Search Results (10,785)

With the rapid development of renewable energy, wind power forecasting has become increasingly important in power system scheduling and management. However, the forecasting of wind power is subject to the complex influence of multiple variable features and their interrelationships, which poses challenges to traditional forecasting methods. As an effective feature extraction technique, representation learning can better capture complex feature relationships and improve forecasting performance. This paper proposes a two-stage forecasting framework based on lightweight representation learning and multivariate feature mixing. In the representation learning stage, the efficient spatial pyramid module is introduced to reconstruct the dilated convolution part of the original TS2Vec representation learning model to fuse multi-scale features and better improve the gridding effect caused by dilated convolution while significantly reducing the number of parameters in the representation learning model. In the feature mixing stage, TSMixer is used as the basic model to extract cross-dimensional interaction features through its multivariate linear mixing mechanism, and the SimAM lightweight attention mechanism is introduced to adaptively focus on the contribution of key time steps and optimize the allocation of forecasting weights. The experimental results conducted on actual wind farm datasets show that the model proposed in this paper significantly improves the accuracy of wind power forecasting, providing new ideas and methods for the field of wind power forecasting. Full article

Presently, Java is a fundamental object-oriented programming language that can be mastered by any student in information technology or computer science. To assist both teachers and students, we developed the Java Programming Learning Assistant System (JPLAS). It offers several types of practice problems with different levels and learning goals for step-by-step self-study, where any answer is automatically marked in the system. One challenge for teachers that is addressed with JPLAS is the generation of proper exercise problems that meet learning requirements. We implemented programs for generating new problems from given source codes, as collecting and evaluating suitable codes remains time-consuming. In this paper, we present an automatic code generation tool using generative AI to solve this challenge. Prompt engineering is used to help generate an appropriate source code, and the quality is controlled by optimizing the prompt based on the outputs. For applications in JPLAS, we implement a web application system to automatically generate an element fill-in-the-blank problem (EFP) in JPLAS. For evaluation, we select the element fill-in-the-blank problem (EFP) as the target type in JPLAS and generate several instances using this tool. The results confirm the validity and effectiveness of the proposed method. Full article

As the proportion of renewable energy continues to increase, the large-scale grid integration of photovoltaic (PV) generation presents new technical challenges for reactive power balance in power systems. This paper proposes a coordinated reactive power and voltage optimization method based on Filtered Multiband Decomposition (FMD). First, to address the stochastic fluctuations of PV power, an improved FMD-based prediction model is developed. The model employs an adaptive finite impulse response (FIR) filter to decompose signals and captures periodicity and uncertainty through kurtosis-based feature extraction. By utilizing adaptive function windows for multiband signal decomposition, combined with kernel principal component analysis (KPCA) for dimensionality reduction and a long short-term memory (LSTM) network for prediction, the model significantly enhances forecasting accuracy. Second, to tackle the challenges of integrating high-penetration distributed PV while maintaining reactive power balance, a multi-head attention-based velocity update strategy is introduced within a multi-objective particle swarm optimization (MOPSO) framework. This strategy quantifies the spatial distance and fitness differences of historical best solutions, constructing a dynamic weight allocation mechanism to adaptively adjust particle search direction and step size. Finally, the effectiveness of the proposed method is validated through an improved IEEE 33-bus test case. Full article

The main aim of this research was to synthesize the new geopolymer composite and test its antibacterial properties. The new composites are based on a geopolymer matrix, with the addition of carbon fiber, nano-silica and antibacterial nanopowder. The first stage of this research was the synthesis of geopolymer composites containing variable proportions of nano-additives and, as a reference material, cement. The next step was bacterial cultivation. Two different bacterial strains were selected, Gram-positive and Gram-negative (Escherichia coli and Staphylococcus aureus). In this stage, the agar microbiological medium is used for the evaluation of bacterial growth inhibition by cement and geopolymers. In the final stage, the growth of the colony was observed and the pH measurements were taken. The final assessment of efficiency was made by using optical microscopy and a colony counter based on the Petri dish. The test performed showed that the main mineralogical components are quartz, 55.0%, and mullite, with 42.1% of crystalline ingredients. EDS analysis shows that the main oxide component is SiO₂, about 50.9%. The obtained results connected with bacteria growth show the growth of both types of bacteria on materials; however, after several days, the growth was inhibited. An assessment of microorganism growth inhibition by cement and geopolymers shows the better efficiency of geopolymer composites in this area for both types of colonies (Gram-positive and Gram-negative). The new element in this research was to plan the research from the point of view of its application in the water environment. The provided research can be useful for the inhibition of biofouling phenomena on marine and inland water infrastructure. Full article

Lignocellulosic biomass, derived from plant materials, represents a renewable alternative to fossil fuels and plays a crucial role in advancing environmental sustainability. This systematic review investigates recent developments in the conversion of lignocellulosic biomass into biofuels, with a focus on pre-treatment technologies that enhance enzymatic hydrolysis, a critical step in efficient biofuel production. This review addresses two primary questions: (1) What are the most effective pre-treatment methods for enhancing enzymatic hydrolysis in lignocellulosic biomass conversion? (2) How do these pre-treatment methods compare in terms of efficiency, environmental impact, and economic feasibility? Consequently, studies were selected based on inclusion criteria that focus on research investigating these pre-treatment methods and their comparative performance. A structured search of original studies was applied across databases such as Crossref, Google Scholar, Scopus, PubMed, and Semantic Scholar, resulting in the inclusion of 17 peer-reviewed articles published between 2019 and 2024. The findings highlight effective pre-treatment methods that significantly improve enzymatic accessibility and bioethanol yields. However, ongoing challenges such as feedstock variability, process efficiency, and cost-effectiveness remain. These results highlight the need for further research and development to optimize conversion technologies and identify new areas for exploration. Full article

Here, a pure and systematic numerical study is conducted to investigate the detonation propagation in a curvature bend by focusing on the combined effect of curvature and cross-section area with a simple two-step chemical reaction model. In a channel with a small radius of curvature R/λ < 10, the detonation wave presents a periodical failure-reinitiation mode. The detonation wave near the inner wall cannot sustain itself due to the strong curvature effect. In contrast, the compression of the outer wall strengthens the front and can form a transverse detonation wave to re-initiate the failed detonation near the inner wall. In a channel with a large radius of curvature R/λ > 10, the inner wall’s weak rarefaction effect is not strong enough to completely quench the detonation wave. In the same way, the numerical results also show that a large area expansion rate inevitably produces a strong rarefaction effect near the inner wall, causing wave front decoupling and even failure. According to the radius of the curvature and the area increase rate, there are three different modes of detonation propagation: stable, critical, and unstable. By defining a new parameter κ to characterize different detonation modes and by considering both the curvature and area expansion effect, we found that the threshold κ = 0.33 can be used to distinguish the unstable and critical modes. Full article

By decreasing manufacturing costs for different civic purposes, glass recycling is an economically significant technology that also helps conserve natural resources and mitigates environmental problems. Throughout the recycling process, this study used recycled domestic glass in compliance with European guidelines for recycling of household garbage. The purpose of this research is to examine the chemical and mechanical properties of recycled and crushed glass with particle sizes varying from 0.1 mm to 2 mm as a function of various treatment temperatures. This might pave the way for novel building materials, artwork, and interior design components, among other potential uses. “Silica glass”, the most common and ancient kind of glass, which includes SiO_k, Na_kO, CaO, and small amounts of other elements, was utilized in the investigation. Several materials can be successfully modified or altered using step heat treatment. The mechanical and chemical properties of recycled and shattered glass were assessed using microhardness, compressive, and chemical testing. These samples were then compared to mosaics from Murano, Italy, and Dynasty Smalti, China. The recycled and heat-treated glass produced microhardness values of 550.6 HV and 555.0 HV, respectively, when tested with forces of 0.981 N and 2.942 N. These values were higher than those of Murano (Italy) and were comparable to those of Dynasty Smalti mosaic (China). Furthermore, compression testing demonstrated that tempered and heat-tempered glass, which might include up to 5 g of TiO₂, could endure compressive strains of up to 16 MPa. This is in sharp contrast to Dynasty Smalti, which could only withstand tensions of 6–8 MPa, and Murano, which could only withstand stresses of 3–4 MPa. Tests conducted chemically over a seven-day period using KOH at 30 g/L and 100 g/L, along with HCl at 3% and 18%, showed that the samples did not alter in any way; their surface, color, and weight were untouched. Crushing and heating recycled glass makes it a viable alternative to using new glass in civil engineering projects. This helps make material reuse more efficient, which in turn helps the environment. Sturdy and resilient in a variety of contexts, the material shares mechanical and chemical properties with standard mosaics. Full article

Background/Objectives: Subperiosteal implants (SPIs) were first used in the 1940s, but due to their complications and the rise of dental implants, they were discontinued. Thanks to new technologies and new materials, nowadays they are being used again and studied as a treatment for severe bone defects. This review analyzes the clinical results—survival rates and complications—of SPIs used to support full arch rehabilitations of severely resorbed maxillae and mandibles, comparing the outcomes resulting from implant placement conducted in one or two surgical interventions. Methods: An automated search was conducted in four databases (Medline/Pubmed, Scopus, Web of Science, and Cochrane Library), as well as a manual search for relevant clinical articles published before 28 February 2025. The review included human studies with at least four patients, in which SPIs were placed to restore full-arch edentulous maxillae and mandibles. Quality of evidence was evaluated using the Newcastle–Ottawa Quality Assessment Scale and the Joanna Briggs Institute Critical Appraisal tool. Results: A total of 14 studies met the inclusion criteria and were included for analysis, including 958 patients and 973 SPIs. The survival rate was 100% when one surgical intervention was performed and 85% when two interventions were performed after 4–38 months and 3–22 years follow-up, respectively. Conclusions: SPIs would appear to offer a good alternative for patients with severe bone atrophies, especially SPIs fabricated using digital techniques in a single step, presenting promising survival rates and a low complication rate, although more randomized clinical trials with long-term follow-up are needed. Full article

Background/Objectives: Colony stimulating factor 1 receptor kinase (CSF1R) is a well-validated molecular target in drug discovery for various reasons. Based on the structure of an early lead molecule identified in our lab and the marketed drug Pexidartinib (PLX3397), we merged fragments of Pexidartinib with our pyrrolo[2,3-d]pyrimidine nucleus, and the idea was supported by initial molecular docking studies. Thus, several new compounds were synthesized with Pexidartinib fragments on C4, C5, and C6 on the pyrrolopyrimidine scaffold using molecular hybridization. Methods: Nine final products were synthesized using a combination of Buchwald-Hartwig and Suzuki-Miyaura cross-coupling reactions in three to four steps and in good yields. The analogues were subsequently profiled as CSF1R inhibitors in enzymatic and cellular assays, and ADME properties were evaluated for some derivatives. Results: N-Methyl-N-(3-methylbenzyl)-6-(6-((pyridin-3-ylmethyl)amino)pyridin-3-yl)-7H-pyrrolo[2,3-d]pyrimidin-4-amine (12b) emerged as the most potent CSF1R inhibitor, showing low-nanomolar enzymatic activity, cellular efficacy, and favorable ADME properties, highlighting its promise as a lead compound for further development. Conclusions: These findings suggest that combining structural elements from previously reported CSF1R inhibitors such as Pexidartinib could guide the development of improved drug candidates targeting this kinase. Full article

Various sectors focus on transitioning to clean and renewable energy sources, particularly airport microgrids (AMGs), which offer the potential for highly reliable and resilient operations. As airports increasingly integrate renewable energy sources, ensuring stable and efficient power becomes a critical challenge. In this context, maintaining proper frequency is essential for the reliable operation of AMGs, which helps maintain grid stability and reliable operation. This paper proposes a new hybrid disturbance observer-based controller with a fractional-order controller (DOBC/FOC) for operating AMGs with high levels of renewable energy integration and advanced frequency regulation (FR) capabilities. The proposed controller utilizes DOBC coupled with a non-integer FOC for load frequency control (LFC), optimized for peak performance under varying operational conditions. In addition, a decentralized control strategy is introduced to manage the participation of electric vehicles and lithium-ion battery systems within the airport’s energy ecosystem, enabling effective demand response and energy storage utilization. Furthermore, the parameters of these controllers are optimized simultaneously to ensure optimal performance in both transient and steady-state conditions. The proposed DOBC/FOC controller demonstrates strong performance and reliability according to simulation outcomes, showcasing its superior performance in maintaining frequency stability, reducing fluctuations, and ensuring continuous power supply in diverse operating scenarios, such as 55.5% and 76.5% in step load perturbations when compared to the utilization of electric vehicles (EVs) and electric aircraft (EAC), respectively. These results underline the potential of this approach in enhancing the resilience and sustainability of AMG and contributing to more intelligent and eco-friendly airport infrastructure. Full article

The structural deformations induced by rocket launch vibrations, on-orbit thermal gradients, and gravitation fluctuations can lead to significant deployment errors for large-aperture, segmented space telescopes. As the size and number of segments increase in future telescopes, the optical-based methods for detecting deployment errors suffer from the range limitations of the millimeter scale and time-consuming processes of the month scale. To address this, we propose a new method for rapid-deployment error detection based on long-range, high-precision capacitive edge sensors. These sensors feature a measurement range of ±13 mm, with a precision better than 7.3 nm, enabling efficient and simultaneous error detection across all segments. This approach significantly reduces the time and steps required compared to traditional optical methods. Through experimental validation, the designed system demonstrated the ability to detect and correct large deployment errors and maintain co-phasing precision, meeting the stringent requirements for future space telescopes. The proposed sensor system enhances deployment efficiency, offering a viable solution for the next generation of segmented space telescopes. Full article

Data security is critical, particularly in medical imaging, yet remains challenging. Many research efforts have focused on enhancing medical image security, particularly during network transmission. Ensuring confidentiality and authenticity is a key priority for researchers. However, traditional encryption methods are unsuitable for IoT environments due to data size limitations. Lightweight encryption algorithms that preserve confidentiality, integrity, and authenticity can address these limitations. This paper proposes an efficient, lightweight method to encrypt and authenticate medical images in healthcare systems. The approach splits images into diagonal and non-diagonal blocks, and then processes them in two phases: (1) non-diagonal blocks are permuted using inter-block differences and XORed with diagonal blocks for substitution; (2) diagonal blocks are encrypted via AES and enhanced CBC mode with a tag mechanism for integrity. Security tests (histograms, correlation, entropy, NPCR, UACI) verify the scheme’s robustness. The results show that the model outperforms existing techniques in efficacy and attack resistance, making it viable for medical IoT and smart surveillance. Full article

Shaping or splitting of a Gaussian beam is often desired to optimise laser–material interactions, improving throughput and quality. This can be achieved holographically using liquid crystal-on-silicon spatial light modulators (LC-SLMs). Until recently, maximum exposure has been limited to circa 120 W average power with a Gaussian profile, restricting potential applications due to the non-linear (NL) phase response of the liquid crystal above this threshold. In this study, we present experimental tests of a new SLM device, demonstrating high first-order diffraction efficiency of η = 0.98 ± 0.01 at 300 W average power and a phase range Δφ > 2π at P = 383 W, an exceptional performance. The numerically calculated device temperature response with power closely matches that measured, supporting the higher power-handling capability. Surface modification of mild steel and molybdenum up to P = 350 W exposure is demonstrated when employing a single-mode (SM) fibre laser source. Exposure on mild steel with a vortex beam (m = +6) displays numerous ringed regions with varying micro-structures and clear elemental separation created by the radial heat flow. On molybdenum, with multi-spot Gaussian exposure, both MoO₃ films and recrystallisation rings were observed, exposure-dependent. The step change in device capability will accelerate new applications for this LC-SLM in both subtractive and additive manufacturing. Full article

This paper presents a new method for the simultaneous estimation of system states and unknown inputs in fractional-order Takagi–Sugeno (FO-TS) systems with unmeasurable premise variables (UPVs), by introducing a fractional-order proportional-integral unknown input observer (FO-PIUIO) based on partial state augmentation. This approach permits the estimation of both states and unknown inputs, which are essential for system monitoring and control. Partial state augmentation allows the integration of unknown inputs into a partially augmented model, ensuring accurate estimates of both states and unknown inputs. The state estimation error is formulated as a perturbed system. The convergence conditions for the state estimation errors between the system and the observer are derived using the second Lyapunov method and the $L_2$ approach. Compared to traditional integer-order unknown input observers or fuzzy observers with measurable premise variables, in our method, fractional-order dynamics are combined with partial state augmentation uniquely for the persistent estimation of states along with unknown inputs in unmeasurable premise variable systems. Such a combination allows for robust estimation even under uncertainties in systems and long memory phenomena and is a significant step forward from traditional methods. Finally, a numerical example is provided to illustrate the performance of the proposed observer. Full article

Aims: To investigate sociodemographic associations with medicated health conditions, general practitioner (GP) contacts, and computed tomography (CT) scans of the lung, as 12-month precursors of diagnosis of lung cancer at a local stage (cancers localized to the primary site of bronchus and lung). Methods: Cancer Registry data for New South Wales (NSW) adults diagnosed with lung cancer (ICD-10 C33-34) since the Census of August 2016 (n = 6160) were linked at person level with census and other administrative data. These included residents diagnosed with lung cancer from September 2016 to December 2018. Structural equation modelling indicated adjusted measures of associations with lung cancer, including adjusted odds ratios (aORs), in stepped analyses. Results: The first part of the multivariate modelling showed age, major city residence, and other sociodemographic characteristics that were associated with numbers of medicated conditions. The second part showed the numbers of medicated conditions and other sociodemographic characteristics that were associated with the number of GP consultations. The third part of the modelling showed the numbers of GP consultations and other sociodemographic characteristics that were associated with having CT lung scans. Modelling showed that having CT scans and being female were the main predictors of lung cancer diagnosis at a local stage, with aORs of 2.30, 95%CI 2.01–2.63 and 1.39, and 95%CI 1.23–1.58, respectively. The modelling also showed age, GP consultations, residence in a major city, and other sociodemographic characteristics to be associated with having CT scans. Conclusions: The findings of the study indicate the main precursors of lung cancer diagnosis at a local stage after multivariate adjustment. Irrespective of causal significance, results reveal population-wide characteristics for targeting screening for early detection. They demonstrate the potential value of person-level linkage of cancer-registry data with census and other administrative data for this purpose. Our study of linked cancer-registry and census data revealed broad descriptive features of pathways to early diagnosis of relevance to service screening and planning. Full article