernel Density Estimation in ArcGIS 10.8; (3) assessment of infrastructural accessibility (Accessibility Index, A_i); and (4) quantitative evaluation of institutional land use organization (Institutional Index, I_i). This integrative approach enabled the identification of stable, transitional, and critically overloaded zones and provided a cartographic basis for sustainable management. Results revealed persistent degradation hotspots within 3–5 km of water sources and settlements, while up to 40% of productive pastures remain excluded from use. The proposed AL model demonstrated high reproducibility and applicability for environmental monitoring and regional land use planning in arid regions of Central Asia. Full article

High-precision angular positioning mechanisms are essential across diverse scientific and industrial applications, from optical instrumentation to automated mechanical systems. Conventional bronze–steel gear reduction units, while reliable, are often heavy, costly, and unsuitable for chemically aggressive or vacuum environments, limiting their use in advanced research setups. This work introduces a novel 1:360 gear reduction system manufactured by resin-based additive manufacturing, designed to overcome these limitations. The compact worm–gear assembly translates a single crank rotation into a precise one-degree indicator displacement, enabling fine and repeatable angular control. A primary application is the alignment of parabolic mirrors in schlieren systems, where accurate tilt adjustment is critical to correct optical alignment; however, the design is broadly adaptable to other precision positioning tasks in laboratory and industrial contexts. Compared with conventional assemblies, the resin-based reducer offers reduced weight, chemical and vacuum compatibility, and lower production cost. Its three-stage reduction design further enhances load-bearing capacity, achieving approximately double the theoretical torque transfer of equivalent commercial systems. These features establish the device as a robust, scalable, and automation-ready solution for high-accuracy angular adjustment, contributing both to specialized optical research and general-purpose precision engineering. Full article

Plastic waste, particularly polyethylene terephthalate (PET), poses a growing environmental challenge. This study investigates the feasibility of incorporating recycled PET into clay bricks as a sustainable alternative in construction. Bricks were fabricated with 0%, 5%, 10%, and 15% PET content. Clay characterization included particle size distribution, Atterberg limits, and moisture content. Physical and mechanical tests evaluated dimensional variability, void percentage, warping, water absorption, suction, unit compressive strength ($f_b^{\prime }$), and prism compressive strength ($f_m^{\prime }$). Statistical analysis (Shapiro–Wilk, p < 0.05) validated the results. PET addition improved physical properties—reducing water absorption, suction, and voids—while slightly compromising mechanical strength. The 15% PET mix showed the best overall performance ($f_b^{\prime }$ = 24.00 kg/cm²; $f_m^{\prime }$ = 20.40 kg/cm²), with uniform deformation and lower absorption (18.7%). Recycled PET enhances key physical attributes of clay bricks, supporting its use in eco-friendly construction. However, reduced compressive strength limits its structural applications. Optimizing PET particle size, clay type, and firing conditions is essential to improve load-bearing capacity. Current formulations are promising for non-structural uses, contributing to circular material strategies. Full article

In the context of low carbon, achieving accurate forecasting of electrical energy is critical for power management with the continuous development of power systems. For the sake of improving the performance of load forecasting, an improved MNet-Atten electric vehicle charging load forecasting based on composite decomposition and the evolutionary predator–prey and strategy model is proposed. In this light, through the data decomposition theory, each subsequence is processed using complementary ensemble empirical mode decomposition and filters out high-frequency white noise by using singular value decomposition based on matrix operation, which improves the anti-interference ability and computational efficiency of the model. In the model construction stage, the MNet-Atten prediction model is developed and constructed. The convolution module is used to mine the local dependencies of the sequences, and the long term and short-term features of the data are extracted through the loop and loop skip modules to improve the predictability of the data itself. Furthermore, the evolutionary predator and prey strategy is used to iteratively optimize the learning rate of the MNet-Atten for improving the forecasting performance and convergence speed of the model. The autoregressive module is used to enhance the ability of the neural network to identify linear features and improve the prediction performance of the model. Increasing temporal attention to give more weight to important features for global and local linkage capture. Additionally, the electric vehicle charging load data in a certain region, as an example, is verified, and the average value of 30 running times of the combined model proposed is 117.3231 s, and the correlation coefficient PCC of the CEEMD-SVD-EPPS-MNet-Atten model is closer to 1. Furthermore, the CEEMD-SVD-EPPS-MNet-Atten model has the lowest MAPE, RMSE, and PCC. The results show that the model in this paper can better extract the characteristics of the data, improve the modeling efficiency, and have a high data prediction accuracy. Full article

Optimally positioning renewable-based distributed generation (DG) units is vital for mitigating technical challenges in active distribution networks (ADNs). With the goal of achieving technical goals such as reduced losses and mitigated unstable voltage, two available optimization methods have been combined for positioning wind-energy DGs: grey wolf optimization (GWO) and opposition-based learning (OBL), which tries out opposite possibilities for each assessed population, thus addressing GWO’s susceptibility to becoming stuck in local optima. This new fusion technique enhances the algorithm’s scrutiny of each area under consideration and reduces the likelihood of premature convergence. Results show that, compared with standard GWO, the proposed OBL-GWO reduced active power losses by up to 95.16%, improved total voltage deviation (TVD) by 99.7%, and increased the minimum bus voltage from 0.907 p.u. to 0.994 p.u. In addition, the voltage stability index (VSI) was also enhanced by nearly 30%. The proposed methodology outperformed both standard GWO on the IEEE 33-bus test system and comparable techniques reported in the literature consistently. By accounting for the uncertainty in wind generation, load demand, and future growth, this framework offers a more reliable and practical planning approach that better reflects real operating conditions. Full article