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Accurate wind speed forecasting (WSF) is important for effectively harnessing wind energy with clean and sustainable energy benefits. Therefore, this study develops different models established through the use of correlation analysis (CA) and decomposition techniques, Harris hawks optimization algorithm (HHO), and S2S (sequence2sequence) based spatial and temporal attention (STAt-S2S) for effective WSF. First, the CA selects variables of significant correlation with the wind speed data. In the next stage, improved complete ensemble empirical mode decomposition with additive noise (ICEEMDAN) and discrete wavelet transform with maximum overlap (MODWT) techniques are employed to decompose the components having significant correlation. Afterwards, HHO selects suitable features from the decomposed data. Finally, STA-S2S extracts spatial, temporal features and performs forecasting. The CA-ICEEMDAN–HHO–STAt-S2S and CA-ICEEMDAN-STAt-S2S models reveal better forecasting outcomes over the other standalone and hybrid foresting models. The RMSE, MAE, and sMAPE values presented by CA-ICEEMDAN-STAt-S2S are 0.639 m/s, 0.474 m/s and 15.710 m/s with NSE of 0.922. The lowest error values with the highest efficiency values of ICEEMDAN, HHO, and STAt-S2S-based hybrid models corroborate the feasibility of these models for WSF with equal applicability for similar time series applications. [Display omitted] • Multivariate meteorological data is utilized for wind speed forecasting. • MODWT and ICEEMDAN decompose data to reduce nonlinearity and nonstationary. • Harris hawks optimization selects suitable decomposed subcomponents. • Spatiotemporal attention extracts spatial and temporal features and Sequence2Sequence framework performs forecasting. • CA-ICEEMDAN–HHO–STAt-S2S and CA-ICEEMDAN-STAt-S2S models reveal better results than the other developed models. [ABSTRACT FROM AUTHOR]

Radical sustainability innovations (RSIs) often run the risk of producing unintended consequences, which can sometimes be of catastrophic nature. Literature offers some innovation management methods (IMMs) for preventing unintended consequences, but these methods remain untested at the boundary condition of failure, i.e., catastrophic failure. This gap leaves blind spots in our understanding of the application of IMMs, especially in cases of catastrophic failure of RSIs. Our objective is to apply select radical sustainability innovation management methods (RSIMMs) to a case where RSI failed catastrophically, in order to: (i) identify shortcomings of these methods at boundary condition; and (ii) understand how these methods can prevent catastrophic failures. We chose representative RSIMMs through systematic literature search. We applied these methods to the case of leaded gasoline, which qualifies the definition of RSI but produced disastrous consequences. We used process analysis technique to explicate discovery, commercialization, institutionalization, and abandonment of leaded gasoline between 1910s and 2000s. Results suggest that disputed science can be leveraged for purely economic gains, and ' show me the data ' and ' my science is right ' mentality can set managers/scientists on pathways leading to failure. We propose a preliminary model for integrating RSIMMs to promote reflective thinking by rendering multiple layers of protection against likelihood of catastrophic failure. Our work has implications for understanding the role of stakeholders in RSIs, rethinking temporal dimension of innovation performance, policy literature on sustainability, and speed of innovation theory. Managers can use these results to improve 'new product development' process by reconsidering the temporal dimension in life-cycle assessments. [ABSTRACT FROM AUTHOR]

[Display omitted] • A new hybrid model VMD-ICEEMDAN-LSTM was proposed for ultrashort term wind speed forecasting. • Multi-metrological data were used as input. • Seven machine-learning models provided a comparative analysis. • Experiments validated the outstanding performance and the statistical significance of the proposed model. Wind speed forecasting (WSF) accuracy is vital for exploiting renewable and environment-friendly wind energy. Therefore, a hybrid WSF model, namely VIL (VMD-ICEEMDAN-LSTM), was constructed in this study. VIL is a three-layered structure utilizing variational mode decomposition (VMD), improved complete ensemble empirical mode decomposition with additive noise (ICEEMDAN), and a long short-term memory (LSTM) neural network optimized using a differential evolutionary algorithm (DEA). The VIL was trained on multivariate meteorological data collected from Sujawal station, Pakistan. The partial autocorrelation function (PACF) analysis and an ensemble of VMD and ICEEMDAN techniques were employed to determine the appropriate input variables. The forecasted results obtained through LSTM were combined to create an ensemble forecasting model for the WSF. Seven other models including radial basis function (RFB), support vector regression (SVR), random forest regression (RFR), extreme learning machine (ELM), extreme gradient boosting (XGB), gated recurrent unit (GRU), and LSTM were developed as benchmarks for comparison purposes. As per the results, the VIL model revealed the lowest statistical errors, the best goodness-of-fit, the consistency of variation trend following the pattern of the observed wind speed, and the nearly matched probability distribution with the observed wind speed data. VIL reduced MAPE compared to RFB, SVR, RFR, ELM, XGB, GRU, and LSTM models by 7.297%, 5.704%, 4.993%, 5.233%, 6.514%, 5.013%, 5.204%, respectively, during testing. Similarly, VIL revealed highest R2 = 0.984 value during testing compared to the counterpart models including RFB (R2 = 0.911), SVR (R2 = 0.927), RFR (R2 = 0.930), ELM (R2 = 0.932), XGB (R2 = 0.931), GRU (R2 = 0.934), and LSTM (R2 = 0.935). The results validated the efficacy and statistical significance of the VIL model over the seven other constructed standalone models. The superior results of the VIL model compared to the counter developed models highlight the viability of integration of VMD and ICEEMDAN decomposition approaches and LSTM deep learning network for multivariate WSF. Therefore, the VIL model is performant for WSF and has good prospects for the energy-environment-transportation-health nexus. [ABSTRACT FROM AUTHOR]

Pakistan has not fully harnessed the capacity of its vast hydropower potential for electricity generation. It depends on imported and local fossil fuels to fulfill its energy demands, which consume a significant portion of the country's economy. Hydropower can provide an economical, renewable, clean, and secure source of energy for the country. The potential benefits and prospects of hydropower for Pakistan require a comprehensive review of the hydropower sector to investigate the true resource potential and its development, considering the associated pros and cons. Therefore, the review work examines hydropower utilization in Pakistan using SWOT (strengths, weaknesses, opportunities, threats) analysis. The strengths include an enormous resource potential, sustainable energy source, and a well-established technology; the weaknesses include substantial capital investment, time-taking project completion, degradation of the reservoir over time, and the effect of seasonal variations. The opportunities include increasing demand-supply gap, 93% unexploited potential, water storage dams, energy security, rising concerns for climate change; the threats include economic and political instability, dependence on foreign investment and fossil fuels, transboundary location of water resources, irregular and vague energy policies. The SWOT analysis shows that despite having severe issues, hydropower is still a priority source of secure, cheap, and clean energy for the country with abundant resource potential and a properly managed technology. • The paper analyzes the current energy crisis of Pakistan, the scenario of different power generation resources, the development history of hydropower, existing and installed potential, and policies related to hydropower. • The review conducts a comprehensive SWOT analysis to determine strengths, weaknesses, opportunities, and threats regarding hydropower. • Strengths and opportunities include tremendous untapped potential, clean and cheap source, decrease in the dependence on fossil fuels. • Weaknesses and threats include high capital cost, lengthy projects completion, the effect of seasonal variations, vague policies, transboundary water resources, economic and political instability. • The paper emphasizes the full exploitation of hydropower resources to lessen the energy crisis. [ABSTRACT FROM AUTHOR]

The debate on the future of fossil fuels regained momentum in the late 1990s and has subsisted due to their negative environmental impact and the need to develop sustainable energy sources. Owing to the increasing emissions footprint, there is growing pressure to adapt to alternative clean energies in a bid to mitigate the deteriorating climatic conditions. Research on alternative energy sources is top on the agenda. As such, biofuels are now being tested as the next aviation fuel that has since attracted substantial attention. Various researchers have overtime sought to redefine the trajectory of research on biofuels starting from the feedstock varieties, evolving from sugar-rich crops to lignocellulosic or non-food-plant, to microalgae and to synthetic biological and metabolic engineering of fungal hosts. Substantial effort is directed at enhancing the biofuel storage capacity as well as deploying technologies that seek to enhance cost efficiency, towards ultimately making biofuels cost effective and commercialize-able. Despite the great potential of biofuels, the Sub- Saharan Africa contribution is still low, notwithstanding the cross cutting deterioration in climatic conditions. Therefore, following a systematic literature review approach, this study sought to ascertain the status of research on the commercialization of biofuels, conceptualization and the applied antecedents, as well as recommend policy actions and areas for further research. The findings suggest that commercialization is presently conceptualized on a few dimensions; capacity development towards large scale production and cost efficiency. Also, significant effort has been directed at migrating production from crop based feedstock to non-crop feedstock, as well as improving the storage life span of biofuels. [ABSTRACT FROM AUTHOR]

The tumorigenic activity of upregulated Mcl-1 is manifested by binding the BH3 α-helical death domains of opposing Bcl-2 family members, neutralizing them and preventing apoptosis. Accordingly, the development of Mcl-1 inhibitors largely focuses on synthetic BH3 mimicry. The condensation of α-pyridinium methyl ketone salts and α,β-unsaturated carbonyl compounds in the presence of a source of ammonia, or the Kröhnke pyridine synthesis, is a simple approach to afford highly functionalized pyridines. We adapted this chemistry to rapidly generate low-micromolar inhibitors of Mcl-1 wherein the 2,4,6-substituents were predicted to mimic the i , i  + 2 and i  + 7 side chains of the BH3 α-helix. [ABSTRACT FROM AUTHOR]

Soil erosion, which will induce the redistribution of soil and associated soil organic carbon (SOC) on the Earth's surface, is of critically importance for biogeochemical cycling of essential elements and terrestrial carbon sequestration. Despite the importance of soil erosion, surprisingly few studies have evaluated the sources of eroded carbon (C). This study used natural abundance levels of the stable isotope signature ( 13 C) and radioactive isotopes ( 137 Cs and 210 Pb ex ), along with elements ratio (C/N) based on a two end member mixing model to qualitatively and quantitatively identify the sources of sedimentary OC retained by check dam in the Qiaozigou small watershed in the Loess Plateau, China. Sediment profiles (0–200 cm) captured at natural depositional area of the basin was compared to possible source materials, which included: superficial Loess mineral soils (0–20 cm) from three land use types [i.e., grassland ( Medicago sativa ), forestland ( Robinia pseudoacacia. ), shrubland ( Prunus sibirica ), and gully land ( Loess parent material. )]. The results demonstrated that SOC in sediments showed significantly negative correlation with pH ( P < 0.01), and positive correlation with soil water content (SWC) ( P < 0.05). The sedimentary OC was not derived from grasslands or gullies. Forestland and shrubland were two main sources of eroded organic carbon within the surface sediment (0–60 cm deep), except for that in the 20–40 cm soil layer. Radionuclides analyses also implied that the surface sediments retained by check-dams mainly originated from soils of forestland and shrubland. Results of the two end-member mixing model demonstrated that more than 50% SOC (mean probability estimate (MPE) 50.13% via 13 C and 60.53% via C/N) in surface sediment (0–20 cm deep) derived from forestland, whereas subsurface sedimentary SOC (20–200 cm) mainly resulted from shrubland (MPE > 50%). Although uncertainties on the sources of SOC in deep soils exist, the soil organic δ 13 C and C/N is still an effective indicator for sources of sedimentary organic carbon in the deposition zone in the short term (<10 years). [ABSTRACT FROM AUTHOR]

Background: Cancer cells are characterised by uncontrolled replication involving loss of control of cyclin-dependent kinases (CDKs) and cyclins, and by abolished differentiation. In this study we introduce KGI, a nanoparticle with a Quillaja saponin as an active molecule. Methods: By the use of RNA array analysis and confirmation at the protein level, we studied the effects of KGI on myeloid leukaemia cells. Findings: KGI affects myeloid leukaemia cells (particularly the U937 monoblast cancer cell) by the following mechanisms: ceasing cell replication via proteasome degradation; down-regulation of key molecules at check points between G1/S and G2/M phases; reduction of thymidine kinase activity; followed by exit to differentiation and production of IL-8, eventually leading to apoptosis. Leukaemia cell lines (U937 and HL-60 cells) were exposed to KGI for 8h, after which the drug was removed. The cancer cells did not revert to replication over the following 10days. Interpretation: Our findings suggest that the nanoparticle KGI inhibits proliferation and promotes differentiation in leukaemic cells by interfering with the cell cycle process. [ABSTRACT FROM AUTHOR]