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With the rapid expansion of data, the problem of data imbalance has become increasingly prominent in the fields of medical treatment, finance, network, etc. And it is typically solved using the oversampling method. However, most existing oversampling methods randomly sample or sample only for a particular area, which affects the classification results. To solve the above limitations, this study proposes an imbalanced data oversampling method, SD-KMSMOTE, based on the spatial distribution of minority samples. A filter noise pre-treatment is added, the category information of the near-neighbouring samples is considered, and the existing minority class sample noise is removed. These conditions lead to the design of a new sample synthesis method, and the rules for calculating the weight values are constructed on this basis. The spatial distribution of minority class samples is considered comprehensively; they are clustered, and the sub-clusters that contain useful information are assigned larger weight values and more synthetic sample numbers. The experimental results show that the experimental results outperform existing methods in terms of precision, recall, F1 score, G-mean, and area under the curve values when the proposed method is used to expand the imbalanced dataset in the field of medicine and other fields.

Machinery operation is a major source of carbon emissions in building deconstruction. Early intervention through Design for Deconstruction (DfD) is crucial for emission reduction, yet the factors influencing these emissions are underexplored. This study integrates parametric BIM with multi-objective optimization (MOO) to assess mechanical carbon emissions in deconstruction. Using the Octopus solver in Grasshopper for Rhino, the study analyzes independent variables—possible working hours (PWH), vertical speed (VS), and horizontal speed (HS)—and dependent variables—minimum mechanical carbon emissions (MCE (min)), minimum deconstruction period (DP (min)), and maximum working efficiency (WE (max)). A lightweight steel roof truss structure is analyzed, comparing real-world deconstruction with optimized DfD schemes. Sensitivity analysis for BIM-MOO optimized results reveal that: (1) Adjusting PWH, VS, and HS significantly affects WE and DP, though with limited impact on carbon emissions; (2) VS influences WE and DP more than HS; (3) Limiting DP is essential for balancing WE, DP, and MCE, with WE adjusted to 20–60% and modifications to PWH and VS achieving balanced management. This study underscores the importance of early design and real-time adjustments for efficient, low-emission deconstruction, supporting the advancement of green building practices.

The sol–gel chemistry of silica has long been used for manipulating the size, shape, and microstructure of mesoporous silica particles. This manipulation is performed in mild conditions through controlling the hydrolysis and condensation of silicon alkoxide. Compared to amorphous silica particles, the preparation of mesoporous silica, such as MCM-41, using the sol–gel approach offers several unique advantages in the fields of catalysis, medicament, and environment, due to its ordered mesoporous structure, high specific surface area, large pore volume, and easily functionalized surface. In this review, our primary focus is on the latest research related to the manipulation of mesoporous silica architectures using the sol–gel approach. We summarize various structures, including hollow, yolk-shell, multi-shelled hollow, Janus, nanotubular, and 2D membrane structures. Additionally, we survey sol–gel strategies involving the introduction of various functional elements onto the surface of mesoporous silica to enhance its performance. Furthermore, we outline the prospects and challenges associated with mesoporous silica featuring different structures and functions in promising applications, such as high-performance catalysis, biomedicine, wastewater treatment, and CO2 capture.

Transition-metal-coordinating nitrogen-doped carbon catalysts （M-N/C, M = Co, Fe, Mn, Ni, etc.） are considered one of the most promising nonprecious-metal electrocatalysts for the oxygen reduction reaction （ORR）. However, they suffer from low ORR catalytic activity, and their active sites have not been fully identified. Herein, we report the synthesis of a porous Co-N/C hollow-sphere electrocatalyst by carbonization of metanilic anions between the layers of a Co-A1 layered double hydroxide. The as-prepared Co-N/C catalyst exhibited excellent ORR catalytic activity with a high half-wave potential and a large diffusion-limited current in alkaline and neutral solutions. The performance of the catalyst was comparable to those of commercial Pt/C electrocatalysts. Through investigating the effects of mask ions （SCN- and F-） on the ORR activity of the Co-N/C catalyst, and comparing the ORR activity before and after the destruction of Co-Nx sites in different pH media, we concluded that the Co-Nx sites act directly as the ORR active sites in acidic and neutral solutions, but have a negligible effect on the ORR activity in alkaline conditions.

Metal-halide perovskites are novel optoelectronic materials that are considered good candidates for solar harvesting and light emitting applications. In this study, we develop a reproducible and low-cost approach for synthesizing high- quality cesium lead halide perovskite （CsPbX3, X = CI, Br, and I or C1/Br and I/Br） nanocrystals （NCs） by direct heating of precursors in octadecene in air. Experimental results show that the particle size and composition of as-prepared CsPbX3 nanocrystals can be successfully tuned by a simple variation of reaction temperature. The emission peak positions of the as-prepared nanocrystals can be conveniently tuned from the UV to the NIR （360-700 nm） region, and the quantum yield of the as-obtained samples （green and red emissions） can reach up to 87%. The structures and chemical compositions of the as-obtained NCs were characterized by transmission electron microscopy, X-ray diffraction, and elemental analysis. This proposed synthetic route can yield large amounts of high-quality NCs with a one-batch reaction, usually on the gram scale, and could pave the way for further applications of perovskite-based light-emitting and photovoltaic solar cells.

With the surge of IoT devices, sensors, and smart terminals has led to distributed data sources and vast volumes of data. These challenges traditional centralized networks and cloud computing architectures, which struggle with bandwidth, latency, and storage limitations. Consequently, decentralized edge computing is crucial, enabling data processing and analysis at the network's edge to alleviate data return pressure and enhance system response speed and reliability. However, traditional centralized data aggregation methods become inefficient in the face of massive data and computing resources, resulting in long transmission times and low processing efficiency. To address these issues, this paper presents a hierarchical distributed edge data aggregation reporting method based on cluster center selection (HDAR-CCS). This method employs a staged approach to distributed data aggregation, utilizing parallel processing at each stage to efficiently handle data from multiple edge data centers. Additionally, an optimal cluster center selection algorithm is proposed, integrating the distances between cluster centers and available network resources. By establishing a selection criterion based on these distances, we design an effective scheme for choosing initial and subsequent cluster centers. Experimental results demonstrate that our approach outperforms existing algorithms, effectively meeting the low latency, high bandwidth, and efficient processing needs of intelligent applications.

In the path of walking on the road of sustainable and eco-friendly production methods for manufacturing nanomaterials and utilizing them in environmental applications, this article deals with the prosperous synthesis of a biogenic cyclam-functionalized homologous SBA-15 (BCFH-SBA-15). For this purpose, the agricultural waste of the extensively consumed sorghum was used as a rich source of silica in the preparation of BCFH-SBA-15 with a bimodal micro-mesoporous architecture and a substantial surface area of 325 m 2  g –1 through a simple one-pot environmentally friendly approach. The material was structurally characterized through the use of different instrumental analyses such as XRD, FTIR, FESEM, TEM, and nitrogen adsorption/desorption isotherms. BCFH-SBA-15 proved to be highly efficient in adsorbing Ni(II) in aqueous solutions, as confirmed by the most reliable classical models utilized for determining isotherm, thermodynamic, and kinetic adsorption parameters. The Langmuir isotherm model provided the most accurate representation of the experimental results, and it was used to calculate the maximum adsorption capacity of BCFH-SBA-15 under optimal conditions (pH = 6.0, adsorbent dose = 3.00 mg, contact time = 20 min). The maximum adsorption capacity at four temperatures of 298, 303, 308, and 313 K was estimated to be 243.36, 253.87, 260.95, and 266.28 mg g –1 , respectively; surpassing most previously reported adsorbents for Ni(II) adsorption. The thermodynamic data of Ni(II) adsorption on the BCFH-SBA-15 indicated a strong chemisorption ( △ H ads . ∘  = +122.61 kJ mol –1 ) and spontaneous process ( △ G ads . ∘ . = −29.161 to −36.801 kJ mol –1 ) with a low degree of randomness ( △ S ads . ∘ .  = 0.5093 kJ mol –1  K –1 ).

The commercialization of lithium manganese oxide (LMO) is seriously hindered by several drawbacks, such as low initial Coulombic efficiency, the degradation of the voltage and capacity during cycling, and the poor rating performance. Developing a simple and scalable synthesis for engineering with surface coating layers is significant and challenging for the commercial prospects of LMO oxides. Herein, we have proposed an efficient engineering strategy with a Nb2O5 coating layer. We dissolved niobate (V) ammonium oxalate hydrate and stoichiometric rich LMO (RLM) in deionized water and stirred constantly. Then, the target product was calcined at high temperature. The discharge capacity of the Nb2O5 coating RLM is increased from 195 mAh·g−1 (the RLM without Nb2O5) to 215 mAh·g−1 at a coating volume ratio of 0.010. The average voltage decay was 4.38 mV/cycle, which was far lower than the 7.50 mV/cycle for the pure LMO. The electrochemical kinetics results indicated that the performance was superior with the buffer engineering by the Nb2O5 coating of RLM, which provided an excellent lithium-ion conduction channel, and improved diffusion kinetics, capacity fading, and voltage decay. This reveals the strong potential of the Nb2O5 coating in the field of cathode materials for lithium-ion batteries.

Energy consumption is directly related to the energy supply and production costs of gas-based direct reduction ironmaking, which is an effective choice to reduce the energy consumption of iron making. In this paper, the minimum Gibbs free energy principle was used to calculate the equilibrium composition under the conditions of reduction gas consisting of hydrogen and carbon monoxide (hydrogen concentration of 0–100%, reduction gas amount of 0–6.0 mol, reduction temperature of 790–1100 °C, and 0.5 mol Fe2O3). According to the enthalpy change, a simplified energy consumption model of a gas-based direct reduction ironmaking process was established, and the energy consumption per mole of metallic iron produced was calculated in detail. The following conclusions were drawn: at the stage when the reduction reaction occurred, the utilization rate of hydrogen or carbon monoxide remained unchanged with the increase in the amount of reduction gas or the increase in the hydrogen concentration of initial gas. The direct energy consumption increased with the increase in the hydrogen concentration at 790–980 °C and the opposite was true at 980–1100 °C. At 790–980 °C, the total energy consumption per ton of iron was greater than 0 and increased with the increase in initial hydrogen concentration from 40% to 100%, and it was less than 0 and increased with the increase in initial hydrogen concentration from 0% to 30%. It was possible to achieve zero total energy consumption with a hydrogen concentration of 30% and a 973 °C reduction.

Current antiretroviral therapy does not eliminate the integrated and transcriptionally silent HIV-1 provirus in latently infected cells. Recently, a “shock and kill” strategy has been extensively explored to eradicate the HIV-1 latent reservoirs for a permanent cure of AIDS. The therapeutic efficacy of currently used agents remains disappointing because of low efficiency, non-specificity and cellular toxicity. Here we present a novel catalytically-deficient Cas9-synergistic activation mediator (dCas9-SAM) technology to selectively, potently and persistently reactivate the HIV-1 latent reservoirs. By screening 16 MS2-mediated single guide RNAs, we identified long terminal repeat (LTR)-L and O that surround the enhancer region (-165/-145 for L and -92/-112 for O) and induce robust reactivation of HIV-1 provirus in HIV-1 latent TZM-bI epithelial, Jurkat T lymphocytic and CHME5 microglial cells. This compulsory reactivation induced cellular suicide via toxic buildup of viral proteins within HIV-1 latent Jurkat T and CHME5 microglial cells. These results suggest that this highly effective and target-specific dCas9-SAM system can serve as a novel HIV-latency-reversing therapeutic tool for the permanent elimination of HIV-1 latent reservoirs.