AI-driven optimization of ethanol-powered internal combustion engines in alignment with multiple SDGs: A sustainable energy transition

With the escalating requirement for global sustainable energy solutions and the complexities linked with the complete transition to new technologies, internal combustion engines (ICEs) powered with biofuels like ethanol are gaining significance over time. However, problems linked to the performance and emissions of such ICEs necessitate accurate prediction and optimization. The study employed the integration of artificial neural networks (ANN) and multi-level historical design of response surface methodology (RSM) to address these challenges in alignment with the Sustainable Development Goals (SDGs). A single-cylinder spark ignition (SI) engine powered with ethanol-gasoline blends at different loads and speeds was used to gather data. Among six initially trained ANN models, the most efficient model with a regression coefficient (R2) of 0.9952 (training), 0.98579 (validation), 0.98847 (testing), and 0.99307 (overall) was employed to predict outputs such as brake power, brake specific fuel consumption (BSFC), brake thermal energy (BTE), concentration of carbon dioxide (CO2), carbon monoxide (CO), hydrocarbons (HC), and oxides of nitrogen NOx. Predicted outputs were optimized by incorporating RSM. On implementing optimized conditions, it was observed that BP and BTE increased by 19.9%, and 29.8%, respectively. Additionally, CO, and HC emissions experienced substantial reductions of 28.1%, and 40.6%, respectively. This research can help engine producers and researchers make refined decisions and achieve improved performance and emissions. The study directly supports SDG 7, SDG 9, SDG 12, SDG 13, and SGD 17, which call for achieving affordable, clean energy, sustainable industrialization, responsible consumption, and production, taking action on climate change, and partnership to advance the SDGs as a whole respectively

Artificial Intelligence for Energy Processes and Systems: Applications and Perspectives

In recent years, artificial intelligence has become increasingly popular and is more often used by scientists and entrepreneurs. The rapid development of electronics and computer science is conducive to developing this field of science. Man needs intelligent machines to create and discover new relationships in the world, so AI is beginning to reach various areas of science, such as medicine, economics, management, and the power industry. Artificial intelligence is one of the most exciting directions in the development of computer science, which absorbs a considerable amount of human enthusiasm and the latest achievements in computer technology. This article was dedicated to the practical use of artificial neural networks. The article discusses the development of neural networks in the years 1940–2022, presenting the most important publications from these years and discussing the latest achievements in the use of artificial intelligence. One of the chapters focuses on the use of artificial intelligence in energy processes and systems. The article also discusses the possible directions for the future development of neural networks

Acetone-Gasoline Blend as an Alternative Fuel in SI Engines: A Novel Comparison of Performance, Emission, and Lube Oil Degradation

The disproportionate use of petroleum products and stringent exhaust emissions has emphasized the need for alternative green fuels. Although several studies have been conducted to ascertain the performance of acetone-gasoline blends in spark-ignition (SI) engines, limited work has been done to determine the influence of fuel on lubricant oil deterioration. The current study fills the gap through lubricant oil testing by running the engine for 120 h on pure gasoline (G) and gasoline with 10% by volume acetone (A10). Compared to gasoline, A10 produced better results in 11.74 and 12.05% higher brake power (BP) and brake thermal efficiency (BTE), respectively, at a 6.72% lower brake-specific fuel consumption (BSFC). The blended fuel A10 produced 56.54, 33.67, and 50% lower CO, CO2, and HC emissions. However, gasoline remained competitive due to lower oil deterioration than A10. The flash-point and kinematic viscosity, compared to fresh oil, decreased by 19.63 and 27.43% for G and 15.73 and 20.57% for A10, respectively. Similarly, G and A10 showed a decrease in total base number (TBN) by 17.98 and 31.46%, respectively. However, A10 is more detrimental to lubricating oil due to a 12, 5, 15, and 30% increase in metallic particles like aluminum, chromium, copper, and iron, respectively, compared to fresh oil. Performance additives like calcium and phosphorous in lubricant oil for A10 decreased by 10.04 and 4.04% in comparison to gasoline, respectively. The concentration of zinc was found to be 18.78% higher in A10 when compared with gasoline. A higher proportion of water molecules and metal particles were found in lubricant oil for A10

Electrochemical Insight into the Use of Microbial Fuel Cells for Bioelectricity Generation and Wastewater Treatment

Microbial fuel cell (MFC) technology is anticipated to be a practical alternative to the activated sludge technique for treating domestic and industrial effluents. The relevant literature mainly focuses on developing the systems and materials for maximum power output, whereas understanding the fundamental electrochemical characteristics is inadequate. This experimental study uses a double-chamber MFC having graphite electrodes and an anion-exchange membrane to investigate the electrochemical process limitations and the potential of bioelectricity generation and dairy effluent treatment. The results revealed an 81% reduction in the chemical oxygen demand (COD) in 10 days of cell operation, with an initial COD loading of 4520 mg/L. The third day recorded the highest open circuit voltage of 396 mV, and the maximum power density of 36.39 mW/m2 was achieved at a current density of 0.30 A/m2. The electrochemical impedance spectroscopy analysis disclosed that the activation polarization of the aerated cathode was the primary factor causing the cell’s resistance, followed by the ohmic and anodic activation overpotentials

Hybrid off-river augmentation system as an alternative raw water resource: the hydrogeochemistry of abandoned mining ponds

The use of water from abandoned mining ponds under a hybrid off-river augmentation system (HORAS) has been initiated as an alternative water resource for raw water. However, it raises the questions over the safety of the use of such waters. In this study, the hydrogeochemical analysis of the waters is presented to assess the degree to which the water has been contaminated. Comparisons were made between sampling sites, i.e. abandoned mining ponds, active sand mining ponds and the receiving streams within Bestari Jaya, Selangor River basin. The aqueous geochemistry analysis showed different hydrochemical signatures of major elements between sites, indicating different sources of minerals in the water. Discharges from the sand mining ponds were found to contain elevated availability of dissolved concentrations of iron, manganese, lead, copper and zinc, among others. However, the quality of the water (from the main river) that is supplied for potable water consumption is at a satisfactory level despite being partly sourced from the abandoned mining ponds. In fact, all the metal concentrations detected were well below the Malaysia Ministry of Health guideline limits for untreated raw water. In addition, the results of the geochemical index analysis (i.e. geoaccumulation index, enrichment factor and modified contamination factor) showed that the rivers and abandoned mining ponds were generally unpolluted with respect to the metals found in sediments

Cell Pattern in Adult Human Corneal Endothelium

A review of the current data on the cell density of normal adult human endothelial cells was carried out in order to establish some common parameters appearing in the different considered populations. From the analysis of cell growth patterns, it is inferred that the cell aging rate is similar for each of the different considered populations. Also, the morphology, the cell distribution and the tendency to hexagonallity are studied. The results are consistent with the hypothesis that this phenomenon is analogous with cell behavior in other structures such as dry foams and grains in polycrystalline materials. Therefore, its driving force may be controlled by the surface tension and the mobility of the boundaries

Comparison of Ion Balance and Nitrogen Metabolism in Old and Young Leaves of Alkali-Stressed Rice Plants

BACKGROUND: Alkali stress is an important agricultural contaminant and has complex effects on plant metabolism. The aim of this study was to investigate whether the alkali stress has different effects on the growth, ion balance, and nitrogen metabolism in old and young leaves of rice plants, and to compare functions of both organs in alkali tolerance. METHODOLOGY/PRINCIPAL FINDINGS: The results showed that alkali stress only produced a small effect on the growth of young leaves, whereas strongly damaged old leaves. Rice protected young leaves from ion harm via the large accumulation of Na(+) and Cl(-) in old leaves. The up-regulation of OsHKT1;1, OsAKT1, OsHAK1, OsHAK7, OsHAK10 and OsHAK16 may contribute to the larger accumulation of Na(+) in old leaves under alkali stress. Alkali stress mightily reduced the NO(3)(-) contents in both organs. As old leaf cells have larger vacuole, under alkali stress these scarce NO(3)(-) was principally stored in old leaves. Accordingly, the expression of OsNRT1;1 and OsNRT1;2 in old leaves was up-regulated by alkali stress, revealing that the two genes might contribute to the accumulation of NO(3)(-) in old leaves. NO(3)(-) deficiency in young leaves under alkali stress might induce the reduction in OsNR1 expression and the subsequent lacking of NH(4)(+), which might be main reason for the larger down-regulation of OsFd-GOGAT and OsGS2 in young leaves. CONCLUSIONS/SIGNIFICANCE: Our results strongly indicated that, during adaptation of rice to alkali stress, young and old leaves have distinct mechanisms of ion balance and nitrogen metabolism regulation. We propose that the comparative studies of young and old tissues may be important for abiotic stress tolerance research

TGF-ß Sma/Mab Signaling Mutations Uncouple Reproductive Aging from Somatic Aging

Female reproductive cessation is one of the earliest age-related declines humans experience, occurring in mid-adulthood. Similarly, Caenorhabditis elegans' reproductive span is short relative to its total life span, with reproduction ceasing about a third into its 15–20 day adulthood. All of the known mutations and treatments that extend C. elegans' reproductive period also regulate longevity, suggesting that reproductive span is normally linked to life span. C. elegans has two canonical TGF-ß signaling pathways. We recently found that the TGF-ß Dauer pathway regulates longevity through the Insulin/IGF-1 Signaling (IIS) pathway; here we show that this pathway has a moderate effect on reproductive span. By contrast, TGF-ß Sma/Mab signaling mutants exhibit a substantially extended reproductive period, more than doubling reproductive span in some cases. Sma/Mab mutations extend reproductive span disproportionately to life span and act independently of known regulators of somatic aging, such as Insulin/IGF-1 Signaling and Dietary Restriction. This is the first discovery of a pathway that regulates reproductive span independently of longevity and the first identification of the TGF-ß Sma/Mab pathway as a regulator of reproductive aging. Our results suggest that longevity and reproductive span regulation can be uncoupled, although they appear to normally be linked through regulatory pathways

Global mortality from dementia : Application of a new method and results from the Global Burden of Disease Study 2019

Introduction Dementia is currently one of the leading causes of mortality globally, and mortality due to dementia will likely increase in the future along with corresponding increases in population growth and population aging. However, large inconsistencies in coding practices in vital registration systems over time and between countries complicate the estimation of global dementia mortality. Methods We meta-analyzed the excess risk of death in those with dementia and multiplied these estimates by the proportion of dementia deaths occurring in those with severe, end-stage disease to calculate the total number of deaths that could be attributed to dementia. Results We estimated that there were 1.62 million (95% uncertainty interval [UI]: 0.41-4.21) deaths globally due to dementia in 2019. More dementia deaths occurred in women (1.06 million [0.27-2.71]) than men (0.56 million [0.14-1.51]), largely but not entirely due to the higher life expectancy in women (age-standardized female-to-male ratio 1.19 [1.10-1.26]). Due to population aging, there was a large increase in all-age mortality rates from dementia between 1990 and 2019 (100.1% [89.1-117.5]). In 2019, deaths due to dementia ranked seventh globally in all ages and fourth among individuals 70 and older compared to deaths from other diseases estimated in the Global Burden of Disease (GBD) study. Discussion Mortality due to dementia represents a substantial global burden, and is expected to continue to grow into the future as an older, aging population expands globally.Peer reviewe