With the extension of the post-catch circulation time, a series of changes had taken place in the soft tissue of the live clams, resulting in the decline of its quality. This study investigated the quality changes of clams (Ruditapes philippinarum) in cold chain and was mainly focused on the rehydration process, which included gradient heating rehydration (GR) and sudden heating rehydration (SR). It was found that the GR had a better effect on the quality of clams than the SR. The GR stage clams showed a higher survival rate, glycogen content and adenylate energy charge (A.E.C) value than the SR stage clams. Conversely, the GR stage clams showed lower lactic acid content and K-value than the SR stage clams. The results indicated that the gradient heating rehydration was beneficial to the quality of clams. The transportation and rehydration strategies benefited both producers and shellfish merchants to save total cost.

Histone deacetylase (HDAC) inhibitors are class of drugs used in the cancer treatment. Here, we developed a library of 19 analogues of Vorinostat, an HDAC inhibitor used in lymphomas treatment. In Vorinostat, we replaced the hydrophobic phenyl group with various tricyclic ‘caps’ possessing a central, eight-membered, heterocyclic ring, and investigated the HDAC activity and cytotoxic effect on the cancer and normal cell lines. We found that three out of the 19 compounds, based on dibenzo[b,f]azocin-6(5H)-one, 11,12-dihydrodibenzo[b,f]azocin-6(5H)-one and benzo[b]naphtho[2,3-f][1,5]diazocine-6,14(5H,13H)-dione scaffolds, showed better HDACs inhibition than the referenced Vorinostat. In leukemic cell line MV4-11 and in lymphoma cell line – Daudi three compounds showed lower IC50 values than Vorinostat. These compounds had higher activity and selectivity against MV4-11 and Daudi cell lines than reference Vorinostat. We also observed a strong correlation between HDACs inhibition and the cytotoxic effect. Cell lines derived from solid tumors: A549 (lung carcinoma) and MCF-7 (breast adenocarcinoma) as well as reference Balb/3T3 (normal murine fibroblasts) were less susceptible to compounds tested. Developed derivatives show superior properties than Vorinostat, thus they are applicable as selective agents for leukemia and lymphoma treatment.

A series of chiral complexes of the form Ir(NHC)2(aa)(H)(X) (NHC = N-heterocyclic carbene, aa = chelated amino acid, X = halide) was synthesized by oxidative addition of -amino acids to iridium(I) bis-NHC compounds and screened for asymmetric transfer hydrogenation of ketones. Following optimization of the reaction conditions, NHC, and amino acid ligands, high enantioselectivity was achieved when employing the Ir(IMe)2(l-Pro)(H)(I) catalyst (IMe = 1,3-dimethylimidazol-2-ylidene), which asymmetrically reduces a range of acetophenone derivatives in up to 95% enantiomeric excess.

Accelerated degradation of collagen membranes (CMs) in diabetic rats is associated with increased infiltration of macrophages and blood vessels. Since pre-implantation immersion of CMs in cross-linked high molecular weight hyaluronic acid (CLHA) delays membrane degradation, we evaluated its effect on the number of macrophages and endothelial cells (ECs) within the CM. Diabetes was induced with streptozotocin in 16 rats, while 16 healthy rats served as control. CM discs were labeled with biotin, soaked in CLHA or PBS and implanted under the scalp. Fourteen days later, CMs were embedded in paraffin and the number of macrophages and ECs within the CMs was determined using antibodies against CD68 and Transglutaminase II, respectively. Diabetes increased the number of macrophages and ECs within the CMs (∼2.5-fold and 4-fold, respectively). Immersion of CMs in CLHA statistically significantly reduced the number of macrophages (p<0.0001) in diabetic rats, but not that of ECs. In the healthy group, CLHA had no significant effect on the number of either cells. Higher residual collagen area and membrane thickness in CLHA-treated CMs in diabetic animals were significantly correlated with reduced number of macrophages but not ECs. Immersion of CM in CLHA inhibits macrophage infiltration and reduces CM degradation in diabetic animals.

Fermented milk product "Laban" in Libya is one of the most a traditional fermented milk product consumed a refreshing drink, particularly in the warm season The average values of the physicochemical including titratable acidity, pH, total solids, and fat were 0.73%, 4.16, 8.12%, and 1.54% respectively. Coliform, yeast and mold counts were 21×10⁴, 39×10⁴, and 41 ×10³ cfu/ ml., respectively. Most strains of coliform bacteria were Serratia odorifera, Escherichia coli 1, E. coli 2. and Klebsiella oxytoca. The average Lactococcus, Streptococcus, Mesophilic Lactobacillus / Leuconostoc and Thermophilic Lactobacillus counts were 99 ×10⁷, 96 ×10⁷, 93 ×10⁷ and 15 ×10⁷ cfu / ml. respectively. A total of 142 lactic acid bacteria (LAB) isolates were identified to the genus level as Lactobacillus (48.59%), Lactococcus (43.66%), Streptococcus (4.93%) and Leuconostoc (2.82%). Sugar fermentation tests revealed the most frequent Lactobacillus species found to be Lactobacillus delbrueckii ssp. lactis (62.32%), followed by Lactobacillus plantarum (31.88%). Furthermore, other selected LAB isolates were identified by API 50 CH test as Lactococcus lactis ssp. lactics, Lactobacillus pentosus, Lactobacillus brevis, and Leuconostoc mesenteroides ssp. cremoris. Thus, our research documented the lactic acid bacteria strains and will provides fundamental basic and useful information for further studies of strain selection starter culture, with regard to the industrial production of fermented dairy milk products.

Quantum leaps advances in the single-molecule investigative science have been made possible over the past decades through the implication of nanopores, as versatile components on dedicated biosensors. Here, we employed the nanopore-tweezing technique to capture amino acid-functionalized, peptide-nucleic acids (PNA) with -hemolysin-based nanopores, and correlate the ensuing stochastic fluctuations of the ionic current through the nanopore with the composition and order of bases in the PNAs primary structure. We demonstrate that while the system enables detection of distinct bases on homopolymeric PNA or triplet bases on heteropolymeric strands, it also reveals rich insights into the conformational dynamics of the entrapped PNA within the nanopore, relevant for perfecting the recognition capability single-molecule sequencing.

Poly (Lactic Acid) (PLA) / Ground Tire Rubber (GTR) blends using Dicumyl peroxide (DCP) as a crosslinking agent were prepared as a route to recycle wastes rubber from the automotive industry. The GTR were exposed to grinding and exhibited mechanical damage, traduced at the rubber network scale by chains scission and/or chemical cross-links breakage. Such damage is accompanied by a decrease of 80% of the rubber chains network density of the initial tire buffing but found independent on the type of grinding (cryogenic, dry ambient) or on the GTR size (from <400 µm to <63 µm). Moreover, the finest sieved GTR contain the largest the amount of reinforcing elements (carbon black, clay) that can be advantageously used in PLA/GTR blends. The melt-blending of these finest GTR particles obtained by cryo-grinding at an amount of 15 wt.% and in presence of the crosslinking agent (DCP), resulted in an optimum improvement of the ductility, energy at break and impact strength of the PLA/GTR blends as compared to neat PLA, while maintaining its stiffness. The results were attributed to (i) the good dispersion of the fine GTR particles into the PLA matrix, (ii) the partial re-crosslinking of the GTR particles and co-crosslinking at PLA/GTR interface and (iii) the presence of reinforcing carbon black into the GTR particles and clay particles dispersed into the PLA matrix.

Chitosan is a chitin-derived fiber, extracted from the shellfish shells, a by-product of fish industry, or from fungi grown in bioreactors. In oenology, it is used for the control of Brettanomyces spp., for the prevention of ferric, copper and protein casse and for clarification. The International Organisation of Vine and Wine established the exclusive utilization of fungal chitosan to avoid the eventuality of allergic reactions. This work focuses on the differences between two chitosan categories, fungal and animal chitosan, characterizing several samples in terms of chitin content and degree of deacety-lation. In addition, different acids were used to dissolve chitosans, and their effect on viscosity and on the efficacy in wine clarification were observed. Results demonstrated that, even if fungal and animal chitosans shared similar chemical properties (deacetylation degree and chitin content), they showed different viscosity depending on the acid used to dissolve them. A significant difference was discovered on their fining properties, as animal chitosans showed a faster and greater sedimentation compared to the fungal, independently from the acid used for their dissolution. This suggests that physic-chemical differences in the molecular structure occur between the two chitosan categories and that this affect significantly their technologic (oenological) properties.

The Hippo pathway regulates a complex signalling network which mediates several biological functions including cell proliferation, organ size and apoptosis. Several scaffold proteins regulate the crosstalk of the members of the pathway with other signalling pathways and play an important role in the diverse output controlled by this pathway. In this study we have identified the scaffold protein IQGAP1 as a novel interactor of the core kinases of the Hippo pathway, MST2 and LATS1. Our results indicate that IQGAP1 scaffolds MST2 and LATS1, supresses their kinase activity, and YAP1-dependent transcription. Additionally, we show that IQGAP1 is a negative regulator of the non-canonical pro-apoptotic pathway and may enable the crosstalk between this pathway and the ERK and AKT signalling modules. Our data also show that bile acids regulate the IQGAP1-MST2-LATS1 signalling module in hepatocellular carcinoma cells which could be necessary for the inhibition of MST2-dependent apoptosis and hepatocyte transformation.

The study aimed to elucidate the optimum level of elemental sulfur, fresh cassava root (FCR), and urea and their effect on gas production, ruminal fermentation, thiocyanate concentration, and in vitro degradability. A 3×2×4 in a completely randomized design were conducted. Factor A was level of sulfur at 0%, 1%, and 2% of concentrate dry matter (DM), factor B was level of urea at 2% and 4% of concentrate DM, and factor C was level of the FCR at 0, 200, 300, and 400 mg of the total substrate. The study found that elemental sulfur, urea, and FCR had no interaction effect on the kinetics of gas, ruminal fermentation, hydrogen cyanide (HCN), and in vitro degradability. Elemental sulfur supplementation (P<0.05) significantly increased the gas produced from an insoluble fraction (b), in vitro DM degradability and either neutral detergent fiber or acid detergent fiber degradability, and propionate (C3) concentration while decreased the ruminal HCN concentration. Urea levels showed a (P<0.05) significant increase of the potential extent of gas production, ruminal NH3-N, and total volatile fatty acid (VFA). FCR supplementation (P<0.05) significantly increased the gas produced from an immediate soluble fraction (a), gas produced from insoluble fraction, gas production rate constant, total VFA, C3 concentration, and HCN while decreased ruminal pH, acetate, and butyrate concentration. It could be concluded that 2% elemental sulfur, 4% urea, and 300 mg FCR showed a greater effect on gas production, ruminal fermentation, and HCN reduction.

We investigated effects of molecular hydrogen (H2) supplementation on acid-base status, pulmonary gas exchange responses, and local muscle oxygenation during incremental exercise. Eighteen healthy, trained subjects in a randomized, double-blind, crossover design received H2-rich calcium powder (HCP) (1500 mg/day, containing 2.544 µg/day of H2) or H2-depleted placebo (1500 mg/day) for 3 consecutive days. They performed cycling incremental exercise starting at 20-watts work rate, increasing by 20 watts/2 min until exhaustion. Breath-by-breath pulmonary ventilation (VE) and CO2 output (VCO2) were measured and muscle deoxygenation (deoxy[Hb + Mb]) was determined via time-resolved-NIRS in the vastus lateralis (VL) and rectus femoris (RF). Blood gases' pH, lactate, and HCO3− concentrations were measured at rest and 120-, 200-, and 240-watt work rates. At rest, the HCP group had significantly lower VE, VCO2, and higher HCO3−, PCO2 versus placebo. During exercise, a significant pH decrease and greater HCO3− continued until 240-watts work rate in HCP. The VE was significantly lower in HCP versus placebo, but HCP did not affect the gas exchange status of VCO2 or oxygen uptake (VO2). HCP increased absolute values of deoxy[Hb + Mb] at the RF but not VL. Thus, HCP-induced hypoventilation would lead to lower pH and secondarily impaired balance between O2 delivery and utilization in the local RF during exercise, suggesting that HCP supplementation, which increases the at-rest antioxidant potential, affects the lower ventilation and pH status during incremental exercise. HPC induced a significantly lower O2 delivery/utilization ratio in the RF but not the VL, which may be because these regions possess inherently different vascular/metabolic control properties, perhaps related to fiber-type composition.

Hyperuricemia is a risk factor for gout. It has been well observed that a large proportion of hyperuricemia individuals have never had a gout attack(s), while some patients with gout can have a normuricemia. This raises a puzzle of the real role of serum uric acid (SUA) in the occurrence of gout attacks. As the molecule of uric acid has its dual effects in vivo with antioxidant property as well as being an inflammatory promoter, it has been placed in a delicate position in balancing metabolisms. Gout seems to be a multifactorial metabolic disease and its pathogenesis should not rely solely on hyperuricemia or MSU crystal. This critical review aims to unfold the mechanisms of the SUA role participating in gout development. It also discusses some key elements which are prerequisite for the formation of gout in association with the current therapeutic regime. The compilation should be helpful in precisely fighting for a cure of gout clinically and pharmaceutically.

Coffee is rich in caffeine (CF), chlorogenic acid (CGA) and phenolics. Differing types of coffee beverages and brewing procedures may result in differences in total phenolic contents (TPC) and biological activities. Inflammation and increases of platelet activation and aggregation can lead to thrombosis. We focused on determining the chemical composition, antioxidant activity and inhibitory effects on agonist-induced platelet aggregation and cyclooxygenase (COX) of coffee beverages in relation to their preparation method. We prepared instant coffee and brewed coffee beverages using drip, espresso and boiling techniques. Coffee extracts were assayed for their CF and CGA contents using HPLC, TPC using colourimetry, platelet aggregation with an aggregometer and COX activity using ELISA. The findings have shown all coffee extracts, except the decaffeinated types, contained nearly equal amounts of CF, CGA and TPC. Inhibitory effects of coffee extracts on platelet aggregation differed depending on the activation pathways induced by different agonists. All espresso, drip and boiled coffee extracts caused dose dependent inhibition of platelet aggregation induced by ADP, collagen, epinephrine, and arachidonic acid (ARA). The most marked inhibition was seen at low doses of collagen or ARA. Espresso and drip extracts inhibited collagen-induced platelet aggregation more than purified caffeine or CGA. Espresso, boiled and drip coffee extracts were also a more potent inhibitors of COX-1 and COX-2 than purified caffeine or CGA. We conclude that inhibition of platelet aggregation and COX-1 and COX-2 may contribute to anti-platelet and anti-inflammatory effects of espresso and drip coffee extracts.

Poly lactic acid (PLA) has made inroads for commercial market segment with lot many unique characteristics such as tenacity, low flame rate, moisture regain percentage, loss of ignition percentage, heat of combustion, UV resistance, Elastic recovery and higher melting point allowed it to be the fastest moving material in today's commercial market. An attempt has been made to test the feasibility and biocompatibility aspect of PLA with cement mix. The basic strength and physical test results were carried out and published in an article, to the continuation of the work, micro-structural study was conducted to evaluate the elemental characteristics. Thermo gravimetric analysis revealed that PLA either in granular form or filament will hold good for the inclusion into construction applications, provided degradation aspects are to be looked out for improvisation. From DSC it was found that PLA in filament form is the best inclusion material for construction application, however the tenacity of fibers has to checked, as currently available filaments in market does not have high tenacity value. From EDX reports, 30% inclusion of PLA as replacement for fine aggregate has constituent members as Calcium carbonate(CaCO3), Silica(SiO2) and Wollastonite (CaK) resulted in best composition among the rest. FESEM images revealed that, proper gradation in size, rough surface of PLA granular form or filament form will definitely enhance the mechanical/physical or even chemical behavior of PLA.

As symbionts of fungi and algae, lichens produce a variety of secondary products which pharmacological activities. This study aimed to investigate the anti-inflammatory activities of Heterodermia hypoleuca and its main compound, atraric acid. The results confirmed that atraric acid could regulating induced pro-inflammatory cytokine, nitric oxide, induced nitric oxide synthase and cyclooxygenase-2 expression by lipopolysaccharide (LPS)-stimulated RAW264.7 cells. Means while, atraric acid down-regulated expression of phosphorylated IκB, ERK and nuclear factor kappa B (NFκB) signaling pathway to exhibit anti-inflammatory effects in LPS-stimulated RAW264.7 cells. Based on these results, the anti-inflammatory effect of atraric acid during LPS-induced endotoxin shock in a mouse model was confirmed. In the atraric acid treated-group, cytokine production was decreased in the peritoneum and serum, and each organ damaged by LPS-stimulation was recovered. These results show that atraric acid has an anti-inflammatory effect and its molecular mechanism may be involved in the inactivation of the ERK/NFκB signaling pathway, demonstrating its value as a potential therapeutic for inflammatory diseases.

Gambogic acid, a common traditional Chinese medicine and widely distributed throughout South China, Vietnam, Cambodia, and Thailand. It is prenylated xanthone which is the significant bioactive compound of gamboge. Gambogic acid is known as a strong apoptotic inducer in cancer cells. It has been found as strong anticancer agent against various types of cancer cells lines such as breast cancer, pancreatic, and cervical cancer. It induces apoptosis, down regulates the anti-apoptotic proteins (survivin and BCL2,) and down regulates the activities of P-glycoprotein in drug sensitive human breast MCF-7 and drug-resistant MCF-7/ADR cells. Similarly, it also exerts alteration in P13K, AKT, p21, MMP-2 &-9, and phosphorylated-AKT expressions. The current review highlights the anticancer and chemo-preventive perspectives of gambogic acid and its mechanistic role against human and animal cancers.

Background and objectives: The hepatoprotective activity of vitamin E and C is evident due to their ability of modulating the antioxidant pathway. In this study, we have evaluated the effects of α-tocopherol and ascorbic acid on paracetamol induced liver damage with offsetting various levels of drug treatment following an in vivo experimental protocol on Wistar albino male rats. Materials and Methods: The level of lipid peroxidation as well as histological examination of liver tissues were observed among 50 Wistar albino male rats to evaluate hepatoprotective effect of α-tocopherol and ascorbic acid on hepatocytes. The experiment was divided into 5 groups (10 rats in each group)- Basal control group (Group-I, with propylene glycol), Paracetamol treated control group (Group –II), α-tocopherol pretreated & paracetamol treated group (Group –III), Ascorbic acid pretreated & paracetamol treated group (Group –IV) and Ascorbic acid pretreated & paracetamol treated group (Group –IV). Results: The mean (± SD) Malondialdehyde (MDA) concentration were significantly reduced in α-tocopherol pretreated and paracetamol treated group (P<0.001), Ascorbic acid pretreated and paracetamol treated group (P≤0.05) and combined α-tocopherol with ascorbic acid pretreated & paracetamol treated group (P<0.001). Statistically significant differences in histological findings of rat liver were observed in paracetamol treated control group (P<0.001), ascorbic acid pretreated and paracetamol treated group (P<0.001). The serum alanine aminotransferase (ALT) level was also significantly higher in paracetamol treated group (P<0.001), α-tocopherol pretreated plus paracetamol treated group (P≤0.05) and in ascorbic acid pretreated plus paracetamol treated group (P<0.001). Conclusion: The combined pretreatment of α-tocopherol & ascorbic acid have better hepatoprotective effects than α-tocopherol or ascorbic acid alone against paracetamol induced liver damage. The decrement of free radicals produced by vitamin E could be a better hepatoprotective antioxidant than vitamin C in paracetamol induced toxicity.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), a novel evolutionarily divergent RNA virus etiological agent of COVID-19, is responsible for present devastating pandemic respiratory illness. To explore the genomic signatures, we comprehensively analyzed 2,492 complete and/or near-complete genome sequences of SARS-CoV-2 strains reported from across the globe to the GISAID database up to 30 March 2020. Genome-wide annotations revealed 1,407 nucleotide-level mutations at different positions throughout the entire genome of SARS-CoV-2. Moreover, nucleotide deletion analysis found nine deletions throughout the genome, including in polyprotein (n=6), ORF10 (n=1) and 3´-UTR (n=2). Evidence from the systematic gene-level mutational and protein profile analyses revealed a large number of amino acid (aa) substitutions (n=722), making the viral proteins heterogeneous. Notably, residues of receptor-binding domain (RBD) having crucial interactions with angiotensin-converting enzyme 2 (ACE2), and cross-reacting neutralizing antibody were found to be conserved among the analyzed SARS-CoV-2 strains, except for replacement of Lysine with Arginine at 378 position of the cryptic epitope of a Shanghai isolate, hCoV-19/Shanghai/SH0007/2020 (EPI_ISL_416320). Our method of genome annotation is a promising tool for monitoring and tracking the epidemic, the associated genetic variants, and their implications for the development of effective control and prophylaxis strategy.

Ellagic acid, a polyphenolic compound present in fruits and berries, has recently been object of extensive research for its antioxidant activity, which might be useful for the prevention and treatment of cancer, cardiovascular pathologies, and neurodegenerative disorders. Its protective role justifies numerous attempts to include it in functional food preparations and in dietary supplements not only to limit the unpleasant collateral effects of chemotherapy. However, ellagic acid use as chemopreventive agent has been debated because of its poor bioavailability associated to low solubility, limited permeability, first pass effect, and interindividual variability in gut microbial transformations. To overcome these drawbacks, various strategies for oral administration including solid dispersions, micro-nanoparticles, inclusion complexes, self-emulsifying systems, polymorphs have been proposed. Here, we have listed an updated description of pursued micro/nanotechnological approaches focusing on the fabrication processes and the features of the obtained products, as well as on the positive results yielded by in vitro and in vivo studies in comparison to the raw material. The micro/nano-sized formulations here described might be exploited for pharmaceutical delivery of this active, as well as for the production of nutritional supplements or for the enrichment of novel foods.

A variety of myricetin derivatives bearing ferulic acid amide scaffolds were designed and synthesized. The structures of all title compounds were determined by 1 H NMR, 13 C NMR, 19 F NMR and HRMS. Preliminary bioassays suggested that some of the target compounds exhibited remarkable antiviral activities. In particular, compound 4l possessed significant protection activity against tobacco mosaic virus (TMV), with an half maximal effective concentration (EC50) value of 196.11 μg/mL, which was better than commercial agent ningnamycin (447.92 μg/mL). Meanwhile, microscale thermophoresis (MST) indicated that compound 4l have strong binding capability to tobacco mosaic virus coat protein (TMV-CP) with dissociation constant (Kd) values of 0.34 μmol/L, which was better than ningnamycin (0.52 μmol/L). These results suggest that novel myricetin derivatives bearing ferulic acid amide scaffolds may be considered as an activator for antiviral agents.

Increasing pesticide resistance in plant pathogens is major concern in agriculture production. Research on ecofriendly alternatives of chemical pesticides are more in demand in pesticide industry. In the current study, an ethyl acetate extract from the endophytic fungus Trichoderma sp. EFI 671, isolated from the stem parts of the medicinal plant Laurus sp., was screened for bioactivity against plant pathogens (Fusarium graminearum, Rhizoctonia solani, Sclerotinia sclerotiorum and Botrytis cinerea), insect pests (Spodoptera littoralis, Myzus persicae, Rhopalosiphum padi) and plant parasites (Meloidogyne javanica). The bioactive components have been characterized following a bioassay-guided isolation against M. persicae. The chemical study of this bioactive extract resulted in the isolation of 1-oleoyl-2-linoleoyl-3-palmitoylglycerol (1), eburicol (2), (24R)-stigmast-4-ene-3-one or β-sitostenone (3), ergosterol (4) and ergosterol peroxide (5). The free fatty acids present in compound 1 (oleic, linoleic and palmitic) showed strong dose-dependent aphid antifeedant effects against M. persicae. Liquid (PDB, and SDB) and solid (corn, sorghum, pearl millet and rice) growth media were tested in order to optimize the yield and bioactivity of the fungal extracts. Pearl millet and corn gave the highest extract yields. All the extracts from these solid media had strong effects against M. persicae with sorghum being the most active. Corn increased the content in linolenic, pearl millet the oleic and stearic and sorghum oleic and linolenic acids compared to rice. Their antifeedant effects correlated with linoleic /oleic acids. The phytotoxic effects of these extracts against Lolium perenne and Lactuca sativa varied with culture media, with sorghum being the least toxic.

Background and objectives: This study were designed to determine uric acid concentration and renal histopathology of M. calabura L. stem bark extract in diabetic rats and to compare the natural product of M. calabura L. stem bark extract with allopurinol. Materials and Methods: A completely randomized design was used for the experiment which consisted of 6 treatment groups, each consisting of 4 rats, as follows: 1) NR, normal rat; 2) KN, diabetic rat (negative control); 3) KP, diabetic rats given allopurinol 10 mg/kg body weight; 4) EM150, diabetic rats given the test extract 150 mg/kg body weight/day; 5) EM300, diabetic rats given the test extract 300 mg/kg body weight/day; and 6) EM450, diabetic rats given the test extract 450 mg/kg body weight/ day. Results: The results showed that M. calabura L. stem bark extract decrease (p<0.05) uric acid levels in diabetic rats and no specific damage to renal proximal tubular cells was seen. Conclusions: It was concluded that M. calabura L. stem bark extract has a potential as an antihyperuricemic in diabetic rats. The recommended does was 300 mg/kg body weight to provide a significant effect on reducing the uric acid level in diabetic rats. Our findings support the use of this plant as a treatment for gout and other inflammatory diseases.

Adsorption is an effective strategy for the removal of pollutants from the wastewater. Herein, a 2-hydroxyterephthalic acid (HTC) modified hypercrosslinked polymer (HTC-HCP) is successfully synthesized via Friedel-Crafts reactions, and used as an adsorbent for the different types of pollutants including organic contaminants and heavy metal ions from wastewater. Excellent adsorption capacities are observed for amines (aniline, p-methylaniline (p-MA), p-chloroaniline (p-CA), and p-aminobenzoic acid (p-ABA)), phenols (phenol, p-chlorophenol (4-CP) Bisphenol A (BPA), 1-Naphthol (1-NP)), dyes (rhodamine B (RhB) and methyl orange (MO)), and metal ions (Pb2+, Hg2+, and Cd2+). The resulting polymers exhibited excellent adsorption performance towards these pollutants. Especially, the removal rate of aniline is above 95% in the concentration of 2.5 mg/L in 40 min at 25 °C. The interaction mechanism has been investigated, and confirmed by FTIR and the theoretical calculation results. It is due to surface complexation and chemisorption between adsorbent and adsorbate. The polymer exhibits good performance such as high adsorption capacity, high separation efficiency, biodegradable properties, and easy regeneration, suggesting that its potential technological applications for the removal of organic compounds and heavy metal ions from actual industrial effluent.

Bauxite residue is the voluminous by-product of alumina production after Bayer process. Its high alkalinity causes disposal problems and harmful environmental impacts. However, the residue contains significant amounts of valuable elements such as rare earth elements including scandium. Greek bauxite residue contains a high amount of scandium close to its main resources. Taking into account scandium limited availability coupled with its high demand in modern technology, bauxite residue could be considered as a potential resource for scandium recovery. In this study, the optimization of scandium extraction from bauxite residue with sulfuric acid is investigated using Taguchi methodology. Based on previous studies acid molarity, leaching time, solid/liquid ratio and reaction temperature were selected as control parameters for the selective Sc recovery. Method optimization targeted the highest concentration of scandium combined with the lowest concentration of iron without taking into account applications constraints. The predicted values resulted by Taguchi methodology were affirmed by a confirmation experiment conducted at optimal conditions. Regression analysis provided the respective equations to be applied on several conditions depending on different applications.

Background: Diets based on meat products are not recommended in the case of ulcerative colitis (UC). However, some foods, as those containing high oleic acid and a low omega-6/omega-3 ratio show anti-inflammatory properties. The objective here is to test if some traditional cured meat products, as acorn-fed ham (high levels of oleic acid), may be useful for controlling inflammatory diseases as UC in animal models. Methods: 3 rat cohorts have been used: vegetable rat feed, control ham and acorn-fed ham (a traditional ham where high oleic acid concentration from acorns is storage in the muscle fat). UC was induced with DSS in drinking water ad libitum for one week. Short-chain fatty acids (SCFAs) and 16S rRNA from bacterial populations were analyzed in cecum samples. Colon samples were analyzed for histological parameters (inflammatory cell density, mucosa damages, myeloperoxidase). Results: In the acorn-fed ham cohort, a protective effect was observed with respect to UC disease activity index, inflammatory cells density, colon mucosa alterations, myeloperoxidase levels, blood total antioxidant capacity and lower levels of pro-inflammatory cytokines, in comparison with feed cohort. Both ham diets caused a reduction in Firmicutes and an increase in Actinobacteria, Bacteroidetes and Proteobacteria in comparison with rat feed diet. Also, acorn-fed ham diet induced changes in gut microbiota composition, with pronounced enrichments in anti-inflammatory bacterial genera such as Alistipes, Bacteroides, Blautia, Butyricimonas and Parabacteroides. Conclusions: In the acorn-fed ham cohort, as a result of the dietary intake of oleic acid and low intake of omega-6 fatty acids, a strong preventive effect against UC symptoms was observed, indicating a valuable effect of this traditional Mediterranean cured meat product.

The reaction mechanism of the carboxylation of K-2-naphthoxide was investigated by density functional theory calculations and spectroscopic studies. The reaction intermediates and products were confirmed by CO₂ adsorbed-FTIR and ¹H-NMR measurements. Four steps of the reaction pathway were identified: CO₂ activation, electrophilic substitution, CO₂-K complex rearrangement, and H-shift, producing 2-hydroxy-1-naphthoic acid (2,1-HNA), 2-hydroxy-3-naphthoic acid (2,3-HNA), and 2-hydroxy-6-naphthoic acid (2,6-HNA). The occurrence of CO₂-K complex rearrangement was also confirmed. These energy profiles of reaction pathways for the reaction intermediates were well consistent the experimental results on the carboxylation of K-2-naphthoxide.

Sepsis is a devastating disease that carries an enormous toll in terms of human suffering and lives lost. Over 100 novel pharmacologic agents which targeted specific molecules or pathways have failed to improve the outcome of sepsis. Preliminary data suggests that the combination of Hydrocortisone, Ascorbic Acid and Thiamine (HAT therapy) may reduce organ failure and mortality in patients with sepsis and septic shock. HAT therapy is based on the concept that a combination of readily available, safe and cheap agents which target multiple components of the host’s response to an infectious agent will synergistically restore the dysregulated immune response and thereby prevent organ failure and death. This paper reviews the rationale for HAT therapy with a focus on vitamin C.

The energy sensor AMP-activated protein kinase (AMPK) is a key player in the control of energy metabolism. AMPK regulates hepatic lipid metabolism through the phosphorylation of its well-recognized downstream target acetyl CoA carboxylase (ACC). Although AMPK activation is proposed to lower hepatic triglyceride (TG) content via the inhibition of ACC to cause inhibition of de novo lipogenesis and stimulation of fatty acid oxidation (FAO), its contribution to the inhibition of FAO in vivo has been recently questioned. We generated a mouse model of AMPK activation specifically in the liver achieved by expression of a constitutively active AMPK using adenoviral delivery. Indirect calorimetry studies revealed that liver-specific AMPK activation is sufficient to induce a reduction in the respiratory exchange ratio and an increase in FAO rates in vivo. This led to a more rapid metabolic switch from carbohydrate to lipid oxidation during the transition from fed to fasting. Finally, mice with chronic AMPK activation in the liver display high fat oxidation capacity evidenced by increased [C14]-palmitate oxidation and ketone body production leading to reduced hepatic TG content and body adiposity. Our findings suggest a role for hepatic AMPK in the remodeling of lipid metabolism between the liver and adipose tissue.

In the human organism various carotenoids are present of which, some are retinoid precursors. The bioactive derivatives of these retinoids are the retinoic acids, which can potently activate nuclear hormone receptors like the retinoic acid receptor and the retinoid X receptor. In our study using an HPLC analytical approach we aimed to assess how plasma carotenoid and retinoid concentrations along with the ratio of their isomers are altered in atopic dermatitis (AD) patients (n=20) compared to healthy volunteers (HV, n=20). We found that plasma levels of the carotenoids lutein (HV 198 ± 68 ng/ml, AD 158 ± 57 ng/ml), zeaxanthin (HV 350 ± 142 ng/ml, AD 236 ± 85) as well as the retinoids retinol (HV 216 ± 89 ng/ml, AD 167 ± 76 ng/ml) and all-trans-retinoic acid (HV 1.1 ± 0.6 ng/ml, AD 0.7 ± 0.5 ng/ml) were significantly lower in AD-patients, while lycopene, α-carotene and β-carotene levels were comparable. In addition the ratios of 13-cis vs. all-trans lycopene as well as 13-cis vs. all-trans retinoic acid were increased in the plasma of AD-patients indicating an AD-specific 13C-isomerisation. A positive correlation with SCORRAD was calculated with 13-cis vs. all-trans lycopene ratio, while a negative correlation was observed with zeaxanthin plasma levels. Based on our results we conclude that in the plasma of AD-patients various carotenoids and retinoids are at lower levels, while the ratio of lycopene isomers was also altered. The higher rate of lycopene and retinoic acid isomerisation products might be a consequence of AD or might result in an altered activation of nuclear hormone receptor signaling pathways and thus maybe partly be responsible for the AD-phenotype and additionally may represent a good plasma marker for AD.

Saturated fatty acids are implicated in the development of metabolic diseases, including obesity and type 2 diabetes. There is evidence, however, that polyunsaturated fatty acids can counteract the pathogenic effects of saturated fatty acids. To gain insight into the early molecular mechanisms by which fatty acids influence hypothalamic inflammation and insulin resistance, we performed time-course experiments in a hypothalamic cell line, using different durations of treatment with the saturated fatty acid palmitate, and the omega-3 polyunsaturated fatty acid, docosahexaenoic acid (DHA). Western blot analysis revealed that palmitate elevated the protein levels of phospho(p)AKT in a time-dependent manner. This effect seems involved in the pathogenicity of palmitate, as temporary inhibition of the PI3K/AKT pathway by selective PI3K inhibitors prevented palmitate-induced insulin resistance. Similarly to palmitate, DHA also increased levels of pAKT, but to a weaker extent. Co-administration of DHA with palmitate decreased pAKT close to the basal level after 8 h, and prevented palmitate-induced insulin resistance after 12 h. Measurement of the inflammatory markers pJNK and pNFκB-p65 revealed tonic elevation of both markers in the presence of palmitate alone. DHA alone transiently induced elevation of pJNK, returning to basal levels by 12 h treatment. Co-administration of DHA with palmitate prevented palmitate-induced inflammation after 12 h, but not at earlier time points.

An exploratory Life Cycle Assessment was carried out to provide insight into the environmental impacts of using the green seaweed Ulva spp. as a feedstock for production of bioplastic. The study focused on the production of lactic acid as a precursor of polylactic acid. The studied production process consists of (1) the cultivation of Ulva spp. in Integrated Multitrophic Aquaculture, (2) the processing of the biomass for solubilisation of sugars, (3) the fermentation of the sugars to lactic acid and (4) isolation of lactic acid from the fermentation broth. The study identifies environmental hotspots and compares the experimental seaweed production chain with conventional feedstock. The main hotspot identified is electricity consumption during seaweed cultivation. This impact of electricity consumption can be lowered by reducing energy use and sourcing renewable energy, and by improving the material efficiency in the product chain. To improve understanding of the environmental impacts of the production process, future studies can broaden the system boundaries and broaden the scope of sustainability issues included in the environmental assessment.

Carbonic Anhydrases (CAs) are a superfamily of metalloenzymes widespread in all life kingdoms, classified into seven genetically different families (α-θ). These enzymes catalyse the reversible hydration of carbonic anhydride (CO2), generating bicarbonate (HCO3-) and protons (H+). Fifteen isoforms of human CA (hCA I-XV) have been isolated, their presence being fundamental for the regulation of many physiological processes. In addition, overexpression of some isoforms has been associated with the outbreak or the progression of several diseases. For this reason, for a long time CA inhibitors (CAIs) are used in the control of glaucoma and as diuretics. Furthermore, the search for new potential CAIs for other pharmacological applications is a very active field. Amino acids constitute the smallest fundamental monomers of protein and, due to their useful bivalent chemical properties, are widely used in organic chemistry. Both proteinogenic and non-proteinogenic amino acids have been extensively used to synthesize CAIs. This article provides an overview of the different strategies that have been used to design new CAIs containing amino acids, and how these bivalent molecules influence the properties of the inhibitors.

Macrophages are cells involved in the primary response to debris derived from wear of implanted CoCr alloys. The biocompatibility of wear particles from a high carbon CoCr alloy produced under polarization in physiological hyaluronic acid (HA) solution was evaluated in J774A.1 mouse macrophages cultures. Polarization was applied to mimic the electrical interactions observed in living tissues. Wear tests were performed in a pin-on-disk tribometer integrating an electrochemical cell in phosphate buffer solution (PBS) and in PBS supplemented with 0.3% HA, physiological synovial fluid concentration, used as lubricant solution. Wear particles produced in 0.3% HA solution showed a higher biocompatibility in J774A.1 macrophages in comparison to those elicited by PBS. A considerable improvement in macrophages biocompatibility in the presence of 0.3 % of HA was further observed by the application of polarization at potentials having current densities typical of injured tissues suggesting that polarization produces an effect on the surface of the metallic material that leads to the production of wear particles that are macrophages biocompatible and less cytotoxic. The results showed the convenience to consider electric interactions together with other particles parameters, as are size and composition, to get a better understanding of the biological effects of the wear products.

Marine water pH is becoming progressively reduced in response to atmospheric CO2 elevation. Considering that marine environments support a vast global biodiversity and provide a variety of ecosystem functions and services, monitoring of the coastal and intertidal water pH assumes obvious significance. Because current monitoring approaches using meters and loggers are typically limited in application in heterogeneous environments and are financially prohibitive, we sought to evaluate an approach to acidification biomonitoring using living gastropod shells. We investigated snail populations exposed naturally to corrosive water in Brunei (Borneo, South East Asia). We show that surface erosion features of shells are generally more sensitive to acidic water exposure than other attributes (shell mass) in a study of rocky-shore snail populations (Nerita chamaeleon) exposed to greater or lesser coastal geochemical acidification (acid sulphate soil seepage, ASS), by virtue of their spatial separation. We develop a novel digital approach to measuring the surface area of shell erosion. Surficial shell erosion of a muddy-sediment estuarine snail, Umbonium vestiarium, is shown to capture variation in acidic water exposure for the timeframe of a decade. Shell dissolution in Neripteron violaceum from an extremely acidic estuarine habitat, directly influenced by ASS inflows, was high variable among individuals. In conclusion, gastropod shell dissolution potentially provides a powerful and cost-effective tool for rapidly assessing marine pH change across a range of spatial and temporal frameworks and coastal intertidal environments. We discuss caveats when interpreting gastropod shell dissolution patterns.

A simple and effective surface modification of polyester fabrics with sulfuric acid to improve the interfacial deposition of polypyrrole was presented in our work. A range of sulfuric acid concentrations were analyzed by studying water contact angle. Effect of sulfuric acid modification on the deposition of polypyrrole was investigated by sheet resistance and color depth of fabric samples. Polyester fabrics coated with polypyrrole layer were confirmed by scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), X-Ray diffraction spectra (XRD), Fourier transform infrared spectroscopy (FTIR). XPS showed that sulphur containing functional groups were obviously appeared on the polyester fiber surface after modification, which were advantageous to promote the deposition of polypyrrole onto polyester fabrics. The improved deposition increased electrical conductivity of fabric samples.

Using the techniques of computer-aided drug design, the docking of Survivin and known active small molecules was simulated and then the key amino acid residue fragment of the target protein was analyzed. It led to the discovery of active groups capable of binding to the critical sites. Through the use of the natural product, Oleanolic Acid, as a lead compound, the introduction of the active groups onto the A-ring, and the modification of the carboxyl group at the C-28 position using esterification or amidation, twenty new Oleanolic acid derivatives had been designed and synthesized.A549 and SGC-7901 cells were used to screen the antitumor activity in vitro through the standard MTT method. The compounds, II₃, III₅ and Ⅳ_4, exhibited more potent cytotoxicity than positive drugs.

Electrospinning is the versatile technique to generate large quantities of micro- or nano-fibers from a wide variety of shapes and sizes of polymer. Natural bone is a hierarchically composites with the dispersion of inorganic ceramic along organic polymer. The aim of this study, the electrospun poly (lactic acid) (PLA) mats coated with chitosan (CH) and calcium silicate (CS) powder were fabricated. The morphology, chemical composition, and surface properties of CS/CH-PLA composites were characterized by scanning electron microscopy, X-ray diffraction, and Fourier transform infrared spectroscopy. In vitro, the CS/CH-coated PLA mats increased the formation of apatite on the surface when soaking in cell cultured medium. During culture, the adhesion and proliferation of the human mesenchymal stem cells (hMSCs) cultured on CS/CH-PLA were significantly promoted relative to those on PLA. Collagen I and fibronectin levels and promoted cell adhesion were observed upon an increase in CS content. Further, compared to PLA mats without CS/CH, CS10 and CS15 mats markedly enhanced the proliferation of hMSCs as well as their osteogenesis properties, which was characterized by bone-related gene expression. Our results demonstrated that the biodegradable and electroactive CS/CH-PLA mats had potential application as an ideal candidate for bone tissue engineering. Together, findings from this study clearly demonstrated that PLLA-C2S composite scaffold may function as an ideal candidate for bone tissue engineering.

Diseases in articular cartilages have affected millions of people globally. Although the biochemical and cellular composition of articular cartilages is relatively simple, there is the limitation in self-repair ability of cartilage. Therefore, developing the strategies for cartilage repair is very important. Here, we reported a new manufacturing process of water-based polyurethane based photosensitive materials with hyaluronic acid and applied the materials for 3D printed customized cartilage scaffolds. The scaffold has high cytocompatibility and is one that closely mimics the mechanical properties of articular cartilages. It is suitable for culturing human Wharton's jelly mesenchymal stem cells (hWJMSCs) and the cells showed an excellent chondrogenic differentiation capacity. We consider that the 3D printing hybrid scaffolds may have potential in customized tissue engineering and facilitate the development of cartilage tissue engineering.

Consumption of refined foods and beverages high in sugar make the teeth susceptible to the formation of biofilm, and lead to dental caries and diseases of the oral cavity such as periodontitis and periodontal disease. The aim of the present study was to determine the ability of selected probiotics to inhibit growth and biofilm formation by the cariogenic bacterium Streptococcus mutans in vitro. We screened strains of latic acid bacteria (LAB) (n=120) from the Bioresources Collection and Research Center (BCRC), saliva of healthy adults and infant stool. The antimicrobial activity of LAB in vitro was evaluated by agar spot culture and co-culture of the S. mutans strains. We determined the effect of heating and dilution factors (2- and 4-fold) on the antagonistic activity of LAB. Antagonistic substances in the spent culture suspensions (SCS) of LAB were precipitated by extraction with ammonium sulphate and chloroform to characterise the protein and lipophilic fractions. Results of co-culturing show that the SCS of the three LAB strains (Lactobacillus pentosus 13-1, 13-4 and Lactobacillus crispatus BCRC 14618) subjected to heat treatment showed significantly high antimicrobial activity. We found that substances produced by L. pentosus 13-4 which have the potential to exhibit antimicrobial properties might be lipophilic proteins. Additionally, we infer that the mechanism of reducing biofilm formation by Lactobacillus strains is associated with sucrose-dependent cell–cell adhesion and the gtfC level of glucosyltransferases (Gtfs) in the biofilm. Native LAB strains screened in our study may be used in chewing gums and other processed foods for preventing tooth decay.

Human milk fat is a concentrated source of energy and provides essential and long chain polyunsaturated fatty acids. According to previous experiments, human milk fat is partially lost during continuous enteral nutrition. However, these experiments were done over relatively short infusion times, and a complete profile of the lost fatty acids was never measured. Whether this lost happens considering longer infusion times or if some fatty acids are lost more than others remain unknown. Pooled breast milk was infused through a feeding tube by a peristaltic pump over a period of 30 minutes and 4, 12 and 24 hours at 2 ml/hour. Adsorbed fat was extracted from the tubes, and the fatty acid composition was analyzed by Gas chromatography-mass spectrometry. Total fat loss (average fatty acid loss) after 24 hours was 0.6 ± 0.1%. Short-medium chain (0.7%, p=0.15), long chain (0.6%, p=0.56) saturated (0.7%, p=0.4), monounsaturated (0.5%, p=0.15), polyunsaturated fatty (0.7%, p=0.15), linoleic (0.7%, p=0.25), and docosahexaenoic acids (0.6%, p=0.56) were not selectively adsorbed to the tube. However, very long chain fatty (0.9%, p=0.04), alpha-linolenic (1.6%, p=0.02) and arachidonic acids (1%, p=0.02) were selectively adsorbed and therefore lost in a greater proportion than other fatty acids. In all cases, the magnitude of the loss was clinically low.

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.

Global climate change primarily affects the spatiotemporal variation of physical quantities such as relative humidity, atmospheric pressure, ambient temperature, and notably, precipitation levels. Accurate precipitation predictions remain elusive, necessitating tools for detailed spatiotemporal analysis to better understand climate impacts on the environment, agriculture, and society. This study compares three learning models: ARIMA (Autoregressive Integrated Moving Average), RF-R (Random Forest Regression), and LSTM-NN (Long Short-Term Memory Neural Network), utilizing monthly precipitation data (in millimeters) from 757 locations in Boyacá, Colombia. The LSTM-NN model outperformed others, precisely replicating precipitation observations in both training and testing datasets, significantly reducing the root mean square error (RMSE) with average monthly deviations of approximately 19 mm per location. Evaluation metrics (RMSE, MAE, R², MSE) underscore the LSTM model’s robustness and accuracy in capturing precipitation patterns. Consequently, the LSTM model was chosen to predict precipitation over a 16-month period starting from August 2023, offering a reliable tool for future meteorological forecasting and planning in the region.

This study presents the design and comprehensive 3D multiphysics simulation of a novel microfluidic immunosensor for real-time, non-invasive monitoring of pro-inflammatory biomarkers in human sweat. The patch-like device, designed using COMSOL Multiphysics, integrates magnetofluidic manipulation with direct-field capacitive sensing. The sensor comprises two distinct units: an immunocomplex enhancement unit, employing a series of microcoils to optimize the binding efficiency, reaction kinetics, and homogeneity of biomarker-magnetic nanoparticle (MNP) interactions, thereby enhancing the sensor's specificity; and a layered capacitive sensing unit, designed to concentrate and detect biomarker-tagged MNPs, thus amplifying sensitivity. Simulations of the capacitive sensing unit revealed a substantial sensitivity increase of up to 42.48\% at an 85\% MNP concentration within the detection zone. These findings highlight the potential of the proposed immunosensor for efficient and precise real-time biomarker monitoring, which may facilitate early disease diagnosis and enable personalized healthcare interventions.

This study investigates the spatial urban growth patterns of cities along the Democratic Republic of Congo (DRC) and Zambia border, a region of significant economic importance characterized by cross-border trade. This activity has led to rapid, but unplanned urban growth. The objective is to quantify the spatial expansion of Congolese cities (Kipushi, Kasumbalesa, Mokambo, and Sakania) bordering Zambia and to evaluate associated landscape changes. The methodology integrates Landsat images from 1990, 2000, 2010, and 2023, coupled with Geographic Information Systems (GIS) and landscape ecology analysis tools. The results reveal substantial landscape transformations in the border cities between 1990 and 2023. These changes are primarily driven by rapid urban expansion, particularly pronounced in Kasumbalesa. Between 1990 and 2023, forest cover declined from 70% to less than 15% in Kipushi, from 80% to 10% in Kasumbalesa, from 90% to 30% in Mokambo, and from 80% to 15% in Sakania. This forest cover loss is accompanied by an increase in landscape element diversity, as indicated by the Shannon diversity index, except in Kipushi, suggesting a transition towards more heterogeneous landscapes. In these border cities, landscape dynamics are also characterized by the expansion of agriculture and savannas, highlighted by an increase in the disturbance index. Spatial pattern changes analysis shows that built-up areas, agriculture, and savannas exhibit trends of patch creation or aggregation, while forests are undergoing processes of dissection and patch attrition. Congolese cities bordering Zambia are undergoing substantial spatial changes propelled by intricate interactions between economic, demographic, and infrastructural factors. Our results highlight the necessity for sustainable development strategies to address challenges such as urban sprawl, deforestation, and fostering cross-border cooperation in border regions.

Unmanned Aerial Vehicles (UAVs) equipped with hyperspectral imagers have emerged as an essential technology for the characterization of inland water bodies. The high spectral and spatial resolutions of these systems enable the retrieval of a plethora of optically-active water quality parameters via band ratio algorithms and machine learning methods. However, fitting and validating these models requires access to sufficient quantities of in situ reference data which are time-consuming and expensive to obtain. In this study, we demonstrate how the Generative Topographic Mapping (GTM), a Bayesian realization of the Self-organizing Map, can be used to visualize high-dimensional hyperspectral imagery and extract spectral signatures corresponding to unique endmembers present in the water. Using data collected across a North Texas pond, we first apply the GTM to visualize the distribution of captured reflectance spectra revealing small-scale spatial variability of water composition. Next, we demonstrate how the nodes of the fitted GTM can be interpreted as unique spectral endmembers. Using extracted endmembers together with the normalized spectral similarity score, we are able to efficiently map the abundance of near shore algae as well as the evolution of a rhodamine tracer dye used to simulate water contamination by a localized source.

This study aims to provide analyses of the levels of airborne particulate matter (PM) using a two-pronged approach that combines data from in situ Internet of Things (IoT) sensor networks with remotely sensed aerosol optical depth (AOD). Our approach involved setting up a network of custom-designed PM sensors that could be powered by the electrical grid or solar panels. These sensors were strategically placed throughout densely populated areas of North Texas to collect data on PM levels, weather conditions, and other gases from September 2021 to June 2023. The collected data was then used to create models that predict PM concentrations in different size categories, demonstrating high accuracy with correlation coefficients greater than 0.9. This highlights the importance of collecting hyperlocal data with precise geographic and temporal alignment for PM analysis. Furthermore, we expanded our analysis to a national scale by developing machine learning models that estimate hourly PM2.5 levels throughout the continental United States. These models used high-resolution data from the Geostationary Operational Environmental Satellites (GOES-16) Aerosol Optical Depth (AOD) dataset, along with meteorological data from the European Center for Medium-Range Weather Forecasting (ECMWF), AOD reanalysis, and air pollutant information from the MERRA-2 database, covering the period from January 2020 to June 2023. Our models were refined using ground truth data from our IoT sensor network, the OpenAQ network, and the National Environmental Protection Agency (EPA) network, enhancing the accuracy of our remote sensing PM estimates. The findings demonstrate that the combination of AOD data with meteorological analyses and additional data sets can effectively model PM2.5 concentrations, achieving a significant correlation coefficient of 0.849. The reconstructed PM2.5 surfaces created in this study are invaluable for monitoring pollution events and performing detailed PM2.5 analyzes. These results were further validated through real-world observations from two in situ MINTS sensors located in Joppa (South Dallas) and Austin, confirming the effectiveness of our comprehensive approach to PM analysis. The US Environmental Protection Agency (EPA) recently updated the national standard for PM2.5 to 9 μg/m3, a move aimed at significantly reducing air pollution and protecting public health by lowering the allowable concentration of harmful fine particles in the air. Using our analysis approach to reconstruct the fine-time resolution PM2.5 distribution across the entire United States for our study period, we found that the entire nation encountered PM2.5 levels that exceeded 9 μg/m3 for more than 20% of the time of our analysis period, with the eastern United States and California experiencing concentrations exceeding 9 μg/m3 for over 50% of the time, highlighting the importance of regulatory efforts to maintain annual PM2.5 concentrations below 9 μg/m3.

Within environmental perception, automatic navigation and object detection, computer vision is a crucial and demanding field with many applications in modern industries, such as multi-target long-term visual tracking in automated production, defect detection, and driverless robotic vehicles with sensing ability. The performance of computer vision has greatly improved recently thanks to developments in deep learning algorithms and hardware computing capabilities, which has spawned the creation of a large number of related applications. This paper presents the results of a detailed review of over 50 papers published over the course of two decades (1999–2024), with a primary focus on the technical advancement of computer vision. To elucidate the foundational principles, an examination of typical visual algorithms based on traditional correlation filtering was initially conducted. Subsequently, a comprehensive overview of the most recent advancements in deep learning-based computer vision techniques was compiled. Furthermore, a comparative analysis between conventional and novel algorithms was undertaken to discuss the future trends and directions of computer vision. Lastly, the feasibility of employing visual SLAM (Simultaneous Localization and Mapping) algorithms in the context of autonomous vehicles with sensing capability was explored. Furthermore, we explored a thorough and efficient architecture for utilizing these techniques in autonomous robotic vehicles into unmanned green and low-carbon smart factories, underlining novel advances as well as potential prospects for future research.

Unmanned aerial vehicles (UAVs), commonly known as drones, present a cost-effective solution for the swift collection of data from vast and remote areas that are otherwise difficult to access. The Mediterranean Basin, known for being a hot spot for plant biodiversity, hosts several habitats and taxa of significant naturalistic value. However, many of these areas are often inaccessible to botanists, making exploration and research challenging. The aim of this paper is to involve the utilization of drone surveys and open-source software for botanical research. Our primary goal is to show the effectiveness of these tools in the field and demonstrate their practical application in Natura 2000 sites. The protected area chosen for this research is Rocca di Novara, situated in northeastern Sicily. Thanks to our drone investigations, we were able to capture images of the mountain’s side that is inaccessible to humans. This allowed us to observe the habitat of some species in detail. One of the most fascinating discoveries was the reappearance of Saxifraga callosa subsp. australis, which had not been confirmed in this area for over 140 years. Using drones for botanical research can boost field research, making monitoring easier and more cost-effective over time, especially in Natura 2000 sites.

We present the use of interconnected optical mesh networks for early earthquake detection and localization, exploiting existing terrestrial fiber infrastructure. Employing a Waveplate model, we integrate real ground displacement data from seven earthquakes, magnitudes ranging from four to six, to simulate the strains within fiber cables and collect large set of light’s polarization evolution data. These simulations help to enhance a Machine-Learning model that is trained and validated to detect Primary waves arrivals that precede earthquakes’ destructive Surface waves. The validation results show that the model achieves over 95% accuracy. The Machine-Learning model is then tested against an M4.3 earthquake, exploiting three interconnected mesh networks as a smart sensing grid. Each network is equipped with a sensing fiber placed to correspond with three distinct seismic stations. The objective is to confirm earthquake detection across the interconnected networks, localize the epicenter coordinates by a triangulation method and calculate fibers to epicenter distance. This setup allows early warning generation to municipalities close to epicenter location, and progressing to those further away. The model testing shows 98% of accuracy in detecting primary wave and one-second of detection time, affording nearby areas 21-seconds to take countermeasures, and an extended 57-seconds in more distant areas.

Previous studies separately addressed the impact of urban expansion on vegetation cover and thermal conditions, as well as its effect on moisture and drought indicators, using geospatial techniques. Some studies, such as those by Malah et al. (2021) and Niu and Li (2020), focused on assessing urban environmental quality using an environmental quality index without considering urban expansion. Meanwhile, other studies, like those conducted by Indrawati et al. (2020), primarily focused on evaluating urban environ-ments using an integrated environmental index consisting of four indicators (greenness, moisture, drought, and cumulative clustering). The study by Zhu et al. (2019) applied the same integrated environmental index (IEI) and, by comparing it with temperature, re-vealed a negative correlation illustrating the effects of the environmental index on land surface temperature (LST). These two studies could be utilized to construct an index assessing the environmental condition in Riyadh. This is the case in the present study, where the addition of a temperature indicator due to the hot desert nature of the study area created the Urban Environmental Thermal Index to evaluate the urban en-vironment and provide results suitable to this region. It is expected that the present study will align with the findings of previous relevant studies, showing a negative im-pact of urban expansion on urban environmental components.

Wetland ecosystems in the Qinghai-Tibet Plateau are pivotal for global ecology and regional sustainability, contributing significantly to terrestrial ecosystems by regulating runoff, mitigating floods, and enhancing water quality. This study investigates the dynamic changes in wetland ecosystems within the Chaidamu Basin and their response to drought, aiming to foster sustainable wetland utilization in the Qinghai-Tibet Plateau. Using Landsat TM/ETM/OLI data on the Google Earth Engine platform, we employed a random forest method for annual long-term land cover classification. Meteorological drought conditions were assessed using SPEI3, SPEI6, SPEI9, and SPEI12, derived from monthly precipitation and evapotranspiration data. Pearson correlation analysis examined the relationship between wetland changes and various SPEI scales. The BFASAT method evaluated the impact of SPEI12 trends on wetlands, while cross-wavelet analysis explored teleconnections between SPEI12 and atmospheric circulation factors. Our findings revealed that the land cover dataset of the Chaidamu Basin (1990-2020) exhibited diverse categories with high classification accuracy (OA: 90.27%, Kappa: 88.34%). Wetlands, including lake, glacier, and marsh types, exhibited a noticeable increasing trend. Wetland expansion occurred during specific periods (1990-1997, 1998-2007, 2008-2020), featuring extensive conversions between wetland and other types, notably from other types to wetlands. Spatially, lake and marsh wetlands predominated in the low-latitude basin, while glacier wetlands were situated at higher altitudes. The study identified significant negative correlations between SPEI at various scales and total wetland area and types, with SPEI12 exhibiting the most substantial effect between September and December (r <-0.75) on wetlands. SPEI12 displayed a decreasing trend with non-stationarity and distinct breakpoints in 1996, 2002, and 2011, indicating heightened drought severity. Atmospheric circulation indices (ENSO, NAO, PDO, AO, WP) exhibited varying resonance with SPEI12, with NAO, PDO, AO, and WP demonstrating longer resonance times and pronounced responses.The continuous growth of wetlands amidst increasing aridification emphasizes the need for thoughtful wetland development to establish a sustainable "forest-lake-grass-field-river" ecological community. These findings underscore the significance of comprehending wetland changes and drought dynamics for effective ecological management in the Chaidamu Basin of the Qinghai-Tibet Plateau.

Rapid and accurate detection of road cracks is of great significance for road health monitoring, but currently this work is mainly completed through manual site surveys. Low-altitude UAV remote sensing can obtain images with centimeter or even subcentimeter ground resolution, which provides a new efficient and economical approach for rapid crack detection. Nevertheless, crack detection networks face challenges such as edge blurring and misidentification due to the heterogeneity of road cracks and the complexity of the background. To address these issues, we propose a real-time edge reconstruction crack detection network (ERNet) which adopts multi-level information aggregation to reconstruct crack edges and improve the accuracy of segmentation between the target and background. To capture global dependencies across spatial and channel levels, we propose an efficient bilateral decomposed convolutional attention module (BDAM) that combines depth separable convolution and dilated convolution to capture global dependencies across spatial and channel levels. To enhance the accuracy of crack detection, we use a coordinate-based fusion module that integrates spatial, semantic, and edge reconstruction information. In addition, we propose an automatic measurement of crack information for extracting the crack trunk and its corresponding length and width. Experimental results demonstrate that our network achieves the best balance between accuracy and inference speed.

This study examines the capacity of a Site-Specific Fodder Management Decision Support System (SSFM-DSS) model to assist in successful cultivation of sericea lespedeza [SL; Lespedeza cuneata (Dum.-Cours.) G. Don.], with a specific focus on small-scale agricultural systems in the southeastern United States (U.S.). The study emphasizes incorporating advanced geospatial technologies, such as Geographic Information Systems (GIS), remote sensing, and Global Navigation Satellite Systems (GNSS), to improve fodder production. The research showcases the versatility of SL under varying environmental conditions, underscoring its significance in promoting sustainable livestock production. The methodology integrates empirical field data, geospatial analysis, and predictive modeling to formulate an SSFM strategy customized to address the distinct difficulties climate change presents, such as sudden and severe drought conditions. The study introduces an automated geospatial model for assessing the appropriateness of SL production throughout Alabama, Georgia, and South Carolina. The model uses many environmental criteria, including soil properties, climate fluctuations, and terrain. Furthermore, the research was instrumental in development of a WebGIS Dashboard, which offers farmers a decision assistance system to enhance the sustainable production of SL. The results highlight the significant impact that a SSFM-DSS can have on promoting sustainable agricultural practices, providing a solution to address the issues of food security and environmental concerns in agriculture.

Among the various types of gas sensors, the chemoresistive gas sensors based on metal oxides are considered the most attractive ones due to their simple operation, low costs and miniaturization. Thin films of V2O5 undoped and doped with TiO2 were studied as nitrogen dioxide, methane and hydrogen sensors. The experimental kinetics of the electrical conductivity changes due to introduction and removal of gas on metal oxide gas sensors were successfully explained in terms of the Langmuir adsorption theory.

The electronic nose (e-nose) is an innovative device that mimics the human olfactory system, allowing for the detection and identification of numerous scents. This abstract gives an in-depth examination of the current status of electronic nose technologies, with an emphasis on sensing mechanisms, applications, and recent breakthroughs. The research delves into the many uses of e-nose, such as food quality control, environmental monitoring, medical diagnostics, and industrial operations. It also covers the problems and potential future applications of electronic nose technology. The literature review highlights the importance of this technology in improving our capacity to evaluate complex scents in several domains.

The Internet of Things (also known as IoT), a revolution, has enhanced the relevance of mobile employees' job efficiency in mobile crowd sensing (MCS). Incentives based on reputation for mobile workers (MW) play an important role in increasing service utilization, motivating mobile employees, and developing confidence in the service. The cell phone is the most exploited entity in mobile crowd-sensing MCS and cloud computing. In the underlying investigation, we observed that they had not evaluated the complexity level of a task, resulting in a strong reputation for executing multiple basic tasks. A person who completes a tough task may have a lower reputation score. Complexity levels of tasks (CLT) were developed for reputation evaluation on a crowd-sensing network, which would be utilized to evaluate the MW reputation. We offered simple and complex levels of challenges to assess reputation scores. Tasks receive incentives based on their reputation. By measuring reputation on (CLT) complexity level of task, this research will help the system to Maintain the reputation of the mobile worker so that entities of the system get the maximum benefit out of it, by hiring well-reputed mobile workers and MW receive an incentive on it. Furthermore, we conduct a comparative analysis between our scheme and various machine learning algorithms to identify the algorithm that best performs in evaluating task complexity. Considering both the simple and complex reputation scores, the comparison suggests that after analyzing the data, it is clear that the CLT (proposed) scheme and the Linear regression scheme not only outperformed the Neural Network scheme in terms of Complex Reputation Scores but also in terms of Simple Reputation Scores. We also apply statistical tests, such as p-tests and t-tests, to determine the significance of the results obtained from different algorithms. Considering MW 308, MW 345, and MW 1045's efforts and abilities, the MCS system may offer incentives and awards to further motivate and consider their continuing contributions. This reputation-based incentivization technique not only encourages strong competition among mobile workers, but also makes sure the delivery of high-quality services inside the MCS system.

The study aims to evaluate the Quorum Sensing (QS) system inhibition against some Gram-positive and Gram-negative bacteria detected by molecular modeling of R. cathartica L. plant extract. R. cathartica L. methanol extract was evaluated in vitro. The research was carried out for the possible role and mechanism of R. cathartica L. The antibacterial activity of R. cathartica L. seed extract on some Gram-positive and Gram-negative strains was evaluated using culture-based and computational analysis. Gentamicin (CN; 30 µg/ml, Bioanalyse) was used as an antibacterial control of the extract on the production potential of QS-dependent virulence factors on Pseudomonas aeruginosa PAO1 was investigated. Phytochemical analysis resulted in the identification of 23 phenolic phytochemicals. R. cathartica L. extract was observed to have antimicrobial activity against Gram-positive and Gram-negative bacteria at different rates (11.3 mm-16 mm). The inhibitory effect of R. cathartica L. extract on P. aeruginosa PAO1 (elastase 61%, pyocyanin 18%, and biofilm 61%) was recorded moderately. Phytochemical analysis revealed that the main component of the extract is kaempferol. Therefore, the binding potential of kaempferol on QS system receptors was investigated via molecular docking. Computational analysis showed that kaempferol can inhibit the QS system by preventing competitive ligands from connecting to LasR. This study explored the activity and mechanism of action for a new green alternative against resistance. might provide a mechanism for R. cathartica L. seed extract components and thus provide a new avenue for using R. cathartica L. seed extract as a green antibacterial agent.

Remote Sensing and Geographic Information System are recognized as crucial instruments for conducting geographical and geospatial research. RS and GIS technology was employed to assess the morphological Characteristics of the Kasura River Watershed situated within the Parbhani and Jalna Districts of Maharashtra, India. The entire drainage area of the Kasura River basin measures 490.13 square kilometers (km²). Various morphometric characteristic were calculated and examined, encompassing linear features such as stream number, stream order, mean stream length, stream length, and stream length ratio, areal attributes including elongation ratio, circularity ratio, form factor, constant of channel maintenance, drainage density and drainage texture and relief characteristics such as relief, relief ratio, maximum relief, ruggedness number, relative relief, and length of overland flow. These findings underscore the significance of employing morphometric analysis in watershed assessment, offering a quantitative method for characterizing the surface attributes of a drainage pattern and yielding valuable insights into the topography and runoff dynamics of the region.

Global climate change has induced a rise in sea surface temperature (SST), although this increase is not uniform across the world. Significant variations exist between coastal and offshore waters, particularly in regions affected by upwelling processes. This study focuses on the Canary Current Upwelling System (CCUS), stretching from Northwest Iberia to Northwest Africa. High-resolution remotely sensed SST data (0.05°) from the ODYSSEA Level 4 Sea Surface Temperature Reprocessed dataset were validated with in-situ measurements and employed to establish a regional climatological baseline for 1982 – 2012. Subsequent years were compared to this baseline to construct SST anomaly maps, revealing SST changes since 2012. The study area was further divided into sub-regions for comparative analysis. Results indicate that SST consistently increased at a higher rate offshore compared to the adjacent nearshore regions. A reference dataset spanning 1951 – 1981 was used to gauge SST variability between the two baselines. SST exhibited a 0.59°C increase from 1951 – 1981 to 1982 – 2012, with a slowing of SST trends beyond the 1982 – 2012 baseline. This research offers valuable insights into the climatological dynamics of the CCUS. These findings enhance our understanding of this critical coastal system's climatology, laying the groundwork for future investigations into evolving climate patterns in coastal regions.

For the territory of the Republic of Tatarstan (Russia), a spatial model of forest communities was built as a basis for assessing the sequestration potential of ecosystems. Combination of multi-temporal and multi-spectral satellite imagery from the Landsat 8 and Landsat 9 platform as input data and Google Earth Engine cloud platform were used. The set of 292 vegetation indices and metrics computed from the pre-processed imagery, were combined into dataset. The Weka X-Means clustering algorithm was trained and applied to study area. The unsupervised classification was carried out by vegetation classes in the Braun-Blanquet system. The results of unsupervised classification were verified using data from more than 17,000 relevés with geographic references from the Flora database. For automatic classification, the EuroVeg Checklist expert system in the JUICE 7.1 package was used. The proposed methodology for obtaining initial data and unsupervised classification, supported by an automated expert system, made it possible to obtain a picture of the vegetation distribution in the study area with sufficient accuracy, and in the future, it will be used to assess the sequestration potential of the ecosystems of the region under study.

Floods are some of the most devastating crop disasters in Southeast Asia. The Pampanga River Basin in the Philippines is a representative flood-prone area, where cultivation patterns are varied according to the flood risk. However, quantitative analyses on the effects of flooding on cultivation patterns remain quite limited. Accordingly, this study analyzes MODIS LAI data (MCD15A2H) from 2007 to 2022 to evaluate annual and geographical differences in cultivation patterns in the Candaba municipality of the basin. The analysis consists of two stages of hierarchical clustering: a first stage for area classification and a second stage for the classification of annual LAI dynamics. As a result, Candaba is divided into four areas, which are found to be partly consistent with the observed flood risk. Subsequently, the annual LAI dynamics in each area are divided into two or three clusters. The obvious differences among the clusters are caused by flooding in the late rainy season, delaying the start of planting in the dry season. The clusters also indicate that the cultivation patterns slightly changed over the 16 years of the study period. The results of this study suggest that the two-stage clustering approach provides an effective tool for the analysis of MODIS LAI data when considering cultivation patterns characterized by annual and geographical differences.

Today, satellite imagery has made a major contribution to the reconstruction and spatial prediction of sensitive or complex areas such as Pendjari and W Transboundary Reserves which constitute biodiversity reservoirs and habitats for wildlife conservation. Despite the protection to which they are subject, they remain dependent on climatic hazards that can influence their stability. This study has applied remote sensing techniques, combined with climate records from the last thirty years, to analyze the past dynamics of land use and climate changes, to predict the future states of the vegetation cover of the two national parks in Benin, as well as their periphery. The methodology used remote sensing and GIS techniques that allowed the supervised classification of Landsat images of 1985, 2000 and 2015. Climatic data were combined in R software to identify the break periods for climatic parameters. Finally, the predictive vegetation cover for the year 2030 was made by combining vegetation and climate in the "Land Change Modeler" extension. Ten land use and land cover classes were obtained. These are the agglomerations, mosaics of fields and fallows, water bodies, dense forests, gallery forests, clear forests and wooded savannahs, swamp forests and shrubby wooded savannahs, saxicolous savannahs and bare ground. The natural vegetation decreased from 90.85% in 1985 to 83.54% in 2000 then to 79.56% in 2015, a decline of 11.39% over a 30-year period. The analysis of the climatic curves revealed the presence of break, meaning drought frequency. The predictive modeling revealed that land use units projected to 2030 are consistent with past trends, but with the continued expansion of fields and fallows (2%) at the expense of vegetation cover. This study will motivate many others in the field.

Detecting rotational objects in remote sensing imagery is a significant challenge. These images typically encompass a broad field of view, featuring diverse and intricate backgrounds, with ground objects of various sizes densely scattered. As a result, identifying objects of interest within these images is a daunting task. While the integration of Convolutional Neural Networks (CNN) and Transformer networks leads to some advancements in rotational object detection, there is still room for improvement, particularly in enhancing the extraction and utilization of information related to smaller objects. To address this, our paper presents a multi-scale feature fusion module and a global feature context aggregation module. Initially, we fuse original, shallow, and deep features to reduce the loss of shallow feature information, thereby improving the detection performance of small objects in complex backgrounds. Subsequently, we compute the correlation of contextual information within feature maps to extract valuable insights. We name the newly proposed model the "Multiscale Feature Context Aggregation Module" (MFCA). We evaluate our proposed methodology on three challenging remote sensing datasets: DIOR-R, HRSC, and MAR20. Comprehensive experimental results show that our approach surpasses baseline models by 2.07\% mAP, 1.02\% mAP, and 1.98\% mAP on the DIOR-R, HRSC2016, and MAR20 datasets, respectively.

In Saudi Arabia’s mountainous regions, debris flow is a natural hazard that poses a threat to in-frastructure and human lives. To assess the potential zones of landslide in the Al-Hada Road ar-ea, a study was conducted using Geographic Information System (GIS) analysis and remote sensing (RS) data. The study took into account various factors that could affect landslide, such as drainage density, elevation, slope, precipitation, land use, geology, soil, and aspect. The study also included a field trip to identify 11 previous landslide events that were influenced by high-density drainage and slope. The study utilized weighted overlay analysis in a GIS environment to create a map indicating the potential landslide zones in the area. According to the analysis, low-risk areas cover 35,354,062.5 square meters, medium-risk areas cover 60,646,250 square meters, and high-risk zones cover an area of 8,633,281 square meters. This result was verified using the locations of previous landslide events. The study’s findings can help planners and decision-makers identify and prioritize areas for mitigation and prevention measures. Additionally, the study contributes to understanding landslide hazards in arid and semi-arid regions.

Remote sensing (RS) as a landscape planning and management tool for socio-cultural diversity and inclusiveness in protected(reserved) areas through monitoring of the land-use changes, settlement/living patterns, predominant trades/socio-economic activities in the urban-rural locations, and the demography in Sub-Saharan Africa (SSA). The eighteen federal governments recognized nature reserves in Nigeria being the giant of Africa because of their green landscapes, rich biodiversity, and human resources. This study aimed to explore the application of remote sensing to assess the extent of land degradation and encroachment in the 18 recognized national parks in Nigeria. A review of the protection of national parks using remote sensing and monitoring the trend of national park invasions which could either be natural or man-made. The study is in line with the United Nations SDG Goal 15: Life on Land- “Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss”. Located in the West Africa subregion lying on Latitude: 9° 04' 39.90" N (9.077899) and Longitude: 8° 40' 38.84" E (8.677599), with an estimated population of over 220,000,000 people. This study through an intensive review of various literature adopted a mixed approach for the analysis and assessment of the geospatial data, the updated aerial photographs obtained through the Landsat imageries (Google Earth Pro, 2023), and the use of GIS for Geospatial analysis which enables the collection and analysis of spatial and geographic data gathering land use/land covers (LULC) maps, soil type, and elevation maps. While exploring the geophysical ecology and biodiversity conservation of some selected national parks in Nigeria, while also providing cost-effective alternatives for biodiversity monitoring and conservation strategy development. A review of national implementation strategies on biodiversity conservation with the deployment of remote sensing technologies, it is now feasible to obtain large details of the surface of the planet without conducting arduous field activities with the assistance of the availability of multi-date, multi-resolution, multi-sensor aerial information to help prevent for encroachment, loss, and degradation of the natural landscapes.

The current deformation and stable state of slopes with historical shatter signs is a concern for engineering construction. Suspected landslide scarps were discovered at the rear edge of the slope of the Genie in the Sichuan-Tibet transportation corridor during a field investigation. In order to qualitatively determine the current status of the surface deformation of this slope, this paper uses high-resolution optical remote sensing, airborne LiDAR and InSAR technologies for comprehensive analysis. The interpretation of high-resolution optical and airborne LiDAR data revealed that the rear edge of the slope exhibits three levels of scarps. However, no deformation was detected with the D-InSAR analysis of ALOS-1 radar images from 2007 to 2008 or with the Stacking-InSAR and SBAS-InSAR processing of Sentinel-1A radar images from 2017 to 2020. A geological model of the slope was established in combination with field investigation stipulating that the slope is composed of steep anti-dip layered dolomite limestone and that the scarps at the rear edge of the slope were caused by historical shallow toppling. Further research is recommended to determine the extent of toppling deformation and evaluate the slope stability under the disturbance of tunnel excavation.

In the context of freshwater ecosystems, turbidity and suspended solids play crucial roles, with their levels significantly influenced by anthropogenic activities. This study focuses on assessing and monitoring these parameters in Albufera de Valencia using Sentinel-2 imagery. The primary aim is to establish reliable estimation algorithms that can contribute to effective ecosystem management. The study calibrated and validated algorithms for estimating turbidity and suspended solids. The R783×R705/R490 model proved to have the best performance for estimating turbidity and total solids in the Albufera. However, R783/R490 obtained a higher coefficient of de-termination for the organic part, while the R705 band was selected for the inorganic part. However, to achieve better estimates of turbidity and inorganic matter, more research is needed in the future. The implications of excessive suspended solids are underscored, including the depletion of dissolved oxygen, and reduced primary productivity due to limited light penetration and habitat availability. Collaboration between disciplines such as limnology, optics and water chemistry are crucial to advance water quality estimation models in lakes and lagoons such as Albufera. By integrating expertise and approaches from these diverse fields, new knowledge can be gained and the basis for more effective management and conservation strategies can be laid.

This study investigates the intricate relationship between Venezuelan migration and urban growth in Colombia from 2018 to 2021. The study employs remote sensing data and social network metrics to uncover migration patterns and their impact on urban expansion. The methodology consists of three stages. Firstly, nighttime satellite imagery is used to analyze year-over-year urban growth in Colombia. Secondly, social network data estimates Venezuelan migration, overcoming challenges of underreporting and informal border crossings. Lastly, an econometric analysis explores the quantitative link between Venezuelan migration and urban growth, integrating socioeconomic variables to address endogeneity. The findings reveal the complex interplay of Venezuelan migration, socioeconomic factors, and urban growth. The study outlines remote sensing analysis, introducing the Anthropogenic Footprint Expansion Index (AFEI) to quantify urban growth. Facebook API data estimates migration trends and explores socioeconomic impacts on urban expansion. The analysis uncovers migration, poverty, aging, and urban population proportion as key factors affecting Colombia's urban landscape. Furthermore, the research underscores how Venezuelan migration affected short-term urban expansion pre- and post-COVID-19. Migration had a notable effect before the pandemic, but this influence waned afterward. The study highlights migration's short-term nature and emphasizes age demographics' role in medium-term dynamics.

Leakages in the water distribution networks (WDNs) are real problems for utilities and other governmental agencies. Timely leak detection and location identification has been a challenge. In this paper, an integrated approach to geospatial and infrared image processing method was used for robust leak detection. The method combines drops in flow, pressure, and chlorine residuals to determine potential water leakage locations in the WDN using Geographic Information System (GIS) techniques. GIS layers were created from the hourly values of these three parameters for the city of Sharjah provided by Sharjah Electricity, Water and Gas Authority (SEWA). These layers are then analyzed for locations with dropped values of each of the parameters and are overlaid with each other. In the case where there were no overlaying locations between flow and pressure, further water quality analysis was avoided, assuming no potential leak. In the case where there are locations with drops in flow and pressure layers, these overlaying locations are then examined for drops in chlorine values. If overlaying locations are found, then these regions are considered potential leak locations. Once potential leak locations are identified, a specialized remote sensing technique can be used for precise leak location. This study also demonstrated the suitability of using an infrared camera for leak detection in a laboratory-based setup. This paper concludes that the following methodology can help water utility companies in the timely detection of leaks, saving money, time, and effort.

The assessment of ecosystem quality and the maintenance of optimal ecosystem function requires understanding vegetation area dynamics and its relationship with climate variables. This study aims to detect vegetation area changes over a downstream dam and to understand the influence of the dam as well as climatic variables on the region’s vegetation areas. The case study is located in an arid area with an average rainfall amount of 50 to 100 mm/year. An analysis of seasonal changes in vegetation areas was conducted using the Normalized Difference Vegetation Index (NDVI), and supervised image classification was used to evaluate changes in vegetation areas using Landsat imagery. Pearson correlation and multivariate linear regression were used to assess the response of local vegetation areas to both hydrologic changes due to dam construction and climate variability. The NDVI analysis reveals a considerable vegetation decline after the dam construction in the dry season. This is primarily associated with the impoundment of the seasonal water by the dam and the increase in cropland areas due to dam irrigation. A significantly stronger correlation between vegetation changes and precipitation and temperature variations is observed before the dam construction. Furthermore, multivariant linear regression was used to evaluate the variations of equivalent water thickness (EWT), climate data, and NDVI before and after the dam construction. The results suggest that 85 percent of the variability in the mean NDVI was driven by climate variables and EWT before the dam construction. On the other hand, it was found that only 42 percent of the variations in NDVI were driven by climate variables and EWT after the dam construction for both dry and wet seasons.

A large amount of raw data collected by satellites is processed by the production chain to obtain a large amount of product data, of which the secure exchange and storage is of interest to researchers in the field of remote sensing information science. Authentic, secure data is a critical foundation for data analysis and decision-making. And traditional centralized cloud computing systems are vulnerable to attacks, and once the central server is successfully attacked, all data will be lost. Distributed Ledger Technology (DLT) is an innovative computer technology that can ensure information security, traceability and tamper-proof, and can be applied to the field of remote sensing. Although there are many advantages to using DLT in remote sensing applications, there are some obstacles and limitations to its application. Remote sensing data has the characteristics of large data volume, spatiotemporal nature, global and so on, and it is difficult to store and interconnect remote sensing data in the blockchain. To address these issues, this paper proposes a trustworthy and decentralized system using blockchain. The novelty of this paper is to propose a multi-level blockchain architecture in which the system collects remote sensing data and stores it in the Interplanetary File System (IPFS) network, after generating the IPFS hash, the network rehashes the value again and uploads it on the Ethereum chain for public query. Distributed data storage improves data security, supports the secure exchange of information, and improves the efficiency of data management.

In current work, we illustrate the effect of adding small amount of Carbon to very common Co2MnSi Heusler alloy based-glass coated microwires. A significant change in the magnetic and structure structural properties has observed for the new alloy Co2MnSiC compared to the Co2MnSi alloy. The magneto-structural investigations have performed to clarify the main phys-ical parameters i.e., structural & magnetic at a wide range of measuring temperature. The XRD analysis illustrated the well-defined crystalline structure with average grain size (Dg = 29.16 nm) and a uniform cubic structure with A2-type compared to the mixed L21 and B2 cubic structures for Co2MnSi-based glass coated microwires. The magnetic behaviour has investigated at a tem-perature range (5 to 300 K) and an external applied magnetic field (50 Oe to 20 kOe). The adding of small amount of Carbon to the Co2MnSi matrix enhance the magnetic thermal stability, where the thermomagnetic behaviour of Co2MnSiC glass-coated microwires show a perfect stable be-haviour for a temperature range from 300 K to 5 k, the differences between the coercivity value is only 0.3 Oe compared to 4 Oe for Co2MnSi-sample. In addition, M-H loops measured at tempera-ture below 50 K show unsaturated loops; meanwhile the Co2MnSi loops shows a strong antifer-romagnetic coupling for the loops measured below 50 K. By studying the field cooling (FC) and field heating (FH) magnetizations curves at a wide range of external applied magnetic field we detected a critical magnetic field (H = 1 kOe) where FC and FH curves have a stable magnetic behavior for Co2MnSiC sample, such stability does not find in Co2MnSi sample. We proposed a phenomenal expression to estimate the magnetization thermal stability, ΔM (%), of FC and FH magnetization curves and the maximum value is detected at the critical magnetic field where ΔM (%) ≈ 98 %. The promising magnetic stability of Co2MnSiC glass-coated microwires with tem-perature is due to the changing of the microstructure induced by adding Carbon, as the A2-type structure show a unique stability by variation the temperature and the external magnetic field. In addition, a unique internal mechanical stress, which induced during the fabrication process and plays on controlling magnetic behavior with temperature and external magnetic field. The ob-tained results make Co2MnSiC promising candidate for magnetic sensing devices based Heusler glass-coated microwires.

Many drylands around the world have seen both soil and vegetation degradation around watering points. It can be seen in spaceborne imagery as radial brightness belts that fade with distance from the water areas. The study's primary goal was to characterize spatio-temporal land degradation/rehabilitation in the drylands of the southeast Iberian Peninsula. The brightness index of Tasseled Cap was discovered to be the best spectral transformation for enhancing the contrast between the bright-degraded areas near the points and the darker surrounding areas far from and in-between these areas. To comprehend the spatial structure present in spaceborne imagery of two desert sites and three key time periods, semi-variograms were created (mid-late 2000s, around 2015, and 2020). In order to assess spatio-temporal land-cover patterns, a geostatistical model (kriging) was used to smooth brightness index values extracted from 30 m spatial resolution images. To assess the direction and intensity of changes between study periods, a change detection analysis based on kriging prediction maps was performed. These findings were linked to the socioeconomic situation prior to and following the EU economic crisis. The study discovered that degradation occurred in some areas as a result of the region's agricultural activities being exploited.

This study is describing airborne geophysical survey, carried out in Northeastern Russia (Archangelsk region) in order to aimed at geological mapping of Grib kimberlite pipe, which is known to have strong presence of remanent magnetization. Airborne data were inverted using two techniques, the magnetization vector inversion (MVI) and a QDIK inversion, a proprietary algorithm developed at the Irkutsk State University (ISTU), which allows extracting four parameters from airborne magnetic data (magnetic susceptibility, inclination, declination and Koeniksberger ratio). The inversion results were further analyzed and verified against the known geology of the Grib kimberlite. The study allowed to better map geology of Grib kimberlite using only uninvasive (remote sensing) technology

Aldehydes are natural volatile aroma compounds generated by the Maillard reaction of sugars and amino acids in food and affect the flavor of food. They have been reported to exert taste-modifying effects, such as increases in taste intensity at concentrations below the odor detection threshold. The present study examined the taste-enhancing effects of short-chain aliphatic aldehydes, such as isovaleraldehyde (IVAH) and 2-methylbutyraldehyde, attempted to identify the taste receptors involved. The results obtained revealed that IVAH enhanced the taste intensity of taste solutions even under the condition of olfactory deprivation by a noseclip. Furthermore, IVAH activated the calcium-sensing receptor CaSR in vitro. Receptor assays on aldehyde analogues showed that C3-C6 aliphatic aldehydes and methional, a C4 sulfur aldehyde, activated CaSR. These aldehydes functioned as a positive allosteric modulator for CaSR. The relationship between the activation of CaSR and taste-modifying effects was investigated by a sensory evaluation. Taste-modifying effects were found to be dependent on the activation state of CaSR. Collectively, these results suggest that short-chain aliphatic aldehydes function as taste modulators that modify sensations by activating orally expressed CaSR. We propose that volatile aroma aldehydes may also partially contribute to the taste-modifying effect via the same molecular mechanism as kokumi substances.

Evaluating the performance of water indices and mapping the spatial distribution of water-related ecosystems are important for monitoring surface water resources. This is particularly the case for Ethiopia since there is limited information available on water resources development over time despite its relevance for the people and ecosystems. To address this problem, this paper evaluates the performance of seven water indices for country-scale surface water detection based on high spatial and multi-temporal resolution Sentinel-2 data, processed using the Google Earth Engine cloud computing system. Results show that the water index (WI) and automatic water extraction index with shadow (AWEIsh) are the most accurate ones to extract surface water. Comparisons are based on qualitative visual inspections and quantitative accuracy indicators. For the latter, WI and AWEIsh obtained kappa coefficients of 0.96 and 0.95, respectively, and an overall accuracy of 0.98 each. Both indices accounted for similar spatial coverages of surface waters with 82,650 km2 (WI) and 86,530 km2 (AWEIsh) for the whole of Ethiopia.

The rapid development of Deep Learning brought novel methodologies for 3D Object Detection using LiDAR sensing technology. These improvements in precision and inference speed performances lead to notable high performance and real-time inference, which is especially important for self-driving purposes. However, the developments carried by these approaches overwhelm the research process in this area since new methods, new technologies, and software versions lead to different project necessities, specifications and requirements. Moreover, the improvements brought by the new methods may be due to improvements in newer versions of deep learning frameworks and not just the novelty and innovation of the model architecture. Thus, it became crucial to create a framework with the same software versions, specifications and requirements that accommodate all these methodologies and allow the easy introduction of new methods and models. A framework is proposed that abstracts the implementation, reusing and building of novel methods and models. The main idea is to facilitate the representation of state-of-the-art (SoA) approaches and simultaneously encourage the implementation of new approaches by reusing, improving and innovating modules in the proposed framework, which has the same software specifications to allow a fair comparison. This makes it possible to determine if the key innovation approach outperforms the current SoA by comparing models in a framework with the same software specifications and requirements.

A nonlinear MEMS multimass sensor is investigated. The MEMS design consists of an array of nonlinear microcantilevers clamped to a shuttle mass which, in turn, is attached to a linear spring and a dashpot. The microcantilevers are made of a polymeric hosting matrix reinforced by aligned carbon nanotubes (CNT) with different CNT volume fractions in each cantilever so as to suitably tune the frequency bandwidth of the device. The advantages of a single input-single output (SISO) system are here exploited for the design, actuation and detection principles as in previous literature.

Smart flexible materials with piezoresistive property are increasingly used in sensors field. When embedded in structures, they would allow an in-situ structural health monitoring and damage assessment of impact loading such as crash, bird strikes and ballistic impacts. However, this could not be achieved without a deep characterization of the relation between piezoresistivity and mechanical behavior. The aim of this paper is to study the potential use of the piezoresistivity effect of a conductive foam made of a flexible polyurethane matrix filled with active carbon for integrated Structural Health Monitoring (SHM) and damage assessment applications. To do so, PolyUrethane Foam filled with Active Carbon, namely PUF-AC, are manufactured and tested under quasi-static compression tests and under Dynamic Mechanical Analyser (DMA) with in-situ measurement of its resistivity during tests. A relation is proposed for describing the evolution of the resistivity versus strain and stress. In addition, a demonstrative experiment of a PUF-AC sample subjected to low velocity impact assesses the interest of this type of materials for damage assessments.

In current study we have obtained Co2FeSi-glass coated microwires with different geometrical aspect ratio, ρ = d/Dtot (diameter of metallic nucleus, d and total diameter, Dtot). The structure and magnetic properties are investigated at a wide range of temperature. XRD analysis illustrates a notable changing in the microstructure by increasing the aspect ratio of Co2FeSi glass coated microwires. Amorphous structure is detected for the sample with the lowest aspect ratio (ρ = 0.23), whereas a growth of crystalline structure is observed in the other samples (aspect ratio ρ = 0.30 and 0.43). This change at the microstructure properties correlates with dramatic changing in magnetic properties. For the sample with the lowest ρ -ratio, non-perfect square loops are obtained with low normalized remanent magnetization. A notable enhancement in the squareness and coercivity are obtained by increasing ρ -ratio. Changing the internal stresses strongly affects the microstructure, resulting in a complex magnetic reversal process. The thermomagnetic curves show large irreversibility for the Co2FeSi with low ρ -ratio. Meanwhile, if we increase the ρ -ratio, the sample shows perfect ferromagnetic behavior without irreversibility. The current result illustrates the ability to control the microstructure and magnetic properties of Co2FeSi-glass-coated microwires by changing only their geometric properties without performing any addition heat treatment. The modification of geometric parameters of Co2FeSi glass-coated microwires allows to obtain microwires which exhibit an unusual magnetization behavior that offers opportunities to understand the phenomena of various types of magnetic domain structures, which is essentially helpful for designing sensing devices based on thermal magnetization switching.

Inositol poly- and pyrophosphates (InsPs and PP-InsPs) are central eukaryotic messengers. These very highly phosphorylated molecules can exist in two distinct conformations, a canonical one with five phosphoryl groups in equatorial positions, and a “flipped” axial conformation. Using 13C-labeled InsPs / PP-InsPs, the behavior of these molecules was investigated by 2D-NMR under solution conditions reminiscent of a cytosolic environment. Remarkably, the most highly phosphorylated messenger InsP8 readily adopts both conformations at physiological conditions. Environmental factors - such as pH, metal cation composition, and temperature - strongly influence the conformational equilibrium. Thermodynamic data revealed that the transition of InsP8 from the equatorial to the axial conformation is, in fact, an exothermic process. The speciation of InsPs and PP-InsPs also affects their interaction with protein binding partners; addition of Mg2+ decreased the binding constant Kd of InsP8 to an SPX protein domain. The results illustrate that PP-InsP speciation reacts very sensitively to solution conditions, suggesting it might act as an environment-dependent molecular switch.

The increase in the production and entry of greenhouse gases into the earth's atmosphere has disturbed the balance of the environment. The signs of climate change (caused by global warming) are clearly visible and its effects are tangible. According to the United Nations, if the current trend continues, the global average temperature will increase by 3.2°C by the end of the century, which will have terrible consequences if it happens. This research aimed to investigate the effects of greenhouse gases and climate changes resulting from them on Hyrcanian forests. The Hyrcanian forests, as one of the oldest forest areas (remaining from the Paleogene era), were studied by telemetry from 2013 to 2021. The analysis of the images taken from the Landsat 8 satellite showed that during 9 years, the NDVI index decreased by 0.6 units and the average air temperature increased by 0.5°C. Although the average relative humidity has only increased by 2%, the average annual rainfall has recorded an increase of 25mm. The analysis of the statistics showed that the rains occur irregularly and are often torrential. Therefore, it is predicted that as the average temperature continues to increase, the NDVI index will further decrease, and as a result, the forest cover will become weaker and the soil will lose more water absorption power, and due to the increase in average rainfall. successive floods will occur. Therefore, soil erosion increases and the extinction and migration of plant and animal species increase significantly.

The detection of waste plastics in the marine and terrestrial environment using satellite Earth Observation data offers the possibility of large-scale mapping, and reducing on-the-ground manual investigation. In addition, costs are kept to a minimum by utilizing free-to-access Copernicus data. A Machine Learning based classifier was developed to run on Sentinel-1 and -2 data. In support of the training and validation, a dataset was created with terrestrial and aquatic cases by manually digitizing varying landcover classes alongside plastics under the sub-categories of greenhouses, plastic, tyres and waste sites. The trained classifier, including an Artificial Neural Network and post-processing decision tree, was verified using five locations encompassing these different forms of plastic. Although exact matchups are challenging to digitize, the performance has generated high accuracy statistics, and the resulting land cover classifications have been used to map the occurrence of plastic waste in aquatic and terrestrial environments.

A neglected dimension of the fire regime concept is fire patchiness. Habitat mosaics that emerge from the grain of burned and unburned patches (pyrodiversity) are critical for the persistence of a diverse range of plant and animal species. This issue is of particular importance in frequently burned tropical Eucalyptus savannas, where coarse fire mosaics have been hypothesized to have caused the recent drastic population declines of small mammals. Satellites routinely used for fire mapping in these systems are unable to accurately map fine-grained fire mosaics, frustrating our ability to determine whether declines in biodiversity are associated with local pyrodiversity. To advance this problem, we have developed a novel method (we call ‘double-differenced dNBR’) that combines the infrequent (c. bi-monthly) detailed spatial resolution Landsat with daily coarse scale coverage of MODIS and VIIRS to map pyrodiversity in the savannas of Kakadu National Park. We used seasonal Landsat mosaics and differenced Normalized Burn Ratio (dNBR) to define burned areas, with a modification to dNBR that subtracts long-term average dNBR to increase contrast. Our results show this approach is effective in mapping fine-scale fire mosaics in the homogenous lowland savannas, although inappropriate for nearby heterogenous landscapes. Comparison of this methods to other fire metrics (e.g., area burned, seasonality) based on Landsat and MODIS imagery suggest this method is likely accurate and better at quantifying fine-scale patchiness of fire, albeit it demands detailed field validation.

Quorum sensing (QS) is a form of intra- and inter-species communication system which is employed by bacteria to regulate their collective behavior in a cell population-dependent manner. QS has been implicated in the virulence of several pathogenic bacteria. This work aimed to investigate the anti-quorum sensing (anti-QS) potential of ethanolic extracts of eight aromatic plants of Cyprus namely, Origanum vulgare subsp. hirtum, Rosmarinus officinalis, Salvia officinalis, Lavendula spp., Calendula officinalis, Melissa officinalis, Sideritis cypria, and Aloysia citriodora. We initially assess the effects of the extracts on autoinducer 2 (AI-2) signaling activity, using Vibrio harveyi BB170 as a reported strain. We subsequently assess the effect of the ethanolic extracts on QS-related processes including biofilm formation and swarming and swimming motilities of Escherichia coli MG1655. Of the tested ethanolic extracts those of Origanum vulgare subsp. hirtum, Rosmarinus officinalis, and Salvia officinalis were the most potent AI-2 signaling inhibitors while the extracts from the other plants exhibited low to moderate inhibitory activity. The three ethanolic extracts also inhibited the biofilm formation (>60%) of E. coli MG1655, as well as its swimming and swarming motility in a concentration-dependent manner. These extracts may consider true anti-QS inhibitors because they disrupt QS-related activities of E. coli MG1655 without affecting bacterial growth. The results suggest that plants from the unexplored flora of Cyprus could serve as a source to identify novel anti-QS inhibitors to treat infectious diseases caused by pathogens resistant to antibiotics

Machine learning (ML) is a subdivision of artificial intelligence in which the machine learns from machine-readable data and information. It uses data, learns the pattern and predicts the new outcomes. Its popularity is growing because it helps to understand the trend and provides a solution that can be either a model or a product. Applications of ML algorithms have increased drastically in G.I.S. and remote sensing in recent years. It has a broad range of applications, from developing energy-based models to assessing soil liquefaction to creating a relation between air quality and mortality. Here, in this paper, we discuss the most popular supervised ML models (classification and regression) in G.I.S. and remote sensing. The motivation for writing this paper is that ML models produce higher accuracy than traditional parametric classifiers, especially for complex data with many predictor variables. This paper provides a general overview of some popular supervised non-parametric ML models that can be used in most of the G.I.S. and remote sensing-based projects. We discuss classification (Naïve Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), Decision Trees (DT)) and regression models (Random Forest (RF), Support Vector Machine (SVM), Linear and Non-Linear) here. Therefore, the article can be a guide to those interested in using ML models in their G.I.S. and remote sensing-based projects

Generative adversarial networks (GAN) opened new possibilities for image processing and analysis. Inpainting, dataset augmentation using artificial samples or increasing resolution of aerial imagery are only a few notable examples of utilizing GANs in remote sensing. This is due to a unique construction and training process expressed as a duel between GAN components. In the following research, GAN has been applied to enhance panchromatic images with Normalized Difference Vegetation Index (NDVI). Panchromatic orthoimagery dataset with NDVI ground-truth labels was prepared by combining RGB and NIR orthophoto. The dataset was then utilized as input for a conditional generative adversarial network (cGAN) to perform an image-to-image translation. The main goal of the neural network was to generate an artificial NDVI channel for each processed 256px × 256px patch using only information available in the panchromatic imagery. The network achieved 0.9869 ± 0.0099 SSIM, 47.1635643 ± 4.0527963 PNSR and 0.0048023 ± 0.0018756 RSME on the test set. Perceptual evaluation was performed to verify the usability of the method when working with a real-life scenario. The research confirms that the structure and texture of the panchromatic aerial remote sensing image contains sufficient information for NDVI estimation for various objects of urban space. Even though these results can be used to highlight areas rich in vegetation and distinguish them from urban background, there is still room for improvement in terms of accuracy of estimated values.

Spatial pattern-oriented evaluations of distributed hydrological models have contributed towards an improved realism of hydrological simulations. This advancement was supported by the broad range of readily available satellite-based datasets of key hydrological variables, such as evapotranspiration (ET). At larger scale, spatial patterns of ET are often characterized by an underlying climate gradient, and with this study, we argue that gradient dominated patterns may hamper the potential of spatial pattern-oriented evaluation frameworks. We hypothesize that the climate control of spatial patterns of ET overshadows the effect model parameters have on the simulated variability. To solve this limitation, we propose a climate normalization strategy. This is demonstrated for the Senegal River basin as modeling case study, where the dominant north-south precipitation gradient is the main driver of the observed hydrological variability. Two multi-objective calibration experiments investigate the effect of climate normalization. Both calibrations utilize observed discharge (Q) in combination with remote sensing ET data, where one is based on the original ET pattern and the other utilizes the normalized ET pattern. We identify parameter sets that balance the tradeoffs between the two independent observations and find that the calibration using the normalized ET pattern does not compromise the spatial patern performance of the original pattern. However, vice versa, this is not necessarily the case, since the calibration using the original ET pattern showed a poorer performance for the normalized pattern. Both calibrations reached comparable performance of Q. With this study, we identified a general shortcoming of spatial pattern-oriented model evaluations using ET in basins dominated by a climate gradient, but we argue that this also applies to other variables such as, soil moisture or land surface temperature.

Urban areas have very complex spatial structures. These spatial structures are primarily composed of a complex network of built environments, which evolve rapidly as the cities expand to meet the growing population’s demand and economic development. Therefore, studying the impact of spatial structures on urban heat patterns is extremely important for sustainable urban planning and growth. We investigated the relationship between surface temperature obtained by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER, at 90 m spatial resolution) on the current EOS-Terra platform and different urban components based on the classification of high-resolution QuickBird imagery. We further investigated the relationships between surface temperature and building footprint and land use information acquired from the New York City (NYC) Department of City Planning. The ASTER image reveals fine-scale urban heat patterns in the NYC metropolitan region. The dark and medium-dark impervious surfaces, along with bright surfaces, generate higher surface temperatures. Even with highly reflective urban materials, the presence of impervious materials leads to an increased surface temperature. At the same time, trees and shadows are effective in reducing urban heat. The data aggregated to the census tract reveals high-temperature clusters in Queens, Brooklyn, and the Bronx region of NYC. These clusters are associated with industrial and manufacturing areas and multi-family walk-up buildings as dominant land use. The census tracts with more trees and higher building height variability generate lower surface temperatures, consistent with shadow cast by high-rise buildings and trees. The results of this study can be valuable for urban heat island modeling on the effects of building heights variability and tree shadows on small-scale surface temperature patterns. It can also help identify the risk areas during extreme heat events to protect public health.

Existing methods enhance mid-low resolution remote sensing ship detection by feeding super-resolved images to the detectors. Although these methods marginally improve the detection accuracy, the correlation between image super-resolution (SR) and ship detection is under-exploited. In this paper, we propose a simple but effective ship detection method called ShipSR-Det, in which both the output image and the intermediate features of the SR module are fed to the detection module. Using the super-resolved feature representation, the potential benefit introduced by image SR can be fully used for ship detection. We apply our method to the SSD and Faster-RCNN detectors and develop ShipSR-SSD and ShipSR-Faster-RCNN, respectively. Extensive ablation studies validate the effectiveness and generality of our method. Moreover, we compare ShipSR-Faster-RCNN with several state-of-the-art ship detection methods. Comparative results on the HRSC2016, DOTA and NWPU VHR-10 datasets demonstrate the superior performance of our proposed method.

The scarcity of surface water resources in the dry season in the Kilinochchi district increases the demand for freshwater. Therefore, the existing groundwater resources should be managed to overcome the situation. Several authors worldwide have published studies on the delineation of potential groundwater zone. However, only a few studies addressed the delineation of potential groundwater zones in the Kilinochchi district. This study aims to delineate potential groundwater zones in Kilinochchi, Sri Lanka using integrated Remote Sensing, Geographic Information Systems, and Analytic Hierarchy Process techniques. Groundwater potential zones are demarcated for the Kilinochchi district by overlaying thematic layers: geology, geomorphology, land use/land cover, soil types, drainage density, slope, lineament, and rainfall. Saaty's scale was applied to the assigned weights of the chosen thematic layers and their features. The thematic layers were integrated into a Geographic Information System, and a weighted overlay analysis is carried out to delineate groundwater zones. Thus the resultant map is categorized into five different potential zones: very low, low, moderate, high, and very high. It was found that the very high groundwater potential zone is mainly found in the north-eastern part of the study area covering 111.26 km2. The upper north-western, middle, and eastern parts of the study area fall within the high groundwater potential zone covering about 507.74 km2. The moderate groundwater potential zones (309.89 km2) mainly occurred in the western part, and the extreme west part of the study area falls under low (207.78 km2) and very low (59.12 km2) zones. The groundwater potential map was validated with the existing seventy-nine wells, which indicated a good prediction accuracy of 81.8%. This research will help policymakers better manage the Kilinochchi district's groundwater resources and gives scope for further research into groundwater exploration in the area.

Electromagnetic sensing has been used for diverse applications of non-destructive testing, including the surface inspection, measurement of properties, object characterization. However, the measurement accuracy could be significantly influenced by the lift-off between sensors and samples. To address the issue caused by lift-offs, various strategies have been proposed for the permeability measurement of ferromagnetic steels, which mainly involves different sensor designs and signal features (e.g., the zero-crossing feature). In this paper, a single high-frequency scenario for the permeability retrieval is introduced. By combining the signal of two sensing pairs, the retrieval of magnetic permeability is less affected by the lift-off of sensors. Unlike the previous strategy on reducing the lift-off effect (directly taking the phase term out of the integration) using the Dodd-Deeds analytical method, the proposed method is based on a high-frequency linear feature of the phase term. Therefore, this method has the merit of high accuracy and fast processing for the permeability retrieval (a simplified version of Dodd-Deeds analytical formulas after the integration). Experimental measurement has been carried out on the impedance measurement of designed sensors interrogating ferromagnetic dual-phase steels. For sensor lift-offs of up to 10 mm, the error of the permeability retrieval is controlled within 4 % under the optimal frequency.

We have an unprecedented ability to map the Earth’s surface as deep learning technologies are applied to an abundance of high-frequency Earth observation data. Simple, free, and effective methods are needed to enable a variety of stakeholders to use these tools to improve scientific knowledge and decision making. Here we present a trained U-Net model that can map and delineate ground mounted solar arrays using publicly available Sentinel-2 imagery, and that requires minimal data pre-processing and no feature engineering. By using label overloading and image augmentation during training, the model is robust to temporal and spatial variation in imagery. The trained model achieved a precision and recall of 91.5% each and an intersection over union of 84.3% on independent validation data from two distinct geographies. This generalizability in space and time makes the model useful for repeatedly mapping solar arrays. We use this model to delineate all ground mounted solar arrays in North Carolina and the Chesapeake Bay watershed to illustrate how these methods can be used to quickly and easily produce accurate maps of solar infrastructure.

Atomtronics is a relatively new subfield of atomic physics that aims to realize the device behavior of electronic components in ultracold atom-optical systems. The fact that these systems are coherent makes them particularly interesting since, in addition to current, one can impart quantum states onto the current carriers themselves or perhaps perform quantum computational operations on them. After reviewing the fundamental ideas of this subfield, we report on the theoretical and experimental progress made towards developing externally-driven and closed loop devices. The functionality and potential applications for these atom analogs to electronic and spintronic systems is also discussed.

This work focuses on the analysis of the performance of satellite-based precipitation products for monitoring extreme rainfall events. Five precipitation products are inter-compared and evaluated in capturing indices of extreme rainfall events during 1998-2019 considering four indices of extreme rainfall. Satellite products show a variable performance, which in general indicates that the occurrence and amount of rainfall of extreme events can be both underestimated or overestimated by the datasets in a systematic way throughout the country. Also, products that consider the use of ground truth data have the best performance.

Dermal interstitial fluid (ISF) is a novel source of biomarkers that can be considered as an alternative to blood sampling for disease diagnosis and treatment. Nevertheless, in vivo extraction and analysis of ISF are challenging. On the other hand, microneedle (MN) technology can address most of the challenges associated with dermal ISF extraction and is well-suited for long-term, continuous ISF monitoring as well as in situ detection. In this review, we first briefly summarise the different dermal ISF collection methods and compare them with MN methods. Next, we elaborate on the design considerations and biocompatibility of MNs. Subsequently, the fabrication technologies of various MNs used for dermal ISF extraction, including solid MNs, hollow MNs, porous MNs and hydrogel MNs, are thoroughly explained. In addition, different sensing mechanisms of ISF detection will be discussed in detail. Subsequently, we identify the challenges and propose the possible solutions associated with ISF extraction. A detailed investigation is provided for the transport and sampling mechanism of ISF in vivo. Also, the current in vitro skin model integrated with the MN arrays will be discussed. Finally, future directions to develop a point-of-care (POC) device to sample ISF are proposed.

’Lockdown’ periods in response to COVID-19 have provided a unique opportunity to study the impacts of economic activity on environmental pollution (e.g. NO2, aerosols, noise, light). The effects on NO2 and aerosols have been very noticeable and readily demonstrated, but that on light pollution has proven challenging to determine. The main reason for this difficulty is that the primary source of nighttime satellite imagery of the earth is the SNPP-VIIRS/DNB instrument, which acquires data late at night after most human nocturnal activity has already occurred and much associated lighting has been turned off. Here, to analyze the effect of lockdown on urban light emissions, we use ground and satellite data for Granada, Spain, during the COVID-19 induced confinement of the city’s population from March 14 until May 31, 2020. We find a clear decrease in light pollution due both to a decrease in light emissions from the city and to a decrease in anthropogenic aerosol content in the atmosphere which resulted in less light being scattered. A clear correlation between the abundance of PM10 particles and sky brightness is observed, such that the more polluted the atmosphere the brighter the urban night sky. An empirical expression is determined that relates PM10 particle abundance and sky brightness at three different wavelength bands.

The assessment of the number of people exposed to natural hazards, especially in countries with strong urban growth, is difficult to be updated at the same rate as land use develops. This paper presents a remote sensing based procedure for quick updating the assessment of the population exposed to natural hazards. A relationship between satellite nightlights intensity and urbanization density from global available cartography is first assessed when all data are available. This can be used to extrapolate urbanization data at different time steps, updating exposure each time new nightlights intensity maps are available. As reliability test for the proposed methodology, the number of people exposed to riverine flood in Italy is assessed, deriving a probabilistic relationship between DMSP nightlights intensity and urbanization density from GUF database for the year 2011. People exposed to riverine flood are assessed crossing the population distributed on the derived urbanization density with flood hazard zones provided by ISPRA. The validation on reliable exposures derived from ISTAT data shows good agreement. The possibility to update exposure maps with higher refresh rate makes this approach particularly suitable for applications in developing countries, where exposure may change at sub-yearly scale.

Manual evaluation of crop injury to herbicides is time-consuming. Unmanned aircraft systems (UAS) and high-resolution multispectral sensors and machine learning classification techniques have the potential to save time and improve precision in the evaluation of herbicide injury in crops, including grain sorghum (Sorghum bicolor L. Moench). The objectives of this research were to (1) evaluate three supervised classification algorithms (support vector machine, maximum likelihood, and random forest) for categorizing high-resolution UAS imagery to aid in data extraction and (2) evaluate the use of vegetative indices (VIs) collected from UAV imagery as an alternative to traditional methods of visual herbicide injury assessment in mesotrione-tolerant grain sorghum breeding trials. An experiment was conducted in a randomized complete block design using a factorial treatment arrangement of three genotypes by four mesotrione doses. Herbicide injury was rated visually on a scale of 0 (no injury) to 100 (complete plant mortality). The UAS flights were flown at 9, 15, 21, 27, and 35 days after treatment. Results show the SVM algorithm to be the most consistently accurate, and high correlations (r = -0.83 to -0.94; p < 0.0001) were observed between the normalized difference vegetative index (NDVI) and ground-measured herbicide injury. Therefore we conclude that VIs collected with UAS coupled with machine learning image classification, has the potential to be an effective method of evaluating mesotrione injury in grain sorghum.

Phenology is one of the ubiquitous fingerprints of climate change on our ecosystems. Monitoring the spatiotemporal patterns of vegetation phenology is thus critical. A wide range of sensors have been used to monitor vegetation phenology. Sensor point of view and resolution can potentially impact estimates of phenology. We compared three different sensors from three different remote sensing platforms—a UAV mounted RGB camera, an under canopy, upward facing hemispherical camera with R, G and NIR capabilities, and a tower mounted RGB PhenoCam—to estimate spring phenological transition in a mixed-species temperate forest in central Virginia, USA. Our study had two objectives: 1) to compare the above- and below- canopy inference of canopy greenness (green chromatic coordinate and normalized difference vegetation index) and canopy structural attributes (leaf area and gap fraction) by matching under-canopy hemispherical photos with high spatial resolution (0.03 m) drone imagery to find the appropriate spatial coverage and resolution for comparison; 2) to compare how each sensor performed in estimating the temporality of the spring phenological transition. We find that a spatial buffer of 20 m radius for UAV imagery is most closely comparable to under-canopy imagery in this system. Sensors and platforms agree within +/- 5 days of when canopy greenness stabilizes from the spring phenophase into the growing season. This work has implications for paring UAV imagery with both tower-based observation platforms, as well as plot-based studies (e.g. long-term monitoring, existing research networks, permanent plots).