Many problems in the fields of finance and actuarial science can be transformed into the problem of solving backward stochastic differential equations (BSDE) and partial differential equations (PDE) with jumps, which are often difficult to solve in high-dimensional cases. To solve this problem, this paper applies the deep learning algorithm to solve a class of high-dimensional nonlinear partial differential equations with jump terms and their corresponding backward stochastic differential equations (BSDE) with jump terms. Using the nonlinear Feynman-Kac formula, the problem of solving this kind of PDE is transformed into the problem of solving the corresponding backward stochastic differential equations with jump terms, and the numerical solution problem is turned into a stochastic control problem. At the same time, the gradient and jump process of the unknown solution are separately regarded as the strategy function and they are approximated respectively by using two multilayer neural networks as function approximators. Thus, deep learning-based method is used to overcome the “curse of dimensionality” caused by high-dimensional PDE with jump, and the numerical solution is obtained. In addition, this paper proposes a new optimization algorithm based on the existing neural network random optimization algorithm, and compares the results with the traditional optimization algorithm, and achieves good results. Finally, the proposed method is applied to three practical high-dimensional problems: Hamilton-Jacobi-Bellman equation, bond pricing under the jump Vasicek model and high-dimensional option pricing model with default risk. The proposed numerical method has obtained satisfactory accuracy and efficiency. The method has important application value and practical significance in investment decision-making, option pricing, insurance and other fields.

Traveling salesman problems (TSPs) are well-known combinatorial optimization problems, and most existing algorithms are challenging for solving TSPs when its scale is large. To improve the efficiency of solving large-scale TSPs, this work presents a novel adaptive layered clustering framework with improved genetic algorithm (ALC\_IGA). The primary idea behind ALC\_IGA is to break down a large-scale problem into a series of small-scale problems. First, the $k$-means and improved genetic algorithm are used to segment the large-scale TSPs layer by layer and generate the initial solution. Then, the developed two phases simplified $2$-opt algorithm is applied to further improve the quality of the initial solution. The analysis reveals that the computational complexity of the ALC\_IGA is between $O(n\log n)$ and $O(n^2)$. The results of numerical experiments on various TSP instances indicate that, in most situations, the ALC\_IGA surpasses the state-of-the-art algorithms in convergence speed, stability, and solution quality. Specifically, the ALC\_IGA can solve instances with $2 \times 10^5$ nodes within 0.15h, $1.4 \times 10^6$ nodes within 1h, and $2 \times 10^6$ nodes in three dimensions within 1.5h.

Excessive high fat diet (HFD) consumption can induce food addiction which is believed to involve the communication between the hypothalamus and mesolimbic dopaminergic neurons, originating in the ventral tegmental area (VTA) and projecting to the nucleus accumbens (NAc). These brain regions are densely populated with opioid receptors, raising the possibility that these receptors, and particularly mu opioid receptors (MORs), are involved in mediating reward induced by palatable foods. This study sought to investigate the involvement of MORs in high fat diet (HFD)-induced reward and if there is any difference between male and female subjects in this response. We also assessed if exposure to HFD would potentiate the rewarding action of oxycodone, a relatively selective MOR agonist. The place conditioning paradigm was used to determine if conditioning for a short time (STC, 2 h) or long time (LTC, 16 h) with HFD induces reward or alters the rewarding action of oxycodone. Male and female C57BL/6J mice as well as MOR knockout and wildtype mice of both sexes were tested for basal place preference on day 1 and then conditioned with HFD in one chamber and regular chow diet (RCD) in another chamber and tested for place preference again after three sets of STC and again after a set of LTC. Each set consisted of two conditioning with RCD and two conditioning with HFD. Controls were conditioned with RCD in both conditioning chambers. Following the last place preference test, mice were treated with oxycodone and conditioned in the HFD-paired chamber and saline in the RCD-paired chamber for one hour once a day to explore the possibility if the HFD could alter oxycodone reward. The result showed that HFD induced conditioned place preference (CPP) in both male and female subjects following the LTC. This response was further potentiated after oxycodone conditioning. The latter response was mediated via MORs, as it was blunted in MOR knockout mice. However, HFD-induced CPP was observed following LTC in only female MOR knockout mice, suggesting for sexual dimorphism in this response.

Leucine-rich α-2 glycoprotein1 (LRG1) is a member of the leucine-rich repeat (LRR) family that was implicated in multiple diseases including cancer, aging and heart failure as well as diabetes and obesity. LRG1 plays a key role in diet-induced hepatosteatosis and insulin resistance by mediating the crosstalk between adipocytes and hepatocytes. LRG1 also promotes hepatosteatosis by upregulating de novo lipogenesis in the liver and suppressing fatty acid β oxidation. In this study, we investigated the association of LRG1 with obesity markers including leptin and other adipokines in adolescents (11-14 years; n=425). BMI-for-age classification based on WHO growth charts was used to define obesity. Plasma LRG1 was measured by ELISA while other markers were measured by multiplexing assay. Median (IQR) of LRG1 levels was higher in obese [30 (25, 38) µg/mL] and overweight [30 (24, 39) µg/mL] adolescents, compared to normal-weight participants [27 (22, 35) µg/mL]. The highest tertile of LRG1 had an OR [95%CI] of 2.55 [1.44, 4.53] for obesity. LRG1 was positively correlated to plasma levels of HsCRP (R=0.2), Leptin (R=0.2) and Chemerin (R=0.24) with p<0.001. Additionally, it was positively associated with plasma level of IL6 (R=0.17) and IL10 (R=0.14) but not TNF-α. In conclusion, LRG1 levels are increased in obese kids and associate with increased levels of adipogenic markers. These results suggest the usefulness of LRG1 as an early biomarker for obesity and its related pathologies in adolescents.

Among solar thermal collectors, the evacuated flat panel is emerging as a reference technology for operation at higher temperatures of up to 200 °C with an increased annual energy production owing to both direct and diffuse light capture. Accurate measurements of the optical properties of the selective absorbers used in such devices are key for a reliable estimation of the overall performance. These optical properties must be measured under high vacuum at high temperatures, conditions under which the panels are meant to operate. In this study, we accurately measured these properties using a calorimetric technique. The measurement procedure is based on a power balance equation for a flat sample suspended in a high-vacuum chamber with minimal thermal losses and is well adapted for this class of devices. Calorimetric measurements obtained under Sun and LED light revealed excellent reproducibility and good agreement with those obtained using traditional optical analysis at low temperatures in air. When extended up to the absorber stagnation temperature, which often exceeds 300 °C, the calorimetric measurements started to deviate from the optical measurements, indicating the importance of measuring under the operating conditions.

Different external stimuli are perceived by multiple sensor histidine kinases and transmitted by phosphorylation via the phosphotransfer protein Ypd1p in the multistep phosphorelay system of the high osmolarity glycerol signaling pathway of filamentous fungi. How the signal propagation takes place is still not known in detail, since multiple sensor histidine kinase genes in most filamentous fungi are coded in the genome, whereas only one gene for Ypd1 exists. That raises the hypothesis that various Ypd1 isoforms are produced from a single gene sequence, perhaps by alternative splicing, facilitating a higher variability in signal transduction. We found that the mRNA of MoYPD1 in the rice blast fungus Magnaporthe oryzae is subjected to an increased structural variation and amplified putative isoforms on a cDNA level. We then generated mutant strains overexpressing these isoforms, purified the products and present here one previously unknown MoYpd1 isoform on a proteome level. Alternative splicing was found to be a valid molecular mechanism to increase the signal diversity in eukaryotic multistep phosphorelay systems.

Development of effective treatments for high-grade glioma (HGG) is hampered by 1) the blood-brain barrier (BBB), 2) an infiltrative growth pattern, 3) rapid development of therapeutic resistance, and, in many cases, 4) dose-limiting toxicity due to systemic exposure. Convec-tion-enhanced delivery (CED) has the potential to significantly limit systemic toxicity and in-crease therapeutic index by directly delivering homogenous drug concentrations to the site of disease. In this review, we present clinical experiences and preclinical developments of CED in the setting of high-grade gliomas.

As new treatment modalities are being explored in neuro-oncology, viruses are emerging as a promising class of therapeutics. Virotherapy consists of introduction of either wild-type or engineered viruses to the site of disease, where they exert anti-tumor effect. These viruses can either be non-lytic, in which case they are used to deliver gene therapy, or lytic, which induce tumor cell lysis and subsequent host immunologic response. Replication-competent viruses can then go on to further infect and lyse neighboring glioma cells. This treatment paradigm is being explored extensively in both preclinical and clinical studies for a variety of indications. Virus-based therapies are advantageous due to the natural susceptibility of glioma cells to viral infection, which improves therapeutic selectivity. Furthermore, lytic viruses expose glioma antigens to the host immune system and subsequently stimulate an immune response that specifically targets tumor cells. This review surveys the current landscape of oncolytic virotherapy clinical trials in high-grade glioma, summarizes preclinical experiences, identifies challenges associated with this modality across multiple trials, and highlights potential to integrate this therapeutic strategy into promising combinatory approaches.

Administration of crude polysaccharides extract from natural product is a promising gut microbiota-targeted approach to preventing obesity and associated metabolic disorders. Dietary restrictions can change the type and number of gut bacteria, which is an important factor in delaying the onset and burden of diseases. This study aimed to investigate the effects of high-yield crude polysaccharides from Cordyceps militaris (CMP) on high-fat diet (HFD) mice model and the gut microbiota community assembly, and to identify whether selenium (Se) addition would improve CMP action mode during cultivation. We found that the CMP treatment ameliorated adipose and liver pathologic morphology and fat accumulation in obese mice, while, SeCMP intervention was not superior than CMP in body mass gain, but notably decreasing serum triglyceride level increased by HFD. The upregulated expression of gene Cyp7a1 in liver and protein UCP1 in brown adipose tissue (BAT) preliminary indicated that the effect might relate to bile acids (BAs) metabolism pathway and thermogenesis. In addition, CMP showed a drastic decrease in the gut microbes which positively correlated with dyslipidemia parameters. Our result reveals the potential of CMP to be used as functional food in the prevention of diet-induced adipose and liver steatosis, so does SeCMP has outstanding capacity of improving dyslipidemia.

There is a growing use of High Voltage Direct Current (HVDC) globally due to the many advantages of Direct Current (DC) transmission systems over Alternating Current (AC) transmission, including enabling transmission over long distances, higher transmission capacity and efficiency. Moreover, HVDC systems can be a great enabler in the transition to a low carbon electrical power system which is an important objective in today’s society. The objectives of the paper are to give a comprehensive overview of HVDC technology, its development, and present status, and to discuss its salient features, limitations and applications.

Analyses of the Hubble diagrams are presented for SN1a supernovae and gamma ray bursts in the redshift ranges z = 0.01–1.3 and 0.034–8.1, respectively. Data are presented on the typical z/μ scale and also on the less common yet increasingly sensitive photon flight time t/(z+1) scale. The primary conclusion is that on the basis of the presently accessible data in the redshift range z = 0.01–8.1, the slope of the Hubble diagram is, or is extremely close to, exponential.

The critical hypoxia in COVID-19 patients during this pandemic, has taken away many lives all around the globe. The mechanism has been poorly understood and initially, word got around that it was a SARS (Severe Acute Respiratory Syndrome) pneumonia. The atypical images in lung computerized axial tomography (CAT) scans were alarming. This immediately alerted everyone including poor countries to purchase lacking sophisticated ventilator equipment. However, in some countries, even 88% of the patients on ventilators lost their lives. New observations and pathological findings are gradually clarifying the disease. What seems evident is that it is not only one disease but several, with different responses in different countries and different altitudes. The critical hypoxia and «gasping» present in some patients are not totally understood. It was mentioned that it could be like a High-Altitude Pulmonary Edema (HAPE). Hereby, as high-altitude medicine and hypoxia physiology specialists, we compare the pathophysiology with that of high-altitude exposure in order to understand the mechanisms involved. Some differences in lung radiological images along with transmission and viral attack mechanisms are discussed. The oxygen transport triad used at high-altitude can be applied on this pathology in order to propose even the use of erythropoietin (EPO) early in the treatment. The immune system is the most important long-term survival tool, so we suggest a short-term strategy: the use of special Earth open-circuit astronaut-resembling suits with effective outside air filtering re-breathing mechanisms in order to return to work and daily activities, without contamination risk. Thereby, the curve can be flattened without quarantine and the economy could recover.

COVID-19 (2019-nCoV) is a pandemic disease with an estimated mortality rate of 3.4% (estimated by the WHO as of March 3, 2020). Until now there is no antiviral drug and vaccine for COVID-19. The current overwhelming situation by COVID-19 patients in hospitals is likely to increase in the next few months. About 15 percent of patients with serious disease in COVID-19 require immediate health services. Rather than waiting for new anti-viral drugs or vaccines that take a few months to years to develop and test, several researchers and public health agencies are attempting to repurpose medicines that are already approved for another similar disease and have proved to be fairly effective. This study aims to identify FDA approved drugs that can be used for drug repurposing and identify biomarkers among high- risk and asymptomatic groups. In this study gene-disease association related to COVID-19 reported mild, severe symptoms and clinical outcomes were determined. The high-risk group was studied related to SARS-CoV-2 viral entry and life cycle by using Disgenet and compared with curated COVID-19 gene data sets from the CTD database. The overlapped gene sets were enriched and the selected genes were constructed for protein-protein interaction networks. Through interactome, key genes were identified for COVID-19 and also for high risk and asymptomatic groups. The key hub genes involved in COVID-19 were VEGFA, TNF, IL-6, CXCL8, IL10, CCL2, IL1B, TLR4, ICAM1, MMP9. The identified key genes were used for drug-gene interaction for drug repurposing. The chloroquine, lenalidomide, pentoxifylline, thalidome, sorafenib, pacitaxel, rapamycin, cortisol, statins were proposed to be probable drug repurposing candidates for the treatment of COVID-19. However, these predicted drug candidates need to be validated through randomized clinical trials. Also, a key gene involved in high risk and the asymptomatic group were identified, which can be used as probable biomarkers for early identification.

Lamellar eutectic structure of Al_0.7CoCrFeNi high-entropy alloy (HEA) is emerging as a promising candidate for structural applications because of its high strength-ductility combination. The alloy consists of a fine-scale lamellar fcc+B2 microstructure with high flow stresses >1500 MPa under quasi-static conditions. The response to shear loading was not investigated so far. This is the first report on the shear deformation of an eutectic structured HEA and effect of precipitation on shear deformation. The dynamic shear response (DSR) of the eutectic HEA was examined in two microstructural conditions, with and without the presence of L1₂ precipitates. A split-Hopkinson pressure bar (SHPB) was used to compress the hat-shaped specimens to study the local DSR of the alloy. The adiabatic shear bands (ASBs) in two different microstructural conditions were characterized after deformation at dynamic strain rates. The adiabatic shear localization occurs at low strains for the high strength material, and the eutectic microstructure does not delay cracking. The width of ASBs and the extent of plastic deformation around them has been correlated with the rate of straining. Dynamic recrystallization within ASBs and profuse twinning around it was observed. Local mechanical response of individual lamellae before and after shear deformation was examined using nano-indentation.

Synthetic chalcomenite-type cupric selenite CuSeO₃∙2H₂O has been studied at room temperature under compression up to pressures of 8 GPa by means of single-crystal X-ray diffraction, Raman spectroscopy, and density-functional theory. According to X-ray diffraction, the orthorhombic phase undergoes an isostructural phase transition at 4.0(5) GPa with the thermodynamic character being first-order. This conclusion is supported by Raman spectroscopy studies which have detected the phase transition at 4.5(2) GPa and by the first-principles computing simulations. The structure solution at different pressures has provided information on the change with pressure of unit-cell parameters as well as on the bond and polyhedral compressibility. A Birch-Murnaghan equation of state has been fitted to the unit-cell volume data. We found that chalcomenite is highly compressible with a bulk modulus of 42 – 49 GPa. The possible mechanism driving changes in the crystal structure is discussed, being the behavior of CuSeO₃∙2H₂O mainly dominated by the large compressibility of the coordination polyhedron of Cu. On top of that, an assignation of Raman modes is proposed based upon density-functional theory and the pressure dependence of Raman modes discussed. Finally, the pressure dependence of phonon frequencies is also reported.

In this paper we describe a nonparametric maximum likelihood (NPML) method for estimating multivariate mixing distributions. Given $N$ independent observations, convexity theory shows that the NPML estimator is discrete with at most $N$ support points. The original infinite NPML problem then becomes the finite dimensional problem of finding the location and probability of the support points. The probability of the support points is found by a Primal-Dual Interior-Point method; the location of the support points is found by an Adaptive Grid method. Our method is able to handle high-dimensional and complex multivariate mixture models.An important application is discussed for the problem of population pharmacokinetics and a non-trivial example is treated.Our algorithm has been successfully applied in hundreds of published pharmacometric studies. In addition to population pharmacokinetics, this research also applies to empirical Bayes estimation and many other areas of applied mathematics.

Virus diseases of strawberry present several complex problems. More than 25 viruses have been described in the genus Fragaria thus far. Here, we describe a novel rhabdovirus, tentatively named strawberry virus 1 (StrV-1), that infects F. ananassa and F. vesca plants. Genomic sequences of three distinct StrV-1 genotypes co-infecting a single F. ananassa host were obtained using combined Illumina and Ion Proton high-throughput sequencing. StrV-1 was transmitted to herbaceous plants via Aphis fabae and A. ruborum, further mechanically transmitted to Nicotiana occidentalis 37B and sub-transferred to N. benthamiana, N. benthamiana DCL2/4i, N. occidentalis 37B and Physalis floridana plants. Irregular chlorotic sectors on leaf blades and the multiplication of calyx leaves seem to be the diagnostic symptoms for StrV-1 on indexed F. vesca clones. StrV-1 was detected in asymptomatic grafted plants and in 49 out of 159 field strawberry samples via RT-PCR followed by Sanger sequencing. The bacilliform shape of the virions, which have a cytoplasm-limited distribution, their size, and phylogenetic relationships support the assignment of StrV-1 to a distinct species of the genus Cytorhabdovirus. Acyrthosiphon malvae, A. fabae and A. ruborum were shown to transmit StrV-1 under experimental conditions.

JBIR-99 is a secondary metabolite of marine fungi that has been shown to possess strong antibiotic activity. An efficient approach using a combination of size exclusion chromatography with a Sephadex LH-20 and high-speed counter-current chromatography (HSCCC) has been successfully developed for the isolation and purification of a polyketide from the solid-state fermentation of Meyerozyma guilliermondii. The active compound was isolated with purity >95% by HSCCC using an optimized solvent system composed of petroleum ether–ethyl acetate– 95% ethanol–water (5:3:5:3, v/v/v/v) after size exclusion chromatography. This compound was successfully purified in the quantity of 68 mg from 120 mg of the crude extract. The structure of JBIR-99 was elucidated and assigned by 1D, 2D NMR spectroscopic, and positive HRESITOFMS. Moreover, the relative configuration of compound JBIR-99, displaying a quite complex multi-ring structure, is determined by X-ray crystallography for the first time. The purification method developed for JBIR-99 will facilitate the further investigation and development of this antibiotic agent as a lead compound. Furthermore, it is suggested that the combination of size exclusion chromatography and HSCCC could be more widely applied for the isolation and purification of polyketides from marine fungi.

A multi-strand composite welding wire was applied to join high nitrogen austenitic stainless steel, and microstructures and mechanical properties were investigated. The electrical signals demonstrate that the welding process using a multi-strand composite welding wire is highly stable. The welded joints are composed of columnar austenite and dendritic ferrite and welded joints obtained under high heat input and cooling rate have a noticeable coarse-grained heat-affected zone and larger columnar austenite in weld seam. Compared with welded joints obtained under the high heat input and cooling rate, welded joints have the higher fractions of deformed grains, high angle grain boundaries, Schmid factor and the lower dislocation density under the low heat input and cooling rate, which indicate a lower tensile strength and higher yield strength. The rotated goss (G_RD) orientation of a thin plate and the cube (C) orientation of a thick plate are obvious after welding, but the S orientation at 65° sections of Euler’s space is weak. The δ-ferrite was studied based on the primary ferrite solidification mode. It is observed that low heat input and high cooing rate result in the increasing of δ-ferrite and high dislocation density was obtained in grain boundaries of δ-ferrite. M₂₃C₆ precipitates due to low cooling rate and heat input in weld seam and deteriorates the elongation of welded joints. The engineering stress-strain curves also show the low elongation and tensile strength of welded joints under low heat input and cooling rate, which is mainly caused by the high fraction of δ-ferrite and the precipitation of M₂₃C₆.

As novel materials for carbon capture, phase change solvents can separate into two immiscible phases during the CO2 capturing procedure under a certain temperature. The solvent systems can significantly decrease the energy consumption since the solvents can be regenerated by only heating the rich-CO2 phase. In this work, amino acid ionic liquids (AAILs) were synthesized using quaternary ammonium salts and amino acids as raw materials, and the aqueous solutions were prepared as novel liquid-solid phase change solvents. The results showed that the solvents had excellent CO2 absorption capacity, and the AAILs functionalized by glycine and tryptophan exhibited significant phase change properties. The mechanism of phase-change of the solvent were mainly due to the lower solubility of the product after reaction between AAILs and CO2. The solvent with tryptophan as anion could be regenerated by only heating the CO2-riched solid phase, which might significantly decrease energy consumption of regeneration. And the absorbent could be reused with the regenerated absorption ratio up to 79%. The solvent system has great potential in industrial application due to the easy operation process and efficient recycling ability.

Objective: Lipids are secreted into milk as bilayer-coated structures: milk lipid globules (MLGs). Adipophilin (ADRP) and perilipin 3 (TIP47) are associated with MLGs in human breast milk; however, the role of these proteins in milk lipid secretion is not fully understood. The aim of the study was to investigate levels of ADRP, TIP47 and total lipid content in human breast milk, their mutual correlations and dynamics during lactation. Research Methods & Procedures: Milk samples from 22 healthy lactating women (Caucasian, Central European) were collected at five time points during lactation (1–3, 12–14, 29–30, 88–90 and 178–180 days postpartum). Mass spectrometry-based method was used for quantification of ADRP and TIP47 in the samples. The gravimetric method was used to determine milk total lipid content. Results: We observed distinctive trends in ADRP, TIP47 levels and lipid content in human breast milk during the first 6 months of lactation. We also found a significant association between lipid content and ADRP, lipid content and TIP47, and ADRP and TIP47 concentrations in breast milk at all sampling points. Moreover, we derived an equation for estimating the mean lipid content of milk based on ADRP concentration in human breast milk. Conclusions: A mass spectrometry-based method was developed for quantifying ADRP and TIP47 in human breast milk. Strong mutual correlations were found between ADRP, TIP47 and total lipid content in human breast milk.

Promoter is a small region of DNA sequence in response to various transcription factors, which initiates a particular gene expression. The promoter-engineered bio-sensor can activate or repress gene expression through transcription factor recognizing specific molecules, such as polyamine, sugars, lactams, amino acids, organic acids or redox molecule, however, the reported applications of promoter enhanced bio-sensor are not too much. This review paper highlights the strategies of construction of promoter-gene engineered bio-sensor with human and bacteria’s genetic promoter array for high-throughput screening (HTS) molecular drugs, study of membrane protein’s localization and nucleocytoplasmic shuttling mechanism of regulating factor, enzyme activity, detection of the toxicity of intermediate chemicals, and probing bacteria density to improve value-added product titer. These bio-sensors’s sensitivity and specificity can be further improved by proposed approaches of Mn²⁺ and Mg²⁺ added random Error-prone PCR and site-directed mutagenesis which is applied for construction of bacteria’s “mutant library”. It is expected to establish flexible HTS platform (Bio-sensor array) to large-scale screen transcription factor-acting drugs, reducing the toxicity of intermediate compounds, and constructing gene dynamic regulatory system in “push and pull” mode to effectively regulate the valuable medicinal product production. This proposed novel promoter-engineered biosensors aided synthetic genetic circuit construction will maximize the efficiency of bio-synthesis of medicinal compound, which will greatly promote the development of microbial metabolic engineering and biomedical science.

Impervious surface mapping with high-resolution remote sensing imagery has attracted increasing interest as it can provide detailed information for urban structure and distribution. Previous studies have suggested that the combination of LiDAR data and high-resolution imagery for impervious surface mapping performs better than using only high-resolution imagery. However, due to the high cost of the acquisition of LiDAR data, it is difficult to obtain the multi-sensor remote sensing data acquired at the same acquisition time for impervious surface mapping. Consequently, real landscape changes between multi-sensor remote sensing data at different acquisition times would lead to the error of misclassification in impervious surface mapping. This issue has mostly been neglected in previous works. Furthermore, the observation differences generated from multi-sensor data, including the problems of misregistration, missing data in LiDAR data, and shadow in high-resolution images would also challenge the final mapping result in the fusion of LiDAR data and high-resolution images. In order to conquer these problems, we propose an improved impervious surface mapping method incorporating both LiDAR data and high-resolution imagery at different acquisition times in consideration of real landscape changes and observation differences. In the proposed method, a multi-sensor change detection by supervised multivariate alteration detection is employed to obtain changed areas and misregistration areas. The no-data areas in the LiDAR data and the shadow areas in the high-resolution imagery are extracted by independent classification yielded by its corresponding single sensor data. Finally, an object-based post-classification fusion is proposed to take advantage of independent classification results with single-sensor data and the joint classification result with stacked multi-sensor data. Experiments covering the study site in Buffalo, NY, USA demonstrate that our method can accurately detect landscape changes and obviously improve the performance of impervious surface mapping.

The superharmonic imaging of tissue has the potential for high spatial and contrast resolutions, compared to the fundamental and second harmonic imaging. For this technique, the spectral bandwidth of an ultrasound transducer is divided for transmission of ultrasound and reception of its superharmonics (i.e., higher than the second harmonic). Due to the spectral division for the transmission and reception, transmitted ultrasound energy is not sufficient to induce superharmonics in media without using contrast agents, and it is difficult that a transducer has a -6-dB fractional bandwidth of higher than 100%. For the superharmonic imaging of tissue, thus, multi-frequency array transducers are the best choice if available; transmit and receive elements are separate and have different center frequencies. However, the construction of a multi-frequency transducer for intravascular ultrasound (IVUS) imaging is particularly demanding because of its small size of less than 1 mm. Here, we report a recently developed dual-element focused IVUS transducer for the third harmonic imaging of tissue, which consists of a 35-MHz element for ultrasound transmission and a 105-MHz element for third harmonic reception. For high quality third harmonic imaging, both elements were fabricated to have the same focus at 2.5 mm. The results of tissue mimicking phantom tests demonstrated that the third harmonic images produced by the developed transducer had higher spatial resolution and deeper imaging depth than the fundamental images.

Alternative fuels available at low cost, friendly to natural environments and meet the energy needs and demands, have witnessed a growing demand and use today. Ethanol is an attractive renewable energy source with a high content of oxygen. Ethanol can be produced through ethanolisis, however for this work direct blending of conventional diesel, waste plastic pyrolysis oil and ethanol with commercial fuel improver CI-0808 purchased from Innospec company was attempted. The primary purpose of adding a cetane improver was to improve the combustion characteristics of the blends by at least 1- 3 ignition quality points. Five mixing ratios were chosen in the following order, 50:25:25, 60: 20:20, 70: 15:15, 80: 10:10 and 90: 5:5 for Waste Plastic Pyrolysis Oil (WPPO), ethanol and conventional diesel (CD) respectively. However, for the fuel additive mixing ratio the total volume percentage was considered and the ratio put at 0.01% of the total quantity of blended fuel. In this work WPPO, diesel blends and fuel additives improvers were used as alternative fuel. This was to evaluate their performance and emission characteristics in a stationary single cylinder water cooled experimental diesel engine. The CI-0808 was added due to its potential power to reduce emissions of CO, UHC, NO_X, PM and improved engine performance. The results obtained were compared carefully to ASTM standards and discussed using graph curves figures and tabulated values. The conclusion was that ethanol and WPPO blends can be used in diesel engines as alternative fuel without modification. Used in combination with cetane improvers the emissions reduce significantly and performance improved equalling that of conventional diesel fuel.

The key to simulating soil erosion is to calculate the vegetation cover (C) factor. Methods that apply remote sensing to calculate C factor at regional scale cannot directly use the C factor formula. That is because the C factor formula is obtained by experiment, and needs the coverage ratio data of croplands, woodlands and grasslands at standard plot scale. In this paper, we present a C factor conversion method from a standard plot to a km-sized grid based on large sample theory and multi-scale remote sensing. Results show that: 1) Compared with the existing C factor formula, our method is based on the coverage ratio of croplands, woodlands and grasslands on a km-sized grid, takes the C factor formula obtained from the standard plot experiment and applies it to regional scale. This method improves the applicability of the C factor formula, and can satisfy the need to simulate soil erosion in large areas. 2) The vegetation coverage obtained by remote sensing interpretation is significantly consistent (paired samples t-test, t = −0.03, df = 0.12, 2-tail significance p < 0.05) and significantly correlated with the measured vegetation coverage. 3) The C factor of the study area is smaller in the middle, southern and northern regions, and larger in the eastern and western regions. The main reason for that is the distribution of woodlands, the Hunshandake and Horqin sandy lands and the valleys affected by human activities. 4) The method presented in this paper is more meticulous than the C factor method based on the vegetation index, improves the applicability of the C factor formula, and can be used to simulate soil erosion on large scale and provide strong support for regional soil and water conservation planning.

A multiscale (micro-macro) approach is proposed for the establishment of the full thermal and induced stress fields in cracked composites that are subjected to heat flow. Both the temperature and stresses distributions are determined by the solution of a boundary value problem with one-way coupling. In the micro level and for combined thermomechanical loading, a micromechanical analysis is employed to determine the effective moduli, coefficients of thermal expansion and thermal conductivities of the undamaged composite. In the macro level, the representative cell method is employed according to which the periodic damaged composite region is reduced, in conjunction with the discrete Fourier transform, to a finite domain problem. As a result, a boundary value problem is obtained in the Fourier transform domain which is appropriately discretized and solved. The inverse transform and an iterative procedure provide the full thermal and stress fields. The proposed method is verified by comparisons with exact solutions. Applications are given for the determination of the thermal and stress fields in cracked fiber-reinforced polymeric composite, cracked porous ceramic material and cracked periodically layered ceramic composite caused by the application of heat flow. The presented formulation admits however the application of a combined mechanical and heat flux on cracked composites.

In this paper we introduce a computational model for the simulation of hypervelocity impact (HVI) phenomena which is based on the Discrete Element Method (DEM). Our paper constitutes the first application of DEM to the modeling and simulating of impact events for velocities beyond 5 kms⁻¹. We present here the results of a systematic numerical study on HVI of solids. For modeling the solids, we use discrete spherical particles that interact with each other via potentials. In our numerical investigations we are particularly interested in the dynamics of material fragmentation upon impact. We model a typical HVI experiment configuration where a sphere strikes a thin plate and investigate the properties of the resulting debris cloud. We provide a quantitative computational analysis of the resulting debris cloud caused by impact and a comprehensive parameter study by varying key parameters of our model. We compare our findings from the simulations with recent HVI experiments performed at our institute. Our findings are that the DEM method leads to very stable, energy–conserving simulations of HVI scenarios that map the experimental setup where a sphere strikes a thin plate at hypervelocity speed. Our chosen interaction model works particularly well in the velocity range where the local stresses caused by impact shock waves markedly exceed the ultimate material strength.

Warming of the climate system is unequivocal, and many of the observed changes are unprecedented over five decades to millennia. Globally the atmosphere and ocean is increasingly getting warmer, the amount of ice on the earth is decreasing over the oceans, and the sea level has risen. According to Intergovernmental Panel on Climate Change, the total increasing temperature globally averaged combined land and surface between the average of the 1850-1900 period and the 2003 to 2012 period is 0.78 °C (0.72 to 0.85). But should we prepare for such the relatively small change? The importance is not the mean of the warming but the considerable likelihood of climate change that could trigger extreme natural hazards. The impact and the risk of climate change associated with railway infrastructure have not been fully addressed in the literature due to the difference in local environmental parameters. On the other hand, the current railway network in Malaysia, over the last decade, has been significantly affected by severe weather conditions such as rainfall, lightning, wind and very high temperatures. Our research findings point out the extremes that can lead to asset system failure, degraded operation and ultimately, delays to train services. During the period of flood, the embankment of the track can be swept away and bridge can be demolished, while during drought, the embankment of the track can suffer from soil desiccation and embankment deterioration, high temperature increases the risk of track buckling and high winds can result in vegetation or foreign object incursion on to the infrastructure as well as additional quasi-static burden exerted. This review is of significant importance for planning and design of the newly proposed high speed rail link between Malaysia and Singapore.

We work within a Winterberg framework where space, i.e., the vacuum, consists of a two component superfluid/super-solid made up of a vast assembly (sea) of positive and negative mass Planck particles, called planckions. These material particles interact indirectly, and have very strong restoring forces keeping them a finite distance apart from each other within their respective species. Because of their mass compensating effect, the vacuum appears massless, charge-less, without pressure, net energy density or entropy. In addition, we consider two variable models, where, , is Newton’s constant, and, , increases with an increase in cosmological time. We argue that there are at least two competing models for the quantum vacuum within such a framework. The first follows a strict extension of Winterberg’s model. This leads to nonsensible results, if increases, going back in cosmological time, as the length scale inherent in such a model will not scale properly. The second model introduces a different length scale, which does scale properly, but keeps the mass of the Planck particle as, the Planck mass. Moreover we establish a connection between ordinary matter, dark matter, and dark energy, where all three mass densities within the Friedman equation must be interpreted as residual vacuum energies, which only surface, once aggregate matter has formed, at relatively low temperatures. The symmetry of the vacuum will be shown to be broken, because of the different scaling laws, beginning with the formation of elementary particles. Much like waves on an ocean where positive and negative planckion mass densities effectively cancel each other out and form a zero vacuum energy density/ zero vacuum pressure surface, these positive mass densities are very small perturbations (anomalies) about the mean. This greatly alleviates, i.e., minimizes the cosmological constant problem, a long standing problem associated with the vacuum.

Ecological Society is based on socioeconomic systems directed to the equilibrium of inter-relationships between environment, climate, production systems, technologies and human communities to promote the overall well-being of people and the Earth planet. It has a main goal for supporting sustainable development and equitable societies and achieving optimal health outcomes recognizing the interconnection between people, technologies, animals, plants, and their shared environment for the one health. Using data of scientific publications and patents until the year 2024, this study shows new technologies that can support the energy transition with the shift from fossil fuels to renewable energy sources in an effort to reduce CO₂ emissions for an ecological society having, whenever possible, zero-carbon economies to reduce environmental pollution and climate change and foster health and wellbeing of people. Results reveal that sustainable technologies supporting the transformation of the energy sector, economies and societies, and having a rapid growth, are blue hydrogen, floating photovoltaic systems, carbon capture and utilization, green hydrogen and liquid metal batteries. These findings can guide decisions of policymakers and stakeholders towards fruitful investments driving the transition from fossil-based to zero-carbon socioeconomic systems to have a long-run sustainable development of overall world system that has to direct towards an ecological society associated with one health.

Electrical energy demand increase does evolve rapidly due to several socioeconomic factors such as industrialization, population growth, urbanization and of course the evolution of modern technologies in this 4th industrial revolution era. Such rapid increase in energy demand introduces a huge challenge in power system. Such has paved way for network operators to seek for alternative energy resources other than the conventional fossil fuel system. Hence, the penetration of renewable energy into the electricity supply mix has evolved rapidly in the past three decades. However, the grid system has to be well planned ahead to accommodate such increase in energy demand in the long run. Transmission Network Expansion Planning (TNEP) is a well ordered and profitable expansion of power facilities that meets the expected electric energy demand with an allowable degree of reliability. This paper proposes a TNEP model that minimises the network reinforcements, operational costs and costs of renewable energy penetrations, while satisfying the increase in demand. The problem is formulated as a mixed integer linear programming (MILP) problem. The developed model has been tested in several IEEE test systems in multi-period scenarios. The paper also carried out a detailed derivation of the new non-negative variables in terms of the power flow magnitudes, the bus voltage phase angles and the lines’ phase angles for proper mixed integer variables’ decomposition techniques. Moreover, this paper tends to provide additional recommendation in terms of which particular year (within 20 years of planning period) can the network operators install new line(s), new corridor(s) and/or additional generation capacity to the respective existing power networks. Such is achieved by running incremental periods simulations from base year through the planning horizon. The results show the efficacy of the developed model in solving the TNEP problem with a reduced and acceptable computation time even for large power grid system.

To minimise environmental impact, avoid regulatory penalties, and improve competitiveness, energy-intensive manufacturing firms require accurate forecasts of their energy consumption so that precautionary and mitigation measures can be taken. Deep learning is widely touted as a superior analytical technique to traditional artificial neural networks, machine learning, and other classical time series models due to its high dimensionality and problem solving capabilities. Despite this, research on its application in demand-side energy forecasting is limited. We compare two benchmarks (Autoregressive Integrated Moving Average (ARIMA), and an existing manual technique used at the case site) against three deep learning models (simple Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)) and three machine learning models (Support Vector Regression (SVM), Random Forest, and K-Nearest Neighbors (KNN)) for short term load forecasting (STLF) using data from a Brazilian thermoplastic resin manufacturing plant. We use the grid search method to identify the best configurations for each model, and then use Diebold-Mariano testing to confirm the results. Results suggests that the legacy approach used at the case site is the worst performing, and that the GRU model outperformed all other models tested.

Solar electric and solar thermal energies are often considered as part of the solution to the current energy emergency. In the present work, a study of the characteristic length of tubes in flat photovoltaic-thermal (PVT) hybrid solar devices has been carried out using three heat transfer fluids: water, Emim-Ac ionic liquid and ionanofluid of graphene nanoparticles suspended in the above ionic liquid. Experimental measurements and numerical simulations have been performed using the HEATT® platform. Slower fluid velocities are recommended if high fluid outlet temperatures are the goal of the application, but this penalizes the overall thermal energy production. The characteristic process length, or most efficient distance at which heat transfer occurs, is not typically achieved in parallel tube PVT collectors; coil-type panels can improve the thermal and temperature performance of INFs in PVT solar collectors depending on the application.

Leveraging the liquid phase immiscibility effect and phase diagram calculations, a sequence of alloy powders with varying Fe content was designed and fabricated utilizing gas atomization method. Microstructural characterizations, employing SEM, EDS, and XRD analyses, revealed the successful formation of incomplete shell on the surfaces of Al-Bi-Fe powders, obviating the need for Sn doping. This study systematically investigated the microstructure, hydrolysis performance, and hydrolysis process of these alloys in deionized water. Notably, Al-10Bi-7Fe exhibited the highest hydrogen production, reaching 961.0 Nml/g, while Al-10Bi-10Fe demonstrated the peak conversion rate at 92.99%. The hydrolysis activation energy of each Al-Bi-Fe alloy powder was calculated using the Arrhenius equation, indicating a reduction in activation energy achieved through Fe doping.

In the context of high energy costs and energy transition, the optimal use of energy resources for industrial consumption is fundamental. This paper presents a decision-making structure for large consumers having flexibility to manage electricity or natural gas consume to satisfy the demands of industrial processes. The proposed modelling energy system structure relates monthly medium and hourly short-term decisions to which these agents are subjected, represented by two connected optimization models. In the medium term, the decision occurs under uncertain conditions of energy and natural-gas market prices, as well as hydropower generation (self-production). The monthly decision is represented by a risk-constrained optimization model. In the short term, hourly optimization considers the operational flexibility of energy and/or natural-gas consumption, subject to the strategy defined in the medium term and mathematically connected by a regret cost function. The model application of a real case of a Brazilian aluminum producer indicates a measured energy cost reduction of U$ 3.98 million over a six-month analysis period.

The 21st century's booming population and escalating energy demands have driven significant efforts to enhance the Energy Hub (EH). The goal is to create a more intelligent and responsive system that can effectively cater to consumer needs while simultaneously improving the reliability and efficiency of contemporary energy infrastructure. The Internet of Things (IoT) has emerged as a key enabling technology for Smart Energy Hubs (SEH). While IoT offers a plethora of innovative solutions across various sectors, including critical infrastructure, it also introduces new security challenges. Since IoT devices inherently connect to the internet, SEH systems become susceptible to a wider range of cyberattacks. This vulnerability heightens as the number of integrated IoT devices increases. A single compromised device can expose the entire hub to cyber threats. Such attacks on energy sources have the potential to cripple entire cities, resulting in significant economic devastation. Therefore, robust security measures must be implemented before the widespread adoption of IoT-based devices in energy systems. This research delves into the architecture and infrastructure of IoT-enabled SEH. Following this, the focus shifts to analyzing the primary challenges and security concerns associated with their deployment. Finally, the research emphasizes cutting-edge approaches and tools that can bolster the security and resilience of IoT-enabled SEH against contemporary physical and cyber threats. Implementing secure and advanced data transmission systems based on blockchain technology holds promise in safeguarding the entire EH from cyber-physical attacks in the future.

High mountain rainforests are vital in the global energy and carbon cycle. Understanding the exchange of energy and carbon plays an important role to reflect responses to climate change. In this study, an eddy-covariance (EC) measurement system installed in the high Andean Mountains of South Ecuador was used. As EC measurements are affected by heterogeneous topography and vegetation height, the main objective was to estimate the effect of the sloped terrain and the forest on the turbulent energy and carbon fluxes considering the energy balance closure (EBC) and the heat storage. The results showed that the performance of the EBC was generally good and estimated to be 79.5%. This could be improved when the heat storage effect was considered. Based on the variability of the residuals in the diel modifications in the imbalances were highlighted. Particularly, during daytime residuals were largest (56.9 W/m² on average) with a clear overestimation. At nighttime mean imbalances were rather weak (6.5 W/m²) and mostly positive, while strongest underestimations developed in the transition period to morning hours (down to -100 W/m²). With respect to the Monin-Obukhov stability parameter ((z-d)/L) and the friction velocity (u*) it was revealed that largest overestimations evolved in weak unstable and very stable conditions associated with large u* values. In contrast, underestimation was related to very unstable conditions. The estimated carbon fluxes were independently modelled with a non-linear regression using a light-response relationship and reached a good performance (R²=0.52). All fluxes were additionally examined in the annual course to estimate whether both, energy and carbon fluxes, resembled the microclimatological conditions of the study site. This unique study demonstrated that EC measurements provide valuable insights into land surface -atmosphere interactions and contribute to our understanding of energy and carbon exchanges. Moreover, the flux data provide an important basis to validate coupled atmosphere ecosystem models.

Visual servoing technology has been well developed and applied in many automated manufacturing tasks, especially in tools’ pose alignment. To access a full global view of tools, most applications adopt eye-to-hand configuration or eye-to-hand/eye-in-hand cooperation configura-tion in an automated manufacturing environment. All research papers mainly put efforts into developing control and observation architectures in various scenarios, but none of them has dis-cussed the importance of the camera’s location in eye-to-hand configuration. In a manufacturing environment, the quality of camera estimations may vary significantly from one observation loca-tion to another, as the combined effects of environmental conditions result in different noise levels of a single image shot at different locations. In this paper, we propose an algorithm for the camera’s moving policy so that it explores the camera workspace and searches for the optimal location where the images’ noise level is minimized. Also, this algorithm ensures the camera ends up at a subop-timal (if the optimal one is unreachable) location among the locations already searched, with limited energy available for moving the camera. An automated manufacturing application has been sim-ulated and the results show the success of this algorithm’s improvement of observation precision with limited energy.

In previous papers we have derived equations of motion with radiation terms and proved the existence-uniqueness of periodic solution of the 3-body problem of classical electrodynamics. The number of equations of motion – 12 is more than the number of unknown functions – 9. It turns out that 9 equations are independent and the rest 3 ones are consequences of them. These three equations have a solution and they give the expressions for the energy of the moving particles. The main goal of the present paper is to estimate the energy of rounding about the nuclei particles and compare it with the values given by quantum mechanics.

The issue of climate change represents a significant global risk that demands immediate attention. However, higher education frequently fails to incorporate global perspectives and intricate transdisciplinary matters. In order to integrate global and transdisciplinary aspects into the curriculum of a technical university with a focus on computer science, a Master's programme has been developed. The programme's objective is to situate global risk at the core of the curriculum, thereby encouraging scientists to work beyond the boundaries of their respective disciplines. The paper considers the challenges that science and higher education must address to facilitate pathways towards a peaceful and sustainable future. It argues for a shift in focus from short-term and localised issues to a more holistic approach, emphasising the importance of considering universal problems. The curriculum, as developed, may be regarded as a guide for future university efforts in the fields of global risk and climate change.

The two combined technologies WPT and AI still have huge prospects for several industries such as consumer electronics and industry. This paper looks at the recent developments in WPT, particularly concerning AI-improved efficiency, range, and reliability. AI-driven also works to control power, manage device alignment, and test when maintenance is necessary, this makes powering smarter and more efficient. Accuracy is given by deploying machine learning and computer vision, and on the other hand, the maintenance procedure is done through predictions. Furthermore, the ability of the management system to include the use of AI in power regulation enables the control of consumption based on the load. In this study, the positive future effect of AI is discussed concerning improvements in WPT technologies, which is paramount in subsequent applications. Based on the results, it is essential to continue the investigation of the topic within this interdisciplinary science to advance existing problems and discover novel possibilities for evolution. Data based on experiments shows a significant improvement in the efficiency of information transfer where AI is used with rates up to 25% higher than those using the traditional ways. Additionally, advanced maintenance forecasting algorithms improve system availability by cutting down on 30% of the downtimes when applied in real-life applications of AI technology. The study also looks at the possibilities of the outlook to the future including emergence of the distributed artificial intelligence for autonomous WPT network, and additional potential of quantum computing for the use to improve the algorithm. This indicates that there is a new era for the convergence of WPT and AI to enhance the progress of power transfer system in conditions of elegant, efficient, and robust.

The ever-growing concerns about global warming and the rapid depletion of fossil fuels have triggered a rising interest in research for cleaner, more efficient and cost-effective energy generation. Organic Rankine cycle systems have immense potential to become a strong alternative to conventional methods of energy generation. However, such systems’ efficiency is limited by the performance of the incorporated gas expanders or prime movers. Conventional gas expanders often utilize ports or cam-operated valves. Ported expanders offer limited efficiency and controllability, and produce high emissions causing a large amount of heated and compressed gas to be wasted. Cam-operated valves, in contrast, increase expansion performance compared to ported expanders, however, they have no variability and adaptability to changes in system parameters such as gas pressure, temperature, dryness fraction, and load variation to name a few. On a positive note, this issue can be addressed by adopting a variable timing and fast-operating, control valve with an accurate and adaptive control mechanism at the expander inlet. A properly designed and controlled valve can greatly improve the system performance of gas expanders and pave the way for an efficient low-cost alternative energy generation. This manuscript provides a comparative review of recent progress on the design, modeling, optimization, and control aspects of valves for gas expanders. A clear pathway on the scope of further development is also drawn based on the present state of the art.

Newton's kinetic energy equation delineates the laws of motion for objects travelling at speeds much less than the speed of light, an equation extensively validated at these lower velocities. Any new, more complete formulation of kinetic energy must approximate Newton's kinetic energy equation under these conditions. Einstein's special relativity, utilizing the Lorentz factor, introduces a new perspective on spacetime, adhering to the requirement that the Newtonian kinetic energy equation serves as an approximation at low velocities. In this paper, we identify all factor expressions satisfying the condition that their approximate solutions at low velocities align with Newton's kinetic energy equation. We introduce the Yuyunrui factor, incorporating two real number hyperparameters, $\alpha$ and $\beta$, encompassing all potentially correct factors, with the Lorentz factor as a specific instance where $(\alpha=0$, $\beta=1)$. We classify all potentially correct $(\alpha, \beta)$ combinations into six categories based on their distinct scaling effects and present the corresponding scaling effects. The combinations of $(\alpha, \beta)$ values applicable in the real world are necessarily included within these candidates, with the precise values requiring rigorous experimental validation. This work aims to refine our understanding of special relativity beyond the Lorentz factor.

The textile processing sector is accountable for the extensive utilisation and pollution of freshwater resources. Due to heightened consumer awareness of environmental legislation regarding freshwater contamination, the textile sector is compelled to review and improve their current water consumption practices. Foan dyeing technique is a highly promising alternative that has the ability to contribute to a more sustainable future. This review paper discussed the current state of foam dyeing technology, highlighting its advantages in terms of re-duced consumption of water and energy, as well as minimal usage of dyes and chemicals compared to conventional methods. The foam configuration allows for more effective application of dyes. As a result, there is a decrease in dye consumption and a reduction in processing time. The process is environmen-tally sustainable since it significantly reduces water pollution. Furthermore, foam dyeing method consumes less energy due to its lower dying temperature need compared to conventional processing technology. Additionally, this paper highlights the continued investigation of foam dyeing on many types of textile substrates (such as fibres, yarns, and fabric) and compositions including cotton, polyester, and their mixtures. In addition, the techno-economic comparison of foam dyeing and conventional dyeing is also given. This review paper addi-tionally endorses the Sustainable Development Goals (SDG 6, clean water and sanitation) by examining the preservation of usable water and the decrease in wastewater discharge through foam dyeing technology. Lastly, the potential advantages of foam dyeing in terms of significantly reducing water consump-tion and the generation of waste in the textile industry were also addressed.

In the context of the Bridge Electromagnetic Theory, a quantum-relativistic theory based on Maxwellian electromagnetism, it has recently been shown that the characteristics of a hydrogen atom can be obtained through an electron-proton orbital capture process forming a non-radial emitting dipolar electromagnetic source. The model structurally different to the Bohr-Sommerfeld and Schrödinger models has now been deepened and completed by testing it on the properties of hydrogen and deuterium atoms and of helium and lithium in hydrogenoid form. These last two atoms are of cosmological interest as they are the heaviest elements produced by electron capture in the early universe. The theoretical results obtained regarding the atomic structure and spectra are in excellent agreement with the observational data by suggesting the implicit correctness of the model. It is also highlighted that the electron-nucleus interaction is influenced on an isotopic basis as a function of the value of the inertial mass of the nuclei considered.

The global photovoltaic (PV) installed capacity, vital for the electric sector decarbonation, has reached 1,552.3 GWp in 2023. In France, the capacity stood in April 2024 at 19.9 GWp. The growth of the PV installed capacity over a year was nearly 32% worldwide and 15.7% in France. However, integrating PV electricity into grids is hindered by poor knowledge of rooftop PV systems, constituting 20% of France's installed capacity, and the lack of measurements of the production stemming from these systems. This problem of lack of measurements of the rooftop PV power production is referred to as the lack of observability. Using ground truth measurements of individual PV systems, available at an unprecedented temporal and spatial scale, we show that estimating the PV power production of an individual rooftop system by combining solar irradiance and temperature data, the characteristics of the PV system inferred from remote sensing methods and an irradiation-to-electric power conversion model provides accurate estimations of the PV power production. Our study shows that we can improve rooftop PV observability, and thus its integration into the electric grid, using little information on these systems, a simple model of the PV system and weather data.

Previous studies on the water-energy nexus mainly focused on the calculation and comparison of resource consumption at the national or regional level, lacking interprovincial sector-sector transfer analysis. In this study, the intensity of water and energy consumption of various sectors in China were calculated, the key nodes and paths of resource networks were identified, and countermeasures for resource conservation were proposed from the new perspective of the “dual saving” and “bidirectional saving” of water and energy. The results showed that the metallurgical industry (Me) in Jiangsu and the chemical industry (Ch) in Hebei and Jiangsu had high node strength in the water and energy network and were key sectors in China with “dual saving” effects of water and energy. The construction industry, Ch, Me in Jiangsu, electricity and hot water production and supply industry in Beijing, and Me in Hebei had high node strength in the water-related energy network and energy-related water network, significantly supporting the “bidirectional saving” effect of water and energy in China. The electrical equipment industry (El) in Jiangsu→El in Zhejiang, El in Zhejiang→El in Shanghai frequently appeared in key paths, which could effectively reduce the resource consumption of the entire network.

Multicropping can solve the energy and GHG balance problems, but the emergence, development and productivity of such mixed crops are at risk due to uneven distribution of precipitation. For this reason, investigations were performed at the Experimental Station of Vytautas Magnus University, Lithuania. Single maize crop was compared with Crimson/red clover, Persian clover, and alfalfa intercropped maize cultivations. Results showed that under arid conditions, the intercrops biomass was about 4 times less than under humid. Humid conditions were less suitable for maize and when growing with intercrops they produced about 3-5 t ha-1 less dried biomass, and when growing sole - about 6 t ha-1 less biomass than in arid conditions. Due to the higher yield of maize biomass in arid season, better energy indicators of the crop were obtained in arid conditions than in humid. The difference between net energy was about 122-123 MJ ha-1 in all treatments, except for the maize crop with intercropped alfalfa, where the difference was 62 MJ ha-1. All tested technologies were environmentally friendly, CO2 equivalent varied between treatments from 804 to 884 kg ha-1. Uneven distribution of precipitation during vegetative season makes us think about the improvement of intercropping technologies. Sowing intercrops at the same time with maize could improve its germination but increase the problem of weed spread.

The effect of wave forces on the structural safety of fixed oscillating water-column (OWC) devices is critical. In this study, a three-dimensional numerical model of an oscillating water column (OWC) device is constructed using the computational fluid dynamics software OpenFOAM. Subsequently, the hydrodynamic performance of the numerical model is comprehensively validated. Finally, the hydrodynamic performance data are analyzed in detail to obtain meaningful conclusions. The results indicate that the horizontal wave force on the OWC device is approximately 6.6 to 7.9 times greater than the vertical wave force, whereas the lateral wave force is relatively small. Both the horizontal and vertical wave forces decrease as the relative water depth increases under a constant wave period and height. The horizontal wave force increases linearly with the wave height, whereas the vertical wave force increases with the square of the wave height. In addition, the highest dynamic water pressure is observed at the interface between the water surface and device, both within and outside the front wall of the gas chamber. The dynamic water pressure at different locations on the front chamber increases and subsequently decreases as the wave frequency increases.

A quantum theory of gravity is among the most pursued goals in physics. I present a definitive and direct experimental proof that refutes the widely believed hypothesis of quantum gravity. The detections of astrophysical gravitational waves (GW) are inferred from the differential oscillations of suspended mirrors of optical interferometers like the aLIGO detectors. If gravity is indeed quantized, then the average energy $\bar{E}$ in the minute oscillations of the mirrors, at a frequency $\nu$, corresponds to the absorption of an integer number $N$ gravitational quanta with total energy $E_{gw}=Nh\nu$. The coherent and coincident detections across large separations of detectors, and also the constraint of the equivalence principle, dictate that the average number of quanta $\bar{N}$ forcing the oscillations obeys $\bar{N}\gg 1$, or $1/\sqrt{\bar{N}}\ll 1$. However, the average energy $\bar{E}$ in the differential oscillations is smaller than the energy of a single quantum of GW radiation at the detected astrophysical GW frequencies. This startling finding implies that $\bar{N} <1$, which is physically impossible if gravity is quantized. This singular contradiction refutes the long-held hypothesis that gravity is quantized.

The purpose of this article is to present an IoT architecture implemented in an intelligent environment, focusing on the optimization of initial conditions and device management. A general control system was developed using an open-source platform, integrating aWi-Fi network and YAML coding to enable communication and control of the IoT architecture devices. The implementation includes actuators, sensors, and control devices that, through a graphical interface, allow the visualization of the historical record of activities and real-time measurements of connected devices. The design includes specific mechanisms and the analysis of materials through tensile tests on specimens, following the ASTM E8 standard, and deformation test simulations, in which PLA demonstrated better performance compared to materials like ABS and resin. The significance of the implemented IoT system lies in an energy saving of 58% compared to conventional structures. Additionally, the study of response times for network connections showed that using an Ethernet cable resulted in a response time of 1.21 seconds, representing a reduction of 1.65 seconds compared to the second lowest value obtained.

Incorporating renewables in the power grid presents challenges for stability, reliability, and operational efficiency. Integrating Energy Storage Systems (ESS) offers a solution by managing unpredictable loads, enhancing reliability, and serving the grid. Hybrid storage solutions have gained attention for specific applications, suggesting higher performance in some respects. This paper compares the performance of hybrid energy storage systems (HESS) to single battery, evaluating their energy supply cost and environmental impact through optimization problems. The model generates an optimized dispatch, minimizing cost or environmental impact of supplying energy to a generic load while considering battery degradation. Seven technologies are assessed, with an example applied to an industrial site contrasting vanadium redox flow (VRFB) and lithium batteries considering the demand of a local load (building). The results also indicates that efficiency and degradation curves have the highest impact in the final costs and environmental functions on the various storage technologies assessed. For the simulations of the example case, a single system only outperforms the hybrid system in cases where lithium efficiency is higher than approximately 87% and vanadium is lower approximately 82%.

Responsible, efficient, and environmentally conscious energy consumption practices are increasingly essential for ensuring the reliability of the modern electricity grid. This study focuses on leveraging time series analysis to improve forecasting accuracy, crucial for various application domains where real-world time series data often exhibit complex, non-linear patterns. Our approach advocates for utilizing Long Short Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) models for precise time series forecasting. To ensure a fair evaluation, we compare the performance of our proposed approach with traditional neural networks, time-series forecasting methods, and conventional decline curves. Additionally, individual models based on LSTM, Bi-LSTM, and other machine learning methods are implemented for a comprehensive assessment. Experimental results consistently demonstrate that our proposed model outperforms all benchmarking methods in terms of mean absolute error (MAE) across most datasets. Addressing the imbalance between activations by consumer and prosumer groups, our predictions show superior performance compared to several traditional forecasting methods, such as the Autoregressive Integrated Moving Average model (ARIMA) and Seasonal Autoregressive Integrated Moving Average model (SARIMA). Specifically, the root mean square error (RMSE) of Bi-LSTM is 5.35%, 46.08%, and 50.6% lower than LSTM, ARIMA, and SARIMA, respectively, on the May test data.

Integrated energy systems (IES) seek to minimize power generating costs in future power grids through the dynamic coupling of different energy technologies. To accommodate fluctuations in load demand due to the penetration of renewable energy sources, flexible operation capabilities must be fully exploited, i.e., even power plants that are traditionally considered as base-load units need to be operated according to unconventional paradigms. Thermomechanical loads induced by frequent power adjustments can accelerate the wear and tear of components. If a unit is flexibly operated without respecting limits on materials and components, the risk of failures of expensive components will eventually increase, nullifying the additional profits ensured by flexible operation. In addition to the limits on power variations (explicit constraints), the solution of the unit dispatch problem needs to meet the limits on temperature, pressure and flow rate variations (implicit constraints). The FARM (Feasible Actuator Range Modifier) module was developed to enable existing optimization algorithms to identify solutions to the unit dispatch problem that are both economically favorable and technologically sustainable. Thanks to the iterative dispatcher-validator scheme, FARM permits addressing all the imposed constraints without excessively increasing the computational costs. In this work, the algorithms constituting the module are described, and the performance was assessed by solving the unit dispatch problem for an IES composed of three units, i.e., Balance of Plant, Gas Turbine, High-Temperature Steam Electrolysis. Finally, FARM module provides dedicated tools for visualizing the response of the process variables of interest during operation transients and a tool aiding the operator at making decisions. These techniques might represent the first step towards the deployment of an Ecological Interface Design (EID) for IES units.

The extent of access to energy of displaced populations in settlements and camps in Africa is largely 14 unknown. A multitude of contextual factors, such as the location and the characteristics of housing, 15 the legal status, the socio-cultural background, and the availability of humanitarian and public ser- 16 vices impact the living conditions and the needed energy services. Limitations in accessing energy 17 services have direct, multilayered, and far-reaching implications, including impacts on health, nu- 18 trition, education, protection, and livelihood. The objective of this article is to contribute to a more 19 comprehensive overview of the current state of energy access in displacement contexts on the Afri- 20 can continent by identifying and utilizing existing data. After a screening of the vast and various 21 available information, setting up of a database, consolidating the gathered data as well as assessing 22 the quality through a quality assessment method, the currently available information is visualized 23 and discussed. Considerable differences in the access to electricity for displaced persons across the 24 countries are found. For both electricity and clean cooking, the availability for displaced persons 25 ranges from nearly no access at all up to an access rate of 100%. More strikingly, the results also 26 show that besides South Africa and countries in the Maghreb region, the access to both clean cook- 27 ing and electricity for displaced persons is very low. At the same time, the fragmented data availa- 28 bility, the poor data quality, and the questionable expediency of available data allows neither solid 29 theoretical conclusions nor planning of effective practical implementation measures. Novel inter- 30 disciplinary research, conceptual frameworks and indicators are needed for the purpose of compa- 31 rability and consistency. Future research has the potential to more comprehensively capture the 32 current state of access to energy in displacements contexts and in a next step to examine how energy 33 is interwoven in the lives of displaced people to derive a set of more detailed context-sensitive en- 34 ergy indicators. It is essential that the concerned persons, the displaced persons themselves are in- 35 cluded in the research in a meaningful way.

Environmental preservation presents numerous challenges, particularly in capturing carbon di-oxide. Despite multiple studies on this topic, there remains a need for additional capture plants, particularly in the energy sector. The power industry emits flue gas during its post-combustion process, and capturing carbon dioxide from this gas would make power plants more environ-mentally friendly while aiding the preservation of the planet. The work conducted in This study aimed to investigate the gap in knowledge to contribute to the development of a better world without harming it. The identified research gap reveals no established optimal monoethanola-mine (MEA) solvent concentration for the process. A simulation was performed using Aspen Hysys to analyze the absorption efficiency at higher solvent concentrations and address this is-sue. The input data was obtained from a flue gas capture plant in the UK. The results obtained from discrete increments indicate an optimal solid result for an MEA concentration of 36 wt.%. Further analysis of temperature and pressure revealed that other solvents, such as diethanola-mine (DEA) and triethanolamine (TEA), are more stable at higher concentrations and that DEA and TEA at the same concentration as MEA consumed significantly less energy to capture carbon dioxide.

Retention of placenta (RP) in crossbred cattle poses health, reproductive, and economic challenges linked to oxidative stress, hormonal imbalances, and complex causative factors. Emphasizing the key role of mitochondria in oxidative phosphorylation (OXPHOS) and hormone synthesis, our study is the first to explore differences in OXPHOS activity, mitochondrial content, and mtDNA-encoded gene expression between normal and RP. In addition, gross morphological examination of placental tissues from both groups revealed noticeable disparities in cotyledons of RP as compared to normal placenta. Moreover, OXPHOS complex I specific activity was decreased in RP, and RT-qPCR analysis unveiled a significant reduction in transcript levels of 10 mtDNA-encoded protein genes. While COX3 and ATP8 genes showed no significant changes, ND6 increased in RP. Whereas, mtDNA copy number varied between animals but showed no statistically significant differences among groups. Retained placenta manifests alterations in the transcript levels of various mitochondrial genes, complex activity, and mtDNA content, potentially linked to heightened energy demands or mitochondrial insufficiency, posing an elevated risk. Overall, our study suggests a link between mitochondrial abnormalities, gene expression, and RP. This connection could influence the removal of fetal membranes by affecting energy supply, hormonal balance, and oxidative stress. This study also offers novel insights and serves as a valuable animal model for studying placental insufficiency in humans. Moving forward, comprehensive investigations are imperative to unravel underlying mechanisms and intricate roles of mitochondria in the context of RP, paving the way for targeted interventions to mitigate health, reproductive, and economic challenges in crossbred cattle.

The assessment of Photovoltaic (PV) systems, a key component of sustainable energy solutions, is of paramount importance in the pursuit of a greener future. This abstract provides an overview of environmental assessments associated with PV systems, considering their life cycle, energy production, and ecological impact. The study explores various methodologies and tools employed in the evaluation of PV systems’ environmental performance, addressing issues such as material sourcing, manufacturing, installation, and end-of-life disposal. Additionally, it delves into the trade-offs and challenges in achieving sustainability objectives in the solar energy sector. By critically examining the environmental implications of PV systems, this research contributes to informed decision-making and the sustainable integration of solar power into our energy landscape.

Despite its growing importance as a dry beneficiation method, there is a lack of studies exploring the fundamentals aspects of dry jigging. In this sense, this study focuses on explore some essential aspects of density-based separation in dry jigs, like pulse profile, energy consumption and, especially, stratification over time, also exploring an innovative operational strategy here called “transient pulsation”. The tests were carried out using density tracers distributed across 11 density ranges (0.4-2.4 g/cm³) and employing a base bed (gravel) to analyze their segregation in a pilot-scale dry jig. Pressure drop and active power data were acquired to measure the pulse characteristics and energy consumption. The findings suggest that establishing the dry jigging cycle is more complex than in hydraulic jigs due to the compressibility of air. Stationary pulsation tests revealed an unstable behavior of stratification over time, contrasting with traditional hydraulic jigs, while transient pulsation tests unveiled varied trends in separation quality. Energy consumption analysis underscored the significant role of the blower drive. These insights have the potential to inform operational enhancements in dry jigging.

Observation of the distribution of the velocity of galactic rotation curves differed from their expected centripetal form and lead to the notion of Dark Matter or modifications to Newtonian and General Relativity, such as MOND, TeVeS and the like and even Quantised Inertia. We aim to show that General Relativity with Dark Energy/the Cosmological Constant is all that is needed, with the proviso that the Cosmological Constant can increase in the presence of a gravitational field and become gravitating to account for the hypothesis of Dark Matter Haloes.

The scope of this work is to empirically check and prove the practical applicability of the Primordially Hydridic Earth (hereinafter PHE) concept for early exploration of the resources of naturally occurring hydrogen. With the PHE concept postulates interpreted within the local geological, tectonic, petrological and geophysical context, the reconnaissance plan, as well as the field exploration and data acquisition programs, were put together and implemented in the field. The results obtained from the surface (

Keywords: Energy harvesting, Hybrid, Medical Wearable Sensor Nodes, Health Monitoring, Power Management

In this paper, four probability functions are compared with the purpose of establishing a methodology to improve the accuracy of the estimated wind energy in a desert city in northwestern Mexico. For the statistical modeling, 3 time series of wind speed data were used, corresponding to the years 2017, 2018, and 2019, recorded with a sonic anemometer at a sampling frequency of 10 Hz. From these, the analysis is performed with different stationarity periods (5, 30, 60, and 600 seconds). The estimation of the parameters characterizing the probability density functions was performed using different methods; the statistical models were evaluated by the coefficient of determination and the Nash-Sutcliffe efficiency coefficient, while their accuracy by the measured quadratic error, the mean square error, the mean absolute error, and the mean absolute percentage error. It was found that Weibull with the energy pattern factor method and Gamma using the method of moments were the probability density functions that best describe the statistical behavior of the wind speed and, therefore, better estimate the energy generated, so it is concluded that the proposed methodology will allow having greater confidence, both in the estimation of the wind speed and in the small wind energy available for its use.

When dealing with limited sample sizes or missing values in time series data, achieving high accuracy in forecasting becomes challenging. This challenge is particularly common in wind energy forecasting, where many wind farms lack complete data. In this paper, we employ the widely-used ARIMA (Autoregressive Integrated Moving Average) model for time series forecasting. To address the difficulties posed by limited sample sizes and missing values, we adopt a unique approach by utilizing multiple ARIMA models for different datasets. A key distinction from other multiple models methods for time series forecasting lies in each model within our approach using its own dataset, making the fusion process more intricate. To overcome this, we incorporate neural networks and two techniques for model fusion: direct fusion with a learning mechanism and compensation for missing data through training. For each ARIMA model, we introduce a noise estimation method to circumvent the white noise assumptions inherent in ARIMA models. Finally, we apply these methods to wind energy prediction, and our experimental results showcase a significant enhancement in forecasting accuracy.

The goal of the study presented in this article is to investigate interconnection issues of the Intelligent Electrical Networks (IENs) with the existing ones, as well as the deployment of the appropriate load forecasting demand techniques. The concept of IENs has recently emerged as a potential solution to the global energy problem, due to the more flexible dispersed production units that are involved. In the same context, additional issues, such as the reduction of production-transfer-distribution costs as well as power losses are analyzed as well, via the penetration of Renewable Energy Sources (RESs). To this end, it is necessary to use appropriate advanced infrastructure, where in combination with modern telecommunication networks will make possible the full exploitation of IENs. Moreover, to make the electricity system more efficient, to avoid voltage and frequency imbalance issues and to implement optimal production and consumption planning, the Electric Load Forecast (ELF) is a vital process and plays a key role in the planning as well as in the management of electricity systems. Therefore, recent state of the art approaches in ELF methods are discussed as well.

In steel structures, oblique thin steel plates serve as panel zones in structures spanning large spaces (e.g., warehouses and gymnasiums). Considerable research has been conducted on the shear buckling of panels due to seismic loads acting on a structure. Conversely, under snow or wind loads, the panel zone may experience compressive and tensile stresses simultaneously from two directions. Considering the economic preference for thin steel plates, evaluating the elastic critical local buckling stresses in the panel zone under biaxial normal stress may provide essential information to structural engineers. In this study, an elastic buckling analysis based on the energy method is performed to clarify the impact of panel geometry and boundary conditions on the elastic local buckling stresses of oblique panel zones. As confirmed, the buckling stresses derived from the energy method could simulate the local buckling stresses with accuracy comparable to that of finite element analysis, and an engineer-friendly design formula was proposed. Finally, we determined the correlation of the buckling stresses under biaxial stresses and presented a method for evaluating this correlation. Engineers can utilize the provided design equations to more efficiently and accurately calculate buckling loads, facilitating safer and more economical design of structures with oblique plates.

Real assets in the energy market are subject to ecological uncertainty due to the penetration of renewables. We illustrate this point by analysing electrolysers, a class of assets which recently became the subject of a large interest, as they lead to the production of the desirable green hydrogen and green ammonia. The latter has the advantage of being easily stored and has huge potential in decarbonising both the fertilizer and shipping industries. We consider the optimization of green ammonia production with different types of electricity procurement in the context of stochastic power and ammonia markets, a necessary assumption to translate the features of renewable, hence intermittent, electricity. We emphasise the importance of using stochastic prices to effectively model the volatile nature of the price dynamics, illustrating the project risks that can be mitigated by hedging activities. This study shows the pivotal role of flexibility when dealing with fluctuating renewable production and volatile electricity prices to maximise profits and better manage risks.

Multi-cropping can solve the energy and GHG balance problems, but the emergence, development and productivity of such mixed crops are at risk due to increasing drought periods. For this reason, single maize crops with inter-row mellowing and weed-mulching practices were compared with faba bean, crimson and Persian clover, and alfalfa intercropped maize cultivations. Tests were performed at the Experimental Station of Vytautas Magnus University, Lithuania. Results showed that by the 30% higher energy inputs were calculated in technologies with intercropped crimson, Persian clover, and alfalfa than in monocropped maize. Single maize crops produced by the 23-40% higher biomass yields compare to clover and alfalfa intercropped maize because, due to the drought periods, intercrops weakly sprouted and developed. Lower harvests reduced the energy output by up to 38%, energy efficiency ratio - by up to 2.5 times and net energy - by up to 42%. All tested technologies were environmentally friendly and similar according to the CO2 equivalent. Long-term drought periods during vegetative season due to climate change make us think about the improvement of intercropping technologies. Sowing intercrops at the same time with maize could solve the problem of its germination but highlight the problem of abundant weed spread. Therefore, this takes us to another sphere of agrotechnologies - the effective non-chemical weed control.

Heterometallic rare-earth clusters have garnered much attention because of their interest-ing solid-state structures and versatile applications in catalysis and material and polymer chemistry over the last four decades. Particular interest is rare earth–transition metal co-ordination compounds that behave as single-molecule magnets (SMMs) because of their potential use in information storage, spintronic devices, and magnetic refrigeration sys-tems. Another implementation involves using lanthanide clusters in luminescent-based sensing for environmental protection and security screening or in photocatalytic energy conversion reactions. Heterometallic rare earth complexes offer the promise of catalytic performance of many organic reactions due to their high oxygenophilicity, Lewis acidity, high coordination numbers, and ability to change the coordination environment quickly. Therefore, they are commonly applied as catalysts in asymmetric synthesis, oxidation re-actions, and CO2 conversions or as initiators in ring-opening polymerization and copoly-merization of cyclic monomers. They are also attractive single-source molecular precur-sors for functional inorganic materials. This review focused on the unique features and properties of the heterometallic rare earth complexes, which are closely correlated with their molecular structure. Our summary is a valuable resource, providing researchers with an insightful exploration of the heterometallic rare-earth complexes that demonstrated multimetallic synergy and cooperativity effects in various applications.

Cloud computing transformed IT organizations with its on-demand model, notably Infrastructure as a Service (IaaS). Cloud providers manage extensive physical devices, consuming substantial energy and bandwidth for data traffic during virtual machine deployment. Balancing energy and bandwidth are critical. This thesis presents BOHGOA integrated with ACO to optimize virtual machine placement in clouds, simultaneously considering bandwidth and energy. It yields Pareto-optimal solutions for this trade-off. Using CloudSim, BOHGOA outperformed GA, ACO, and FFD algorithms, reducing bandwidth consumption by 54.14%, 32.11%, 57.47% (240 VMs) and 38.12%, 22.76%, 47.05% (500 VMs) respectively. Additionally, it decreased physical machine energy usage by 37.70%, 34.01%, 40.14% (240 VMs) and 27.50%, 22.28%, 30.52% (500 VMs) respectively. These results underscore BOHGOA's effectiveness in optimizing cloud VM placement.

The paper proposes a methodological way to compensate the imbalance between energy generation and consumption using the battery block of electric vehicles as energy reservoir through the well-known Vehicle to Grid system (V2G). The method is based on a simulation process developed by the authors that take into consideration the daily fluctuations of energy consumption as well as the power level generated by an energy source, either conventional, renewable or hybrid. The study shows that for very large electric vehicle fleet the system renders non-viable, since the remaining energy in the battery block to let the electric vehicle usable for running during daytime avoids to compensating for the energy grid imbalance, only allowing to cover a percentage of the energy imbalance, which the proposed methodology may optimize. The analysis of the proposed methodology also shows the viability of the system when being applied to a small fleet of electric vehicles, not only compensating for the energy imbalance but also preserving the required energy in the battery of the electric vehicle to make it run. The method allows predicting the optimum size of electric vehicle battery, which depends on the energy generation level, coverage factor of the energy imbalance and size of the electric vehicle fleet.

In this study, a new structure is proposed based on the body-centered cubic (BCC) lattice structure: adding a cubic truss in the center of the BCC structure, which is denoted by TLC (Truss-lattice-cube). The different dimensions of the central cube can affect the mechanical properties of the lattice structure notably. With a fixed length (15mm) of a unit cell, the optimal size for the central cube is determined to be 5 mm. Quasi-static compressive tests are performed on specimens made of Polylactic Acid (PLA) using additive manufacturing technology. The deformation characteristics of the new structure is analyzed in detail by experiments and numerical simulations. Compared to the BCC structure, the mechanical properties of the TLC structure are significantly improved. The initial flow stress of the TLC has been increased by 122% at a strain of 0.1; the specific strength has been enhanced by 293% at a strain of 0.5; and the specific energy absorption has been improved by 312% at the strain of 0.6. Printing defects in the lattice structure could deteriorate the mechanical property remarkably. In this work, incorporation of microcracks into the finite element model makes the simulation capture the influence of printing defects and improve the predictive accuracy of simulation significantly.

Renewable Energy Communities (RECs), strongly sustained by the European Community as a viable way to fasten low carbon transition, slowly develop in Italy for regulatory . This study delves into the framework of RECs and explores the perspective of the specifically small and medium industries in the Lombardy region of Italy through a survey. Our findings reveal that while SMEs exhibit interest in RECs, there is a notable gap in knowledge and engagement. We argue that the active involvement of Public Administration Bodies (PABs) is crucial in disseminating information about RECs and highlighting their benefits. The survey results, elaborated through a non-parametric test (Mann-Whitney U test) indicate that PABs play a pivotal role in influencing SMEs' perceptions and willingness to participate in RECs. The data suggest that more direct and effective communication strategies by PABs could significantly enhance SMEs' understanding and involvement in RECs. This research underscores the need for targeted and strategic efforts by PABs to foster a more informed and proactive approach among SMEs towards renewable energy initiatives. Such endeavors could be instrumental in accelerating the adoption of RECs, contributing to a more sustainable and resilient energy future.

Static voltage stability has become one of the significant risks faced by large-scale renewable energy integration. However, traditional methods for static voltage stability analysis are often overly complex. This paper constructs an equivalent impedance model for renewable energy integrated distribution power systems, proposing a static voltage analysis method for renewable energy integrated distribution power systems based on impedance model index. This method has been verified to be applicable not only to renewable energy single-infeed system but also to multi-infeed system. Furthermore, an analysis is conducted on the influence of the integration capacity, location of renewable energy, and the topology of networks on impedance model index, indicating that a higher impedance model index corresponds to greater static voltage stability margins in the system. Hence, during the planning of renewable energy integration, the plan with the highest impedance model index should be selected. Finally, the accuracy of the analysis methods and conclusions in this paper was validated based on the IEEE 14-node system.

Phosphorus is required by laying hens for a variety of biological functions. It is usually provided in excess, in a readily digestible inorganic form in diet formulations. However, this does not represent an efficient use of a finite resource and can result in water pollution. Plant bound phosphorus in phytate, is common in commercial poultry diets, and represents an alternate source of phosphorus, however poultry lacks the enzyme to digest it. By adding enzymes to their diets, the availability of this phytate-bound phosphorus can be increased allowing for the reduced inclusion of inorganic P in the diet formulation. Therefore, this study was conducted to examine the effect of a natural enzyme complex that contains phytase and xylanase in layer diets. Novogen Brown Classic layers (n=314) were fed one of two diets: (T1) control diet or (T2) control diet plus a natural mixed-enzyme product with both, phytase and xylanase. Experimental diets were provided for 12 days after which the excreta from each pen were collected for an additional three days to determine total retention and ileal digestibility of phosphorus and gross energy. At the end of the trial the birds were euthanized, and their ileal contents were collected. Productivity parameters were also recorded over the course of the study. The amount of phosphorus detected in the excreta of the control group was significantly higher than the amount detected in the enzyme-supplemented group (P<0.05). Both phosphorus retention and phosphorus ileal digestibility were significantly higher in the enzyme-supplemented birds (P<0.001). Gross energy digestibility was significantly higher in the enzyme-supplemented birds for both the total tract and ileal samples (P<0.001). There were only numerical improvements, not significant, in the layer productivity parameters. This study showed that the enzyme supplement can improve total retention and ileal digestibility of phosphorus and gross energy in older layers.

Solar photovoltaic systems (PVs) are becoming more popular in Poland, as the country aims to increase its share of renewable energy sources and reduce its dependence on coal. According to the official data the total installed PV capacity in Poland reached 15.6 GW in September 2023 while at the end of 2020 it was 4.1 GW. This situation was caused by many factors, but mainly by government subsidy programs for households and rising electricity prices. A number of photovoltaic installations with a capacity of up to 50 kWp were carried out, although the installation should always be adapted to the needs of a given household. Estimating the yield of solar energy systems is crucial for that. It can be done by several methods, but always as initial step in modelling of solar energy systems weather data, especially solar irradiation is required. To get this data we can arrange the measurement from ground stations or use reanalysis models and satellite-derived datasets. There are several different datasets, both public and commercial, of solar irradiance data ready to use. This data provides insights into the average solar irradiance levels throughout the year, aiding in estimating the energy generation capacity. The paper contributes the knowledge of energy generation capacity that can be quickly counted for a solar PV micro-system with different orientation and inclination as a multiplier of the energy value obtained from a solar PV system of 1 kW-peak for a significant number of locations in Poland based on open-source solar irradiation data

This paper addresses the Hubble tension in cosmology by exploring dynamic dark energy models, specifically focusing on quintessence scalar fields. We develop a comprehensive theoretical framework for these models, examining various potential forms and their implications for the universe's evolution. We elaborate on the effects of dynamic dark energy on cosmological perturbations, analyzing its influence on the Cosmic Microwave Background (CMB) anisotropies and the formation of large-scale structures. By integrating the theoretical models with observational data, including Type Ia supernovae, CMB observations, and large-scale structure surveys, we investigate the potential of dynamic dark energy to resolve the discrepancies in measurements of the Hubble constant. Our findings suggest that dynamic dark energy models resolve the Hubble tension and have broader implications for our understanding of cosmic evolution and the universe's fate. This study aims to contribute to the ongoing discourse in cosmology, providing a new perspective on dark energy and its role in the expanding cosmos.

The energy crisis is currently a major topic in scientific and managerial discussions in the building and construction industry. While employing low-cost strategies for reducing energy usage is beneficial for principals and decision-makers, the primary purpose of this study is to recommend the use of phase change materials (PCM) to improve cooling and heating load management in buildings. In order to accomplish this, the study applies design-builder simulations to a developing country case study. The results indicate that integrating PCM in buildings can reduce energy usage by around 6 percent. Although this value may be lower than other techniques, it is nevertheless important for reaching green building goals in both developed and developing nations. The case study, which takes place in a four-season region, demonstrates the significant potential of the PCM system for both cooling and heating energy management. In addition, the reciprocal performance improvement of PCMs is a remarkable characteristic, especially when contrasted to some conventional systems that focus on a single energy management objective in buildings. Lastly, it should be underlined that the suggested platform is consistent with Sustainable Development Goal 7 (Affordable and Clean Energy Targets).

The paper describes and analyzes the performance of a new thermoelectric generation system based on the use of Peltier cells inserted in the ducts of the air conditioning equipment. The hot and cold air streams that flow in the two separated sections of an air conditioning system are used to create a thermal gradient between the two faces of the Peltier cells. Hot and cold air flow inside the A/C unit are driven to a built in thermal chamber where the Peltier cells are placed. Peltier cells are incrusted into an insulation wall that separates the two sections of the chamber through which hot and cold air streams are flowing. The so designed configuration allows the Peltier cells to generate electric current. The study has been run for constant cold air temperature and variable hot air temperature, resulting in a variable performance of the thermoelectric generator. Theoretical study has been completed with experimental tests to determine real performance of the thermoelectric unit. Efficiency of the thermoelectric generator has been found to be linearly dependent on the temperature difference between hot and cold air subsections of the chamber. Maximum efficiency, however, is limited by the hot air stream temperature, since in real conditions ambient temperature cannot overpasses a specific value. For standard operational conditions, 5º C and 45º C for the cold and hot air streams, the thermoelectric generator operates at 44% of the maximum efficiency attainable.

A comprehensive review of uncertainties in power systems, covering modeling, impact, and mitigation, is essential to understand and manage the challenges faced by the electric grid. Uncertainties in power systems can arise from various sources and can have significant implications for grid reliability, stability, and economic efficiency. Australia, susceptible to extreme weather such as wildfires and heavy rainfall, faces vulnerabilities in its power network assets. The decentralized distribution of population centers poses economic challenges in supplying power to remote areas, a crucial consideration for emerging technologies emphasized in this paper. In addition, the evolution of modern power grids, facilitated by deploying the advanced metering infrastructure (AMI), has also brought new challenges to the system due to the risk of cyber-attacks via communication link. However, the existing literature lacks a comprehensive review and analysis of uncertainties in modern power systems, encompassing uncertainties related to weather events, cyberattacks, and asset management, as well as the advantages and limitations of various mitigation approaches. To fill this void, this review covers a broad spectrum of uncertainties considering their impacts on the power system and explores conventional robust control, and modern probabilistic and data-driven approaches for modelling and correlating the uncertainty events to the state of the grid for optimal decision making. This article also investigates the development of robust and scenario-based operations, control technologies for microgrids (MGs) and energy storage systems (ESSs), and demand-side frequency control ancillary service (D-FCAS) and reserve provision for frequency regulation to ensure a design of uncertainty-tolerance power system. This review delves into the trade-offs linked with the implementation of mitigation strategies, such as reliability, computational speed, and economic efficiency. It also explores how these strategies may influence the planning and operation of future power grids.

Atrial Fibrillation (AF) is known as one of the dominant cardiac disease that is associated with severe consequences such as risk of strokes. This cardiac arrhythmia can be efficiently diagnosed using EEG signals. However, AF episodes in EEG records are not always permanent for all subjects. In addition, lengthy subject preparation and ECG recording sessions makes this practice time consuming and not attractive for efficient diagnosis. One attractive solution for this problem will be the use of wearable Internet of Medical Things IoMT devices for EEG recording and AF detection. From another side the use machine learning for efficient diagnosis could provide high accurate diagnosis. However, the deployment of these wireless wearable devices faces the problems of limited processing capabilities and of energy scarcity. In this context, the design of energy efficient accurate on-sensor AF detection scheme using machine learning algorithms still requires a focused research effort. This paper proposes a wearable solution for ECG-based processing scheme for embedded AF detection. The proposed method achieved 100% sensitivity and 94.5% of specificity and accuracy of around 97.4 % for wide range of subjects while showing low energy-consumption. The obtained results show the efficiency of using Logistic Regression algorithm for accurate AF detection and attest about practical feasibility of the proposed approach in wearable IoMT devices.

This text mining study delves into the multifaceted contributions of the energy sector to the Sus-tainable Development Goals (SDGs). By analyzing a wide range of academic literature, we un-cover key themes, trends, and challenges shaping the intersection of energy and sustainability. The findings reveal that the energy sector plays a pivotal role in achieving SDGs such as affordable and clean energy (SDG 7) and climate action (SDG 13). Critical issues encompass governance, policy frameworks, and technological innovations. This research underscores the need for inter-disciplinary collaboration and holistic approaches in addressing complex energy-related sus-tainability challenges. The insights derived here provide guidance to policymakers, researchers, and stakeholders seeking to harness the energy sector's potential for a more sustainable and eq-uitable future.

Since the energy sector is the main source of greenhouse gases, it has the highest potential for improvement. Improvements can be achieved by generating energy from renewable sources. Combined with thermal energy storage, concentrating solar power plants represent a promising technology for dispatchable renewable energy, ensuring a stable energy supply even in remote areas without contributing to greenhouse gas emissions during operation. However, it is important to note that greenhouse gases and other polluting emissions may occur during the manufacturing process of concentrating solar power plant components. This work analyses the design of the receiver to produce thermal energy for the existing solar dish CSP plant at the Energy Center of the Politecnico di Torino. The objective of this plant is to produce hot water for a small case study. Considering the average solar irradiance for Turin equal to 800 W/m2, the surface heat flux was obtained from the first part of the analysis, which was used to obtain the maximum internal temperature in the receiver equal to 873.7 °C. The maximum temperature obtained was a constraint for the selection of the material for the solar receiver. Of the various possibilities, copper was chosen. In the second part of the work, a Life Cycle Assessment was carried out to compare the emissions generated during the production of the main components of the CSP plant with the emissions generated by the methane-fuelled water heater that produces the same amount of water as the CSP plant. It can be concluded that the manufacture of the main components of the CSP plant results in lower greenhouse gas emissions than the operational phase of a conventional natural gas-fired water heater (1.6 kg CO2/day).

Psychrophilic anaerobic digestion emerges as an appealing integrated solution for the management of agricultural waste, particularly for farmers in regions where the average temperature does not exceed 26°C, as seen in coffee cultivation. Therefore, this study seeks to assess the biomethane potential of thermochemical treated coffee husk through psychrophilic anaerobic digestion (C3-20°C-w/pretreatment). To examine its viability, outcomes were compared with reactors operating at both mesophilic (C1-35°C) and psychrophilic (C2-20°C) conditions, albeit without the use of pretreated coffee husk. The C3-20°C-w/pretreatment test demonstrated a 36.89% increase (150.47 mL CH4/g VS; 161.04 mL CH4/g COD), while the C1-35°C test exhibited a 24.03% increase (124.99 mL CH4/g VS; 133.77 mL CH4/g COD), both in comparison to the C2-20°C test (94.96 mL CH4/g VS; 101.63 mL CH4/g COD). Notably, the C3-20°C-w/pretreatment trial yielded superior outcomes, accompanied by an associated energy output of 4262.2 KWh/year, sufficient to meet the annual energy demands of 588 residences. This marks an increase of 100 and 217 residences compared to mesophilic and psychrophilic AD of CH without pretreatment, respectively.

The Semiconductor and Liquid Assisted Photothermal Effect (SLAPE)” concept was employed for the first time in this study to generate electricity from sunlight. In SLAPE process, a semiconducting material is immersed completely in a stable organic solvent to capture the complete sunlight reaching the earth surface and to turn it into the heat-energy that can be eventually converted into electricity by using Organic Rankine Cycle (ORC) with the help of a heat engine and electric generator. In this investigation, the SLAPE solar panels were fabricated with two top and bottom chambers separated by thin thermally conducting metal sheet (Cu or Al). Multi-crystalline silicon photovoltaic cells (SPVCs) in conjunction with -butyrolactone were employed to capture the sunlight, and a low-boiling point dichloromethane (DCM) solvent was employed as a working fluid to capture the in situ generated heat energy from sunlight. Upon exposure to the natural sunlight, the 21 numbers of SPVCs together with about 2 litres -butyrolactone containing one square meter area SLAPE solar panel has generated about 17 V when integrated with a laboratory model reciprocally moved steam engine and a custom-made electric generator, whereas, a commercial SPVC solar panel with 21 number SPVCs generate a maximum voltage of 11 V. The theoretical background governing the SLAPE solar panels, the necessity for their invention and the proof of concept of SLAPE solar panels have been thoroughly presented and discussed and established in this article.

The power system has rapidly grown and expanded over the past decades and has been experiencing major changes and challenges. The increase in energy demand and the modern advancements in the smart grid, such as solar and wind energies and electric vehicles, have led to complexity and complications for utilities. A further layer of complexity and difficulty was added by the rapid expansion of behind-the-meter (BTM) photovoltaic (PV) systems with various designs and characteristic features. The rapid increase and the invisible solar power (BTM) have led to fluctuations in power grid stability and reliability and to inefficiency. Accurate forecasting of load generation will help to assure optimal planning, minimize the negative effects of the PV systems, and minimize the operational and maintenance costs. The authors propose a solution that uses combinations of K-means clustering with neural network machine learning models, AMI real-world PV load generation, and weather data to forecast the generation load at customer locations to achieve a 2.49% error between actual and predicted generation load.

Building energy consumption is the main urban energy consumption component, which mainly serves people-centered work and living energy demands. Based on the physical requirements of humans in urban buildings and to determine the comfortable body temperature in each season, this paper establishes a sizing optimization model of building-type integrated energy systems (IES), where the cooling and heating loads required to maintain indoor somatosensory body comfortable temperature are calculated. Depending on the external energy price, internal power balance and other constraints, the model develops an optimal sizing and capacity panning method of energy conversion and storage unit in a building-type IES with PV generation. The operating principle is described as follows: the PV generation is fully consumed, a gas engine satisfies the electric and thermal base load requirements, while the power system and a heat pump supply the remaining loads. The gas price, peak-valley electricity price gap and heat-topower ratio of gas engines are considered as important factors for the overall techno-economic analysis. The developed method is applied to optimize the economic performance of building-type IES and verified by practical examples. The results show that using the complementary characteristics of different energy conversion units is important to the overall IES cost.

Probiotics are suggested to improve pig health, nutrient utilisation, performance, and may reduce nitrogen (N) pollution. However, the effectivity of a single-strain might be different from a multi-strain. The study was conducted to investigate the effect of a novel Bacillus multi-strain on nutrient digestibility, energy utilisation, and N retention in weaned piglets. The experiment consisted of a control diet (CD) and a supplemented diet (SD). The probiotic used for SD consisted of B. amyloliquefaciens - 516 and B. subtilis - 541. A total of 8 boars/ treatment were weaned (day 0; 8.5 kg bodyweight). Until day 10, piglets were fed ad libitum and were housed in pairs; from day 11, piglets were fed semi ad libitum and were housed individually. From day 14, faecal and urine were collected twice daily. Piglets were humanely euthanised at day 19 (15.0 kg bodyweight) after which the jejunum, ileum, and colon content were collected. In faeces the apparent total tract digestibility (ATTD) of, amongst others, DM, organic matter (OM), crude protein (CP), non-starch polysaccharides (NSP), and subsequently net energy (NE) were calculated using titanium dioxide as an indigestible marker. In jejunum and ileum, the apparent digestibility of CP was estimated and in ileum the apparent AA digestibility. In urine, N content was measured to determine N retention. The volatile fatty acid (VFA), branched-chain fatty acid (BCFA), and lactic acid content were analysed in colon and faeces. Apparent CP digestibility in jejunum and ileum were not affected by treatment (P&gt;0.05) and no effect was observed on apparent ileal digestibility of AA (P&gt;0.05). Supplementation with the multi-strain probiotic improved ATTD of DM (P=0.01; +1.3%) and OM (P=0.02; +1.2%) and tended to improved ATTD of CP (P=0.10; +2.2%) and NSP (P=0.07; +1.9%). The multi-strain probiotic also improved the NE value (P=0.02; +0.2 MJ/kg DM) and improved N retention (P=0.05; +1.6%). Supplementation did not influence VFA, BCFA, and lactic acid content in the faeces (P&gt;0.05). However, in colon, supplementation did influence lactic acid content (lower; P=0.01) and tended to influence valeric acid content (higher; P=0.09). In conclusion, results from the current study suggests that the multi-strain probiotic has the potential to contribute to improve nutrient efficiency in weaned piglets. More research needs to be done to identify the impact of the improved nutrient utilization on gut health in post-weaned pigs as well as environmental pollution.

In our investigation of the time evolution of energy states Einstein’s interpretation from more than a century ago is found to have greater internal consistency than that of non-relativistic quantum mechanics. We choose to align more closely with his methods by abandoning the non-relativistic Hamiltonian method, which has been the standard for nearly a century, in favor of a relativistically correct Lagrangian method. Integral equations of motion for the absorption and emission of radiation are derived that underlie and anticipate the differentially motivated Schrödinger equation. This new interpretation applies to a large volume of experimental evidence of both classical and quantum mechanical origin. Among the examples discussed in support are Planck’s law describing black body radiation, matrix mechanics, the function of the simplest quantum mechanical system an electron cyclotron, chaos theory, and evolutionary biology.

Energy efficiency considerations in terms of reduced power dissipation is a significant issue in the design of digital circuits for very large-scale integration (VLSI) systems. Quantum-dot cellular automata (QCA) is an emerging ultra-low power dissipation approach, distinct from the traditional complementary metal-oxide semiconductor (CMOS) technology, for building digital computing circuits. Developing fully reversible QCA circuits has the potential to significantly reduce energy dissipation. The multiplexer is a fundamental element in the construction of useful digital circuits. In this paper, a novel, multilayer, fully reversible QCA 8:1 multiplexer circuit, with ultralow energy dissipation, is introduced. The proposed multiplexer power dissipation is simulated using the QCADesigner-E version 2.2 tool, describing the microscopic physical mechanisms underlying the QCA operation. The results showed that the proposed reversible QCA 8:1 multiplexer consumes 89% less energy than the most energy-efficient 8:1 multiplexer circuit presented previously in the scientific literature.

As part of the Earth Climate Observatory space mission concept for the direct observation from space of the Earth Energy Imbalance, we propose an advanced camera suite for the high resolution observation of the Total Outgoing Radiation of the Earth. For the observation of the Reflected Solar Radiation, we propose the use of two multispectral cameras covering the range from 400 to 950 nm, with a nadir resolution of 1.7 km, combined with a high resolution RGB camera, with a nadir resolution of 0.57 km. For the observation of the Outgoing Longwave Radiation, we propose the use of 6 microbolometer cameras, with each a spectral bandwidth of 1 μm in the range from 8 to 14 μm, and with a nadir resolution of 2.2 km.

This work demonstrates that microbial fuel cells (MFCs), optimized for energy recovery, can be used as an effective tool to detect the presence of antibiotics in water-based environments. MFCs directly convert chemical energy of organic matter in electrical energy thanks to electroactive biofilms. The efficiency of the conversion process can be significantly affected by the presence of contaminants acting as toxicants for the biofilm. In the present work, we demonstrate that MFCs can successfully detect the presence of antibiotic residues in water and water-based electrolytes associated to the food industry, especially using honey as a new and more complex electron donor. The effectiveness of MFCs to sense antibiotics is here demonstrated for tetracycline that was added to both water and water/honey electrolytes with a minimum concentration close to 3.5 μg⁄kg. MFCs not only efficiently detect the presence of tetracycline in both the electrolytes, but also recover the same performance after each cycle of exposure, showing to a be very robust and reliable technology for both biosensing and energy recovery.

The article was devoted to topics related to the courier market, electromobility and renewable energy sources. This is the direction in which the car market is currently heading. The current situation of transport in Poland and the electrification trends of the courier market in Poland and beyond were presented and discussed. The article presents the conducted research and analyzes it for a selected company, which at the same time constituted the achievement of the goal and the solution of the research problem. Much attention was focused on conducting AHP analysis and simulating route changes for selected regions. The study contains an introduction and a summary discussing the most important conclusions.

This paper aims to investigate the coal thickness change area of the combined rock strata energy accumulation law. The method of connecting a uniaxial compression experiment with a theoret-ical analysis is used in this work. We employ a combination of coal and rock to carry out our studies. We investigate how different lithologies and ratios of coal-rock height affect the me-chanical properties of the mixture and the law governing energy accumulation. We have deter-mined the following facts: (1) The combination's peak strength and elastic modulus exceed that of coal and are inferior to that of rock but are nearer to that of coal. (2) When the coal-rock height ratios are dissimilar, the peak strength and modulus of elasticity of the combination show a negative correlation with the coal-thickness share, and the pre-peak energy accumulation and impact energy index of the combination shows a positive correlation with the coal-thickness share. (3) For the combination with the same coal-rock height ratio, the peak strength, elastic modulus, pre-peak energy accumulation, and impact energy index all increase with greater rock strength and elastic modulus. The presence of a hard rock layer affects the accumulation of pre-peak energy. ( 4 ) The stress in the surrounding rock gradually decreases as the coal thick-ness increases. In the area where coal thickness declines, the stress in surrounding rock is higher than that in the region where it thickens. The energy stored in the surrounding rock is directly proportional to the coal in the zone. Conversely, areas with thinner coal deposits exhibit a lower energy storage capacity in the surrounding rock than those with thicker coal deposits. The stress distribution of surrounding rock in coal thickness change is abnormal; substantial energy accu-mulation can swiftly initiate dynamic disasters, such as rock bursts. This study has important reference significance for preventing and controlling rock burst in coal thickness change areas.

The research paper provides a mathematical framework for understanding phase shift in wave phenomena, bridging theoretical foundations with real-world applications. It emphasizes the importance of phase shift in physics and engineering, particularly in fields like telecommunications and acoustics. Key equations are introduced to explain phase angle, time delay, frequency, and wavelength relationships. The study also introduces the concept of time distortion due to a 1° phase shift, crucial for precise time measurements in precision instruments. The research also addresses infinitesimal wave energy loss related to phase shift, enriching our understanding of wave behavior, and impacting scientific and engineering disciplines.

The study presents empirical results investigating the relationships among renewable and non-renewable energy consumption, CO2 emissions, and GDP within the Visegrád Group (V4) countries. Using FMOLS/DOLS and ARDL approaches, along with causality tests based on the Toda-Yamamoto method, the study explores these relationships at a regional level. The findings indicate that renewable energy has a small positive impact on long-term economic growth, with non-renewable energy having a more significant effect. Moreover, CO2 emissions have a negative impact on economic growth, suggesting ongoing reliance on non-renewable energy sources and a burden on economic expansion. On an individual country level, the effects vary. Poland, Slovakia, and Hungary exhibit a negative relationship between CO2 emissions and economic growth. Energy sources also differ in impact: in Poland, the Czech Republic, and Slovakia, non-renewable energy significantly affects economic growth, while in Hungary, renewable energy plays a more substantial role. Causality tests reveal a causal relationship between CO2 emissions and economic growth in the Czech Republic and Poland, suggesting CO2 emissions significantly influence economic expansion. In terms of energy production, renewable energy is causally related to economic growth in the Czech Republic and Slovakia. All countries demonstrate significant causality between non-renewable energy and economic growth. Additionally, a relationship between renewable energy and CO2 emissions is confirmed in Poland.

The article aims to explore social aspects in the area of energy research by using bibliometrics methods and tools. The study is based on the data of Energy Research & Social Science journal publications exported from ScienceDirect database during 2019-2023. Methodologically, the work included clustering of the bibliometric records under consideration. This allowed to identify the main topics of publications and those in which the research of social problems prevails. This stage of the work was carried out using carrot2, an engine for clustering search results. Next, the analysis of publication topics related to social issues research was carried out using VOSviewer — a software tool for visualizing scientific landscapes. The topics of the four clusters obtained at this stage were described by key terms with the maximum total link strength. For the principal ones, a definition was made on the basis of the texts of publications that most fully disclose the meaning of these terms. The 1537 publications in the journal during the period under review deal with many aspects of energy transition research, renewable energy, energy policy, energy justice and energy systems, but only 78 of them are dominated by social issues. The paper reveals the main social issues in the field of energy research: sociology of energy, socio-technical system, community energy, energy democracy, sociology of the future, energy sovereignty, socio-technical imaginary and shows the insufficient attention to this topic, especially to the investment of such research.

Due to its high power, high efficiency, low pollution, and compact size, permanent magnet synchronous motors (PMSMs) have been widely used in a variety of fields, including electric vehicles, aerospace, wind turbines, and marine devices which are used in renewable, sustainable, and environmentally friendly energy resources. However, in these practical scenarios, the motor operating conditions are complex and variable. Under high temperatures and high current conditions, PMSMs may experience demagnetization failures, not only leading to performance degradation but also inducing unexpected failures of the motors. To reduce the risk of unexpected losses caused by demagnetization faults and improve the safety and reliability of motor systems, it is necessary to apply automated monitoring of the magnet flux of the motor's permanent magnets and achieve real-time diagnosis of early demagnetization faults, ensuring the safe operation of the motor. This review article tries to summarize the current detecting methods of the automated monitoring of demagnetization faults in PMSMs. The main online monitoring technologies from both practical and academic perspectives were summarized and their benefits and challenges have been reviewed. Finally, the research trends and suggestions for future improvements are provided. This review article will not only shed light on the origins of the automated monitoring of demagnetization faults but also help to design highly effective and sustainable permanent magnet synchronous motors.

This work focuses on the research area of energy generation and harvesting for its transformation into electrical energy with the primary use of energy independent sensor systems in the power range P=10-10000W. These systems are applied in sensing quantities in the transport sector (bridge structures, transport infrastructure, and the others ). The proposed theoretical harvester models describing the transformation of motion energy into electrical energy, also provides theoretical and experimental models. Results obtained in the design and construction of a robust motion generator with primarily linear geometry system technology are presented. The expected output electrical power of the harvester is variable and is designed based on the linear motion generated by the engine- compressed air, small fuel system, etc. The fundamental design of the generator core has been continuously numerically modeled and an experimental setup has been developed to analyze specific parts and variations in order to validate the concept and achieve the most suitable parameters with the selected construction materials. A scaled down version of the model principle was tested in experiments and then the parameters and results were compared with the predicted theoretical analyses.