The literature analysis did not indicate any studies on fluorination tests of carbon nanocomposite coatings doped with transition metals of the nc-MeC/a-C type. This was the motivation for undertaking research in this area. As a model coating to investigate the effect of fluorination in a tetrafluoromethane (CF4) atmosphere, a nano-composite carbon coating doped with chromium nc-CrC/a-C produced by magnetron sputtering from graphite targets and the use of a Pulse-DC type medium frequency power supply was chosen [6]. After deposition of the gradient CrCN adhesive sublayer, fluorination of the main coating was conducted in a reactive mode in an (Ar+CF4) atmosphere at various CF4 contents. It was observed that the presence of CF4 in the atmosphere resulted in a reduced amount of chromium carbides formed in favor of chromium fluorides. So far, this is an observation that seems unnoticed by the carbon coatings researchers. Fluorine was assumed to bond much more readily to carbon than to chromium, due to the stability of tetrafluoromethane (CF4). The opposite seems to be true. The mechanical properties (nano-hardness and Young's modulus) and tribological properties in the 'pin-on-disc' friction pair are presented, along with the analysis of bonds occurring between chromium, carbon, and fluorine (XPS).

The benthic matrix acts as a key natural archive where the memory of long-term timescale environmental changes is recorded. Some ecological and chemical features of fjord sediments were explored during the AREX cruise carried out in the Svalbard archipelago in summer 2021. The activity rates of the enzymes leucine aminopeptidase (LAP), beta-glucosidase (GLU) and alkaline phosphatase (AP) and community-level physiological profiles (CLPP) were studied with the aim of determining the functional diversity of the benthic microbial community, while bacterial isolates were screened for their susceptibility to antibiotic molecules in order to explore the role of these extreme environments as potential reservoirs of antibiotic resistance. Enzyme activity rates were obtained by fluorogenic substrates, CLPP by Biolog Ecoplates; antibiotic susceptibility assays were performed through the standard disk diffusion method. Spatial trends observed in the functional profiles of the microbial community suggested the variability in the microbial community composition, presumably related to the patchy distribution of organic substrates. Complex Carbon sources, carbohydrates and amino acids were the organic polymers preferentially metabolized by the microbial community. Multiple resistance to ampicillin, clindamycin and gentamycin was detected in all the sampled sediments, excepting in the southern Hornsund area, stressing the role of sediments as a potential reservoir of chemical wastes ascribable to antibiotic residuals. This study provides new insights on the health status of fjord sediments of West Spitsbergen applying a dual ecological and biochemical approach. Microbial communities in the fjord sediments showed globally a good functional diversity, suggesting their versatility to rapidly react to changing conditions.

Promoting sustainable consumer behavior is now an obligation under new European legislation, requiring Life Cycle Assessment (LCA) for accurate environmental impact evaluation. Portugal is a key textile producer, edge in competitiveness in sustainable textile production, driven by electricity-reduced carbon footprint and closed-loop manufacturing. Additionally, while simple spreadsheets can estimate a product's carbon footprint, openLCA software, combined with ecoinvent database, greatly enhances environmental footprint calculations by integrating diverse impact categories, otherwise difficult to estimate. In this study, openLCA is used to evaluate the environmental footprint of a white T-shirt made in Portugal, with 50% recycled cotton from post-industrial wastes combined with 50% organic cotton from Turkey, to assist the design of environmental key performance indicators (KPI). The RECIPE and EF method (adapted) are used to calculate the environmental impacts and allow aggregation in a single score. The KPI related to the global warming impact is validated using a spreadsheet calculator. We propose an “Envi-Score” based on an A to E classification for benchmarking and better communication with the buyers. E is set as the normalized environmental impact of the European benchmark for a mixture material T-shirt encompassing cradle-to-gate boundaries. The introduction of recycled cotton proves to be environmentally beneficial over organic and conventional cotton. Organic cotton proves to be beneficial in comparison with conventional cotton for most environmental categories, except to the ones affected by the lower production yield, for example Land Use. The hotspots for the main impact categories are identified and finally proposed a labelling scheme, to clearly inform about the environmental performance of the products and avoid greenwashing, with the “Envi-Score” rate, carbon footprint, land use and water depletion.

In the global carbon cycle, atmospheric carbon emissions, both ‘natural’ and anthropogenic, are balanced by carbon uptake (i.e. sequestration) that mostly occurs via photosynthesis, plus a much smaller proportion via geological processes. Since the formation of the Earth about 4.54 billion years ago the ratio between emitted and sequestered carbon has varied considerably with atmospheric CO₂ levels ranging from 100,000 ppm to a mere 100 ppm. Over this time, a huge amount of carbon has been sequestered due to photosynthesis and essentially removed from the cycle, being buried as fossil deposits of coal, oil and gas. Relatively low atmospheric CO₂ levels were the norm for the past 10 million years, and during the past million years they averaged about 220 ppm. More recently, the Holocene epoch, starting ~11,700 years ago, has been a period of unusual climatic stability with relatively warm, moist conditions and low atmospheric CO₂ levels of between 260 and 280 ppm. During the Holocene, stable conditions facilitated a social revolution with domestication of crops and livestock leading to urbanisation and development of complex technologies. As part of the latter process, immense quantities of sequestered fossil carbon have recently been used as energy sources, resulting in a particularly rapid increase in CO₂ emissions after 1950 CE to the current value of 424 ppm, with further rises to >800 ppm predicted by 2100. This is already perturbing the previously stable Holocene climate and threatening future food production and social stability. Today, the global carbon cycle has been shifted such that carbon sequestration is no longer keeping up with recent anthropogenic emissions. In order to address this imbalance it is important to understand the roles of high-capacity carbon sequestration systems, such as natural forests and tropical croplands, and to devise strategies to facilitate net CO₂ uptake.

This study evaluates the viability of n-octanol as an alternative fuel in a direct-injection diesel engine, aiming to enhance sustainability and efficiency. Experiments employed varying blends of n-octanol (10%, 30%, and 50% by volume) with pure diesel, analyzing impacts on engine performance and emissions. The methodology involved testing each blend in a single-cylinder engine, measuring engine performance parameters such as brake torque and brake power under full load conditions across an engine speed range from 1400 to 2500 rpm. Comparative assessments of performance and emission characteristics at a constant engine speed of 1700 rpm were also conducted with varying loads. Results indicated that while n-octanol blends consistently improved brake thermal efficiency, they also increased brake specific fuel consumption due to the lower energy content of n-octanol. Consequently, while all n-octanol blends reduced nitrogen oxides (NOx) emissions compared to pure diesel, they also significantly decreased carbon monoxide, hydrocarbons, and smoke opacity, presenting a comprehensive reduction in harmful emissions. However, the benefits came with nuanced trade-offs: notably, higher concentrations of n-octanol (especially the 50% blend) led to a relative increase in NOx emissions as the octanol ratio increased. The study concludes that n-octanol significantly improves engine efficiency and reduces diesel dependence, but optimizing the blend ratio is crucial to balance performance improvements with comprehensive emission reductions.

Eelgrass meadows have attracted a lot of attention not only for their ability to maintain marine ecosystems as feeding grounds for marine organisms, but also for their potential to store atmos-pheric and dissolved CO2 as blue carbon. This study comprehensively evaluated the bacterial and chemical composition data obtained from eelgrass sediments of different scales along the coast of Japan to investigate the sediment effect on the acclimatization of eelgrass. Regardless eelgrass habitat, approximately 1% Anaerolineales, Babeliales, Cytophagales, and Phycisphaerales were present in the bottom sediment. Sulfate-reducing bacteria (SRB) were present at 3.69 % in eelgrass sediment compared to 1.70 % in bare sediment. Sulfur-oxidizing bacteria (SOB) were present at 2.81% and 1.10% in the eelgrass and bare sediment, respectively. Bacterial composition analysis and linear discriminant analysis revealed that sulfur-oxidizing bacteria detoxified H2S in the eelgrass meadows and that the larger-scale eelgrass meadows had a higher diversity of sul-fur-oxidizing bacteria. Our result indicated that one of the following systems detoxifies the H2S in eelgrass meadows microbial oxidation by sulfur-oxidizing bacteria, oxidation by O2 perme-ated by the physical diffusion of benthos. This study indicated the issues of maintaining sustain-able conservation of the eelgrass bed.

In this study, a carbon fiber microelectrode (CF) was applied for the investigation of electrochemical behavior and determination of natural antioxidant, apocynin (APO). Given the limited solubility of APO in water, a mixture of anhydrous acetic acid (AcH) with 20%, v/v acetonitrile (AN) and 0.1 mol L-1 sodium acetate (AcNa) was used. The electrochemical properties of APO were examined through linear sweep voltammetry (LSV), differential pulse voltammetry (DPV), and cyclic voltammetry (CV). The anodic oxidation of APO, which is the basis of the method used, proved to be diffusion-controlled and proceeded with a two-electron and one proton exchange. Both radicals and radical cations, arising from the first and second step of electrode reactions, respectively, underwent subsequent chemical transformations to yield more stable final products (EqCiEiCi mechanism). Using optimized DPV conditions, the anodic peak current of APO at potential of 0.925 V vs Ag/AgCl showed a good linear response within the concentration range of 2.7×10-6 – 2.6×10-4 mol L-1. The detection and quantification limits were determined as 8.9×10-7 and 2.7×10-6 mol L-1, respectively. The developed DPV method enabled the successful determination of APO in herbal extracts and in dietary supplements. It should be noted that this is the first method of voltammetric determination of APO.

In order to achieve global climate goals, it is necessary to estimate greenhouse gases (GHG) fluxes from ecosystems. To obtain a comprehensive assessment of CO2, СН4, N2O natural fluxes for the Russian Federation we used the "bottom-up" method according to [1]. We updated estimates for forest ecosystems based on State Forest Inventory data and satellite monitoring of forest disturbances. For grassland ecosystems, it was based on the correct distribution of areas between steppe and non-steppe zones. The estimated net uptake of natural ecosystems of Russia was 1.1 ± 1.8 billion tons of CO2-eq./year. The study shows that if only CO2 is taken into account, the net absorption of terrestrial ecosystems in Russia corresponds to more than -2.5 billion tons of CO2 (35% of forests’ contribution). However, given the emissions of non-CO2 GHGs, total net absorption in Russia’s natural ecosystems is reduced to about -1 billion tons of CO2-eq (with the forests’ contribution increasing to 80%). With regard to anthropogenic fluxes, the overall balance of GHGs in Russia corresponds to net-emissions of 1 billion tons of CO2-eq/year into the atmosphere. To improve reporting under the Paris Agreement, countries should aim to include only anthropogenic ("manageable") GHG fluxes on managed land.

The objective of this paper is to investigate the debonding behavior of the interface between continuously and discontinuously fiber reinforced thermoplastics using the climbing drum peel test. The study emphasizes the importance of considering different climatic boundary conditions due to the properties of thermoplastics. Specimens with varying moisture contents are prepared and tested. It is observed that an increase in moisture content initially results in a higher fracture surface energy being required to separate the two materials, but a further increase results in a decrease of the required energy. The study presents an explanatory model of increasing plasticization of the polymer due to increased polymer chain mobility, which results in more deformation energy being required to propagate the crack. The experiment is also modeled numerically for the first time with cohesive surfaces, which successfully reproduces the force-displacement curve in the experiment.

In the face of escalating climate concerns and the push for sustainable development, the global shift towards renewable and decentralized energy systems presents new challenges and opportunities. This study investigates integrating decentralized energy production, particularly photovoltaic (PV) systems, into national energy planning, aiming to optimize energy strategies that balance local production and consumption with national objectives. By analyzing the Swiss energy model, the research employs the EnergyScope and REHO models to assess the strategic implications of decentralized versus centralized energy systems. Results show that a decentralized approach can significantly reduce PV installation needs to 35 GW, about 23% of potential capacity, and decrease annual system costs by 10% to CHF 1230 per capita. This strategy emphasizes local consumption, minimizes grid reinforcement demands, and leverages economic advantages while addressing overproduction challenges through effective energy storage and grid management. Conclusions underline the strategic value of combining centralized and decentralized methods for resilient and sustainable energy planning. The study contributes to the discourse on energy policy and infrastructure planning, advocating for a hybrid model that accommodates both local conditions and broader energy objectives, urging further research into climate impacts and technology integration for a comprehensive energy future.

Providing simultaneously autotrophic and heterotrophic carbon sources is a promising strategy to overcome the limits of autotrophic syngas fermentations. D-xylose and L-arabinose are particularly interesting as they can be obtained by the hydrolysis of lignocellulosic biomass. The individual conversion of varying initial concentrations of these pentoses and D-fructose as reference was studied with C. autoethanogenum in fully-controlled stirred-tank reactors with a continuous syngas supply. All mixotrophic batch processes showed increased biomass and product formation compared to an autotrophic reference process. Simultaneous CO and D-xylose or L-arabinose conversion was observed in contrast to D-fructose. In the mixotrophic batch processes with Larabinose or D-xylose, the simultaneous CO and sugar conversion resulted in high final alcohol to acid ratios of up to 58 g g-1. L-arabinose was superior as a mixotrophic carbon source because biomass and alcohol concentrations (ethanol and 2,3-butanediol) were highest, and significant amounts of meso-2,3-butanediol (>1 g L-1) in addition to D-2,3-butanediol (>2 g L-1) were solely produced with L-arabinose. Furthermore, C. autoethanogenum could not produce meso-2,3 butanediol under purely heterotrophic conditions. The mixotrophic production of meso-2,3-butanediol from Larabinose and syngas, both available from residual lignocellulosic biomass, is very promising for use as a monomer for bio-based polyurethanes or as an antiseptic agent.

The new energy structure needs to balance energy security and dual carbon goals, which has brought major challenges to coal-fired power plants, the pollution reduction and carbon emissions reduction of coal-fired power plants will be a key task in the future. This article proposes an optimization operation of electrostatic precipitators(ESP), based on the working mechanism and historical data of ESP, voltage-current characteristic model and outlet dust concentration prediction model are constructed by deep learning algorithm, Particle Swarm Optimization(PSO) is used to achieve the optimal energy consumption while ensuring stable outlet dust concentration. By training with historical data collected on site, accurate prediction of secondary current and outlet dust concentration of ESP has been achieved, the Mean Absolute Percentage Error(MAPE) of secondary current is less than 1.5%, and the MAPE of outlet dust concentration of ESP on the test set is less than 5.2%. Finally, the optimization experiment is carried out in a 330MW coal-fired power plants, the results showed that the fluctuation of the outlet dust concentration is more stable, and the energy saving is about 43% after optimization, according to the annual operation of 300 days, the annual average carbon reduction is approximately 2621.34 tons. This method is effective and can be applied widely.

The rapid development of electric vehicles, unmanned aerial vehicles, and wearable electronic devices has led to a high interest in research related to the synthesis of graphene with a high specific surface area for energy applications. However, the problem of graphene synthesis scalability, as well as the lengthy duration and high energy intensity of the activation processes of carbon materials, are significant disadvantages. In this study, a reactor was developed for the green, simple, and scalable electrochemical synthesis of graphene oxide with a low oxygen content of 14.1 %. The resulting material was activated using a fast joule heating method. The processing of mildly oxidized graphene with a high–energy short electrical pulse (32 ms) made it possible to obtain a graphene–based porous carbon material with a specific surface area of up to 1984.5 m2/g. The low energy intensity, simplicity, and use of environmentally friendly chemicals render the proposed method scalable.

Soil moisture reserve is a key factor in maintaining soil fertility and all other related ecosystem services (including carbon sequestration, soil biodiversity, soil erosion control). In semiarid blown-sand areas under aridification water preservation is a particularly crucial task for agriculture. The international Diverfarming project (2017–2022) within the EU Horizon 2020 Program focused on the impacts of crop diversification and low-input practices in all pedoclimatic regions of Europe. In the three-year experiment conducted in the Pannonian region the impacts of intercropping asparagus with different herbs on some provisioning and regulating ecosystem services were evaluated in the Kiskunság sand regions. Relying on the findings based on a range of measured physical and chemical soil parameters and on crop yields and qualitative properties, advice was formulated for farmers. The message drawn from the experiment is somewhat ambiguous. The local farmer agrees that crop diversification improves soil quality, but he denies that it would directly influence farm competitiveness, which primarily depends on cultivation costs (such as fertilization, plant protection and labour). Further analyses are needed to prove the long-term benefits of diversification through enriching soil microbial life and the possible reduction of fertilizer use while water demand is kept at a low level and the same crop quality is ensured.

The concrete sector is known for its significant contribution to the CO2 emissions. There are two main contributing factors for this situation: the large amount of concrete consumed per year on the planet and the high CO2 released from Portland cement manufacture, the key binding agent in concrete. To face the consequent sustainability issues, diverse strategies have been explored on the carbon capture and storage potential of cementitious materials. This paper addresses the potential of storing CO2 in concrete during the curing stage, applied to the precast Portuguese industry. To this purpose, it was assumed that CO2 will become a waste that will require an outlet in the future, considering that carbon capture will become mandatory in many industries. This work concluded that the net benefit in terms of carbon retention is positive for the process of storing carbon in concrete during the curing stage. More specifically, it was demonstrated that the additional emissions from the introduction of this new operation are only 10% of the stored amount, returning a storage potential of 76 000 tonnes of CO2 yearly. Moreover, the overall net reduction in the concrete life cycle averages 9.4% and 8.8% for precast elements and only non-structural elements, respectively. When a low cement dosage strategy is coupled with carbonation curing technology, the overall carbon net reduction is estimated to be 45%.

Diamond-like carbon (DLC) films have a promising tribological application in green precision manufacturing, but their toughness represents a serious issue in the way of their application perspective. The film thickness is on the micrometer scale and makes conventional methods unsuitable for its toughness characterization. The absence of an accepted method for characterizing the DLC toughness has hindered the related research on the development of toughening methods and understanding the mechanism. This work intends to explore a relatively simple and acceptable characterization method to measure the film toughness. Moreover, toughening mechanisms of DLC films are proposed during the characterization to reveal the toughening mechanism. The toughness can be fully characterized through combining impact toughness and scratch toughness. The impact toughness can be obtained by observing the size of the indentation and the crack state around the indentation in the impact experiment. The scratch toughness could be assessed by combining the scratch morphology and Crack Propagation Resistance (CPRS). The improvement of film toughness should be carried out from two aspects: preventing microcrack initiation and suppressing crack propagation. Appropriate doping of non-carbide metal particles can effectively improve the toughness of the film, and there are seven main factors affecting the toughening of the film. Five toughening mechanisms of non-carbide metals from film preparation to test stage are revealed during the investigation of the change patterns.

The efficient extraction of phthalate acid esters (PAEs) is challenging due to their extremely low concentration, complicated matrices and hydrophilicity. Herein, hol-low microspheres, as an ideal coating, possesses significant potential for solid-phase microextraction (SPME) due to their fascinating properties. In this study, multi-walled carbon nanotubes hollow microspheres (MWCNTs-HMs) were utilized as a fiber coating for the SPME of PAEs from tea beverages. MWCNTs-HMs were ob-tained by dissolving the polystyrene (PS) cores with organic solvents. Interestingly, MWCNTs-HMs well maintain the morphology of the precursors MWCNTs@PS. The layer-by-layer (LBL) assembly of MWCNTs on PS microsphere templates was ac-quired through electrostatic interactions. Six PAEs, di-ethyl phthalate (DEP), di-iso-butyl phthalate (DIBP), di-n-butyl phthalate (DBP), benzyl butyl phthalate (BBP), di-2-ethylhexyl phthalate (DEHP) and di-n-octyl phthalate (DOP), were se-lected as target analytes for assessing the efficiency of the coating for SPME. The stirring rate, sample solution pH and extraction time were optimized by using the Box-Behnken design. Under optimal working conditions, the proposed MWCNTs-HMs/SPME was coupled with gas chromatography-tandem mass spec-trometry (GC-MS/MS) to achieve high enrichment factors (118–2137), wide linearity (0.0004-10 μg L-1), low limits of detection (0.00011-0.0026 μg L-1) and acceptable recovery (80.2%-108.5%) for the detection of PAEs. Therefore, the MWCNTs-HMs coated fibers are promising alternatives in the SPME method for the sensitive detec-tion of PAEs at trace levels in tea beverages.

To study the heat and mass transfer characteristics of an evaporative cooler for cooling high-temperature working fluids such as supercritical carbon dioxide, an experimental device of the evaporative cooler was built, and a corresponding test system was constructed. Then, the experimental study was carried out based on the orthogonal test method, and the law and extent of the influence of water flow in the tube, spray water flow rate, inlet water temperature in the tube, and air flow rate on heat dissipation, cooling efficiency, and heat and mass transfer coefficients have been obtained. The correlation equations of the heat and mass transfer coefficients were obtained by fitting the orthogonal experimental data, and the validity of the correlation equations was confirmed by the verification experiment.

BACKGROUNDː The so-called Low Cardiac Output Syndrome (LCOS) is one of the most common complications in pediatric patients with congenital heart disease undergoing corrective surgery. LCOS requires high concentrations of inotropes to support cardiac contractility and improve cardiac output, allowing for a better systemic perfusion. To date, serum lactate concentrations and central venous oxygen saturation (ScVO2) are the most commonly used perfusion markers, but they are not completely reliable to identify a state of global tissue hypoxia. The study aims to evaluate whether the venoarterial carbon dioxide difference/arterial-venous oxygen difference ratio [P(v-a)CO2/C(a-v)O2] can be a good index to predict the development of LCOS in the aforemen-tioned patients, so as to treat it promptly. METHODSː This study followed a population of 98 children undergoing corrective cardiac surgery from June 2018 to October 2020 at the Department of Cardiac Surgery of University Hospital Inte-grated Trust and their subsequent admission at the Postoperative Cardiothoracic Surgery Intensive Care Unit. During the study, central arterial and venous blood gas analysis were carried out before and after cardiopulmonary bypass (CPB) (pre-CPB and post-CPB), at admission to intensive care unit, before and after extubation and at any time of instability or modification of the patient’s clinical and therapeutic conditions. RESULTSː The data analysis shows that 46.9% of the children developed a LCOS (in line with the current literature) but that there is no statistically significant association between the P(v-a)CO2/C(a-v)O2 ratio and LCOS onset. Despite the limits of statistical significance, however, a 31% increase in the ratio emerged from the pre-CPB phase to the post-CPB phase when LCOS is present. CONCLUSIONSː This study confirms a statistically significant association between the most used markers in adult patients (serum lactate concentration, ScVO2 and oxygen extraction ratio-ERO2) measured in the pre-CPB phase and the incidence of LCOS onset, especially in patients with he-modynamic instability before surgery.

In this work,the wave behaviour propagation of single-walled fluid conveying carbon nanotubes (SWCNT) under longitudinal magnetic fields and elastic foundations is studied, based on the nonlocal strain gradients theory(NSGT). With considertion of surface effect, the governing differential equation are obtained utilising Timoshenko beam theory and Hamilton's variational principle. The influence of small-scale parameters, fluid density, magnetic flux, surrounding elastic medium and surface effects on wave behaviour characteristics of carbon nanotubes are discussed in detail. The numerical results illustrated that with the magnetic flux increase，the phase velocities of the carbon nanotube will increase. When the fluid effects inside the carbon nanotube, the wave frequencies of the system reduces with increase in the non-local coefficient, while promotes with increase in the strain gradient coefficient. In addition, fluid density, surrounding elastic medium and surface effects have meaningful influence on the phase velocity of the system.

We developed a transparent and flexible electrochemical sensor using a platform based on a network of single-walled carbon nanotubes (SWCNTs) for the non-enzymatic detection of hydrogen peroxide (H2O2) released from living cells. We decorated the SWCNT network on a poly(ethylene terephthalate) (PET) substrate with platinum nanoparticles (PtNPs) using a potentiodynamic method. The PtNP/SWCNT/PET sensor synergized the advantages of a flexible PET substrate, a conducting SWCNT network, and a catalytic PtNP, and demonstrated good biocompatibility and flexibility, enabling cell adhesion. The PtNP/SWCNT/PET-based sensor demonstrated enhanced electrocatalytic activity towards H2O2 as well as excellent selectivity, stability, and reproducibility. The sensor exhibited a wide dynamic range of 500 nM to 1 M, with a low detection limit of 228 nM. In addition, Furthermore, the PtNP/SWCNT/PET sensor remained operationally stable even after bending at various angles (15°, 30°, 60°, and 90°) with no noticeable loss of current signal. These outstanding characteristics enabled the PtNP/SWCNT/PET sensor to be practically applied for the direct culture of HeLa cells and the real-time monitoring of H2O2 release by the HeLa cells under drug stimulation.

Among the explored solutions to reduce the environmental impact of the transport sector, Vehicle Integrated Photovoltaics. Thus, we developed a simulation tool of the distance covered by VIPV. It considers various usage patterns and vehicle types, several characteristics of the PV system and all the losses that may decrease energy yield. Focusing on passenger car, simulations indicate the order of influence of the parameters on the outputs of the model: geographic locality, shading, thresholds due to extra-consumption to charge the vehicle battery from PV and frequency of recharge with the grid. With projections of the technology in 2030, with 30 % shading, VIPV cover up to 1444 km yearly distance. This represents up to 12 % of the driven mileage. For the best month, it can get up to 14 km/day. For average Europe and worst-case conditions, VIPV cover only 293 km per year. Life Cycle Assessment (LCA) of solarized passenger car shows negative balance for low-carbon electricity mix and average solar irradiance. In favorable conditions, the carbon footprint is up to 489 kg CO₂-equivalent avoided emissions on 13 years lifespan. Beyond km and LCA focus, VIPV may provide useful functions in non-interconnected zones and for resilience in disaster zones.

In this paper, mesoporous electrodes consisted of Sb-doped SnO2 deposited onto Ti plates controlled corroded under acidic medium were synthesized by spin-coating method, and morpho structurally characterized by X-ray diffraction (XRD) and scanning electron microscopy (SEM). The electrodes were electrochemical testing in the degradation/mineralization by electrooxidation (EO) of doxorubicin (DOX) as single-component and multi-component together with capecitabine (CCB)-from cytostatic class and humic acid (HA)-from natural organic matter (NOM) class in the absence/the presence of activated carbon (AC) as particulate electrode. The best mineralization efficiency of 67 % was achieved for DOX mineralization using Sb-doped SnO2 deposited onto Ti plate controlled corroded with oxalic acid in electrooxidation process. The presence of AC within the electrolysis process generated a synergy effect of 52.75 % for TOC parameter removal, which is in accordance and quite better than the result reported in the literature. The aspects related to the complex mechanism of DOX degradation and mineralization are discussed. The superiority of AC assisted electrooxidation, as electrochemical filtering (EF), was proved considering simultaneous degradation and mineralization of mixture of doxorubicin, capecitabine and humic acid.

Synthesis of a novel series of chromen-3-yl-pyridine moieties. IR, NMR, and MS spectroscopy were used to confirm the structure of these novel compounds and study of antitumor activity of these compounds. The structure activity relationship investigation demonstrated that 2,4-diamino-5-(3-methoxyphenyl)-7-(2-oxo-2H-chromen-3-yl)-1,8-naphthyridine-3-carbonitrile (16), was more effective and naphthyridine-3-carbonitrile derivatives 17, 18 and pyrido[2,3-d]pyrimidine derivative 12, while compounds 5a,b, 9c, 11, 13 and 14 were moderate activity for antitumor activities.

This work aims to investigate the influence of reaction time and catalyst-to-residual fat ratio by catalytic upgrading from pyrolysis vapors of residual fat at 400 °C and 1.0 atmosphere, on the yields of reaction products, physicochemical properties (density, kinematic viscosity, and acid value) and chemical composition of bio-oils, over a catalyst fixed bed reactor of activated carbon pellets impregnated with 10.0 M NaOH, in semi pilot scale. The experiments were carried out at 400 °C and 1.0 atmosphere, using a process schema consisting of a thermal cracking reactor of 2.0 L coupled to a catalyst fixed bed reactor of 53 mL, without catalyst and using 5.0, 7.5, and 10.0% (wt.) activated carbon pellets impregnated with 10.0 M NaOH, in batch mode. Samples of liquid phase products were withdrawn during the course of reaction at 50, 60, 70, 80, 90, 100 and 120 minutes in order to investigate the process kinetics. The physicochemical properties (density, kinematic viscosity, and acid value) of bio-oils were determined by official methods. The chemical composition of bio-oils determined by GC-MS. The thermal catalytic cracking of residual fat show bio-oils yields from 55.55 to 30.22 (wt.%), aqueous phase yields between 2.83 and 3.19 (wt.%), solid phase yields between 13.56 and 9.75 (wt.%), and gas yields from 27.89 to 55.60 (wt.%). The yields of bio-oil decreases from 74.41 to 30.22% (wt.) with increasing catalyst-to-Tallow kernel oil ratio, while that of gaseous phase increases from 12.87 to 55.60% (wt.). For all the thermal and thermal catalytic cracking experiments, the density, kinematic viscosity, and acid value of bio-oils decreases as the reaction time increases varying from 0.9266 to 0.8220 g/cm³, 8.10 to 2.24 mm²/s, and 144.14 to 2.37 mg KOH/g. The GC-MS of liquid reaction products identified the presence of hydrocarbons (alkanes, alkenes, ring-containing alkanes, ring-containing alkenes, and aromatics) and oxygenates (carboxylic acids, ketones, esters, alcohols, and aldehydes). For all the pyrolysis and catalytic cracking experiments, the hydrocarbon selectivity in bio-oil increases with increasing reaction time, while those of oxygenates decrease, reaching concentrations of hydrocarbons up to 95.35% (area.). The best results for the physicochemical properties density, kinematic viscosity, and acid value were 0.8220 g/cm³, 3.03 mm2/s, and 2.37 mg KOH/g, respectively, with a maximum hydrocarbon concentration of 97.194% (area.) and 2.806% ketones (area.) were obtained at 400 °C and 1.0 atmosphere, 80 minutes, without catalyst. For the catalytic cracking experiments, the maximum hydrocarbon content of 75.763% (area.) and 17.041% (area.) carboxylic acids, 4.702% (area.) ketones (area.), and 2.494% (area.) non-identified oxygenates was obtained at 400 °C and 1.0 atmosphere, 90 minutes, using a catalyst fixed bed reactor, with 10.0% (wt.) activated carbon pellets impregnated with 10.0 M NaOH as catalyst.

Creating new business models is crucial for the implementation of clean technologies for industrial decarbonization. With incomplete knowledge of market processes and uncertain conditions, assessing the prospects of a technology-based business model is challenging. This study combines business model innovation, system dynamics and exploratory model analysis to identify new business opportunities in a context of socio-technical transition and assess their prospects through simulation experiments. Furthermore, insights are visualized in a roadmap to coordinate action among the actors involved. This combination of methods is applied to the case of a business model aiming at ensuring stability of the electrical grid by centralizing the management of flexible loads in industrial companies. A system dynamics model was set up to simulate the diffusion of flexible electrification technologies. Through scenario definition and sensitivity analysis, the influence of internal and external factors on diffusion was assessed. Results highlight the central role of energy costs and customer perception. The chosen combination of methods allowed the formulation of concrete recommendation for coordinated action, explicitly accounting for the various sources of uncertainty. We suggest testing this approach in further business model innovation contexts.

This study aligns with the 2030 United Nations Sustainable Development Goals- 3 which aim to “ensure healthy lives and promote well-being for all at all ages”. It contributes to the nascent literature stream on energy-health dynamics by introducing a holistic theoretical model to empirically examine the mediation effect of carbon emissions on the relationship between nonrenewable energy and infant mortality rates. Using an unbalanced panel data on 42 Asia and the Pacific countries from 2005 to 2015 and deploying the structural equation modeling approach, the empirical results are surmised as follows: (i) in regard to the full sample of countries, nonrenewable energy indirectly increases infant mortality rates through increasing carbon emissions. In other words, carbon emissions play a partial mediation role between nonrenewable energy and infant mortality rates; and (ii) for the different income groups, carbon emissions show varying mediation effects. For example, the mediation effects of carbon emissions in lower-middle and upper-middle income countries are found to be similar to those of the full sample of countries. Therefore, based on these findings, we conclude that nonrenewable energy is an essential determinant of infant mortality rates. Policy recommendations are put forward.

Pd-Pt catalysts supported on carbon preheated to 1600°C have been reinvestigated in CHFCl2 hydrodechlorination. An additionally adopted catalyst oxidation at 350-400°C produced an order of magnitude increase in the catalytic activity of Pd/C. This increase is not caused by changes in metal dispersion or possible decontamination of the Pd surface from superficial carbon, but rather by unlocking the active surface, originally inaccessible in metal particles tightly packed in the pores of carbon. Burning carbon from the pore walls attached to the metal changes the pore structure, providing easier access for the reactants to the entire palladium surface. As upon calcination the performance of the rest of the Pd-Pt/C catalysts changes less than for Pd/C, the relation between the turnover frequency and alloy composition does not confirm the Pd-Pt synergy invoked in our previous work. The use of even higher-preheated carbon (1800°C), completely free of micropores, results in a Pd/C catalyst that does not need to be oxidized to achieve high activity and excellent selectivity up to CH2F2 (>90%).

Air change rate is an important parameter for quantification of ventilation heat losses and also affects the indoor climate of buildings. Indoor air quality is significantly associated with ventilation. If air change isn't sufficient, trapped allergens, pollutants and irritants can degrade the indoor air quality and affect the well-being of a building's occupants. Many studies on ventilation and health have concluded that lower air change rates can have a negative effect on people’s health and low ventilation may result in an increase in allergic diseases. Quantification of air change rate is complicated, since it is affected by a number of parameters, of which the one of the most variable is the air-wind flow. This study aims to determination and comparison of values of the air change rate in two methods - by quantifying of aerodynamic coefficient Cp = Cpe - Cpi – so called aerodynamic quantification of the building and the methodology based on experimental measurements of carbon dioxide in the selected reference room in apartment building.

Creating new business models is crucial for the implementation of clean technologies for industrial decarbonization. With incomplete knowledge of market processes and uncertain conditions, assessing the prospects of a technology-based business model is challenging. This study combines business model innovation, system dynamics and exploratory model analysis to identify new business opportunities in a context of socio-technical transition and assess their prospects through simulation experiments. Furthermore, insights are visualized in a roadmap to coordinate action among the actors involved. This combination of methods is applied to the case of a business model aiming at ensuring stability of the electrical grid by centralizing the management of flexible loads in industrial companies. A system dynamics model was set up to simulate the diffusion of flexible electrification technologies. Through scenario definition and sensitivity analysis, the influence of internal and external factors on diffusion was assessed. Results highlight the central role of energy costs and customer perception. The chosen combination of methods allowed the formulation of concrete recommendation for coordinated action, explicitly accounting for the various sources of uncertainty. We suggest testing this approach in further business model innovation contexts.

Soil CO2 efflux (FCO2) plays a dominant role in the terrestrial carbon (C) cycle but interpreting constraints on local observations is impeded by challenges in disentangling belowground CO2 sources. Trees contribute most C to forest soils, so linking aboveground properties to FCO2 could open new avenues to study plant-soil feedbacks and facilitate scaling; furthermore, FCO2 responds dynamically to meteorological conditions, complicating predictions of total FCO2 and forest C balance. We tested for proximity effects of individual Acer saccharum Marsh. trees on FCO2, comparing FCO2 within 1 m of mature stems to background fluxes before and after an intense rainfall event. Wetting significantly increased background FCO2 (6.4±0.3 vs. 8.6±0.6 s.e. μmol CO2 m-2s-1), with a much larger enhancement near tree stems (6.3±0.3 vs. 10.8±0.4 μmol CO2 m-2s-1). FCO2 varied significantly among individual trees and post-rain values increased with tree diameter (with a slope of 0.058 μmol CO2 m-2s-1 cm-1). Post-wetting amplification of FCO2 (the ‘Birch effect’) in root zones often results from the improved mobility of labile carbohydrates and further metabolization of recalcitrant organic matter, which may both occur at higher densities near larger trees. Our results indicate that plant-soil feedbacks change through tree ontogeny and provide evidence for a novel link between whole-system carbon fluxes and forest structure.

Soil organic carbon (SOC) sequestration in arable soils is a challenging goal for soil management. Multiple factors should be considered for the prediction of the soil capacity to fix atmospheric carbon. In this study, we focused on the effect of crop rotation and previous land use for future carbon sequestration on two experimental fields with identical soils (Retisols) and input of organic fertilizers. We analyzed the SOC dynamics and used the Roth C model to forecast SOC changes under RCP4.5 and RCP8.5 scenarios. Our experimental and modelling results indicated a consistent increase in SOC stocks and the stable fractions of soil organic matter (SOM). The increase in SOC was higher in the experiment with the crop-grassland rotation that in the experiment with a rotation of row crops and barley. With similar total SOC stocks, the efficiency of soil management differed as reflected by the contrasting composition of SOM, as fields with a long cultivation history showed higher SOM stability. The goal of 4‰ annual increase of SOC stocks may be reached under crop- grassland rotation in 2020-40 and 2080-90 when applying mineral or organic fertilizer system for scenario RCP4.5, and mineral fertilizer system in 2080-2090 for scenario RCP8.5.

With the development of energy internet, integrated energy system can effectively reduce carbon emissions and improve the utilization of renewable energy. In this paper, a low-carbon optimal scheduling model of integrated energy system considering heat loss of heat network pipeline is proposed. Based on the study of concentrating solar power (CSP) plant and heat storage tank (HS), an optimal scheduling model is established, which takes system operation cost, environmental pollution and penalty cost of abandoning wind and solar energy as objectives. Through the analysis of example results, it is proved that the model proposed in this paper can achieve the goal of reliable, low-carbon and economic operation of the system. At the same time, it shows that CSP unit can reduce the operation cost of system and increase energy coupling and utilization.

Biochar application to the soil can improve soil quality and nutrient leaching loss. Recent studies have reported that surficial application of biochar to stored swine manure can reduce emissions of odorous compounds and reduce the volatilization loss of ammonia. Our working hypothesis was that the biochar-treated manure application to the soil would decrease nutrient leaching from manure and increase plant-available nutrients. The study objectives were to evaluate the impact of biochar-treated swine manure on soil total C, N, and other major and minor nutrients. Three biochars (i) neutral pH red-oak (RO), (ii) highly alkaline autothermal corn (Zea mays) stover (HAP), and (iii) mild acidic Fe-treated autothermal corn stover (HAPE) were incubated with swine manure for a month. The biochar-manure mixture was applied in triplicate to soil columns with application rate determined by the P₂O₅-P content in manure or manure-biochar mixtures after the incubation. The ammonium (NH₄⁺), nitrate (NO₃^-), and reactive P concentrations in soil column leachates were recorded for eight leaching events. Soil properties and plant-available nutrients were compared between treatments and control manure & soil. Manure-(HAP&HAPE) biochar treatments significantly increased soil organic matter (OM) and increased soil total C, N, and improved soil bulk density. Concentrations of KCl-extractable NH₄⁺ and NO₃^- significantly increased in HAPE column leachates during this 4-week study and in the soil after the experiment. A significant reduction in soil Mehlich3 Cu was also observed for the manure-HAPE mixture compared with the control. Overall, the manure-biochar incubation enabled biochar to sorb nutrients from manure, and the subsequent manure-biochar mixture application to soil improved soil quality and plant nutrient availability in comparison to conventional manure application to soil. This proof-of-the-concept study suggests that biochars could be used to solve both environmental and agronomic challenges and further improve the sustainability of animal and crop production agriculture.

Bioenergy with carbon capture and sequestration (BECCS) is one strategy to remove CO2 from the atmosphere. To assess the potential scale and cost of CO2 sequestration from BECCS in the US, this analysis models carbon efficiencies and costs of biomass production, delivery, power generation, and CO2 capture and sequestration in saline formations. The analysis includes two biomass supply scenarios (near-term and long-term), two biomass logistics scenarios (conventional and pelletized), two generation technologies (pulverized combustion and integrated gasification combined cycle), and three cost accounting scenarios (gross cost, net cost after electricity revenues, and net cost after electricity revenues with avoided emissions from conventional power generation). Results show cost Mg-1 CO2 as a function of CO2 sequestered (simulating capture up to 90% of total CO2 sequestration potential) and associated spatial distribution of resources and generation locations for the array of scenario options. Under a near-term scenario using 222 million Mg yr-1 of biomass, up to 196 million Mg CO2 can be sequestered at scenario-average costs ranging from $60 to $158 Mg 1 CO2; under a long-term scenario using 823 million Mg yr-1 of biomass, up to 727 million Mg CO2 yr 1 can be sequestered at scenario-average costs ranging from $32 to $242 Mg-1 CO2. These costs are largely influenced by cost accounting scenario, and the CO2 sequestration potential may be reduced if future competing demand reduces resource availability. Results suggest there are multiple feedstock-logistics-generation pathways toward CO2 drawdown that could be incrementally trialed and monitored for environmental sustainability effects. Interactive visualization of results is available at [final link to be determined].

The sustainable production of food faces formidable challenges. Foremost is the availability of arable soils, which have been ravaged by the overuse of fertilizers and detrimental soil management techniques. As such, maintenance of soil quality, and reclamation of marginal soils, has become an increasingly important endeavor. Recently, there has been emerging interest in the use of biochar, a carbon rich, porous material thought to improve various aspects of soil performance. Biochar (BC) is produced through the thermochemical decomposition of organic matter at high temperature in an oxygen limited environment, in a process known as pyrolysis. Importantly, the source of organic material, or ‘feedstock,’ used in this process and different parameters of pyrolysis, especially temperature, determine the chemical and physical properties of biochar. Incorporation of BC impacts soil-water relations, tilth and nutrient status, pH, soil organic matter (SOM), and microbial activity. Soil amendment with BC has been shown to have an overall positive impact on soil health and crop productivity; however, initial soil properties need to be considered prior to the application of BC. There is an urgent need to understand the effects of long-term field application of BC and how it influences the soil microcosm. This knowledge will facilitate predictable enhancement of crop productivity and meaningful carbon sequestration.

What proportions should a thermally massive building have? How should the thermal mass be distributed? Should the "massing" change with the choice of material? This paper shows how to optimize the physical proportions of a building so that it synchronizes ambient heat exchanges in a natural feedback cycle. The internal mass is thermally coupled with buoyancy ventilation; the cycle is driven by the daily swing of outdoor temperature. Tripling up functions in this way—so that structural materials can reliably cool and power the ventilation for buildings—could help decarbonize the construction industry and provide an effective strategy for adapting to life-threatening heatwaves. Based on harmonic analysis, the method allows designers to thermally tune the form and mass of a building to meet chosen targets for temperature and ventilation in free-running mode. Once the optimal balance of exchange rates is known, design teams can proportionally vary the building height and ventilation openings against the surface area and thickness of an internal thermal mass. The possible permutations are infinite but parametrically constrained, allowing teams to fairly compare the functional and environmental credentials of different construction materials while they produce and evaluate preliminary options for organizing the exterior form and interior spaces of a building. An example study suggests that thin-shell structures of minimum weight, and even timber buildings, may be optimally tuned to produce ample ventilation and temperature attenuation.

Nanoparticles of platinum-group metals (PGM) on carbon supports are widely used as catalysts for a number of chemical and electrochemical conversions on laboratory and industrial scale. The newly emerging field of single atom catalysis focuses on the ultimate level of metal dispersion, i.e. atomically dispersed metal species anchored on the substrate surface. However, the presence of single atoms in traditional nanoparticle-based catalysts remains largely overlooked. In this work we use aberration-corrected scanning transmission electron microscope to investigate four commercially available nanoparticle-based PGM/C catalysts (PGM = Ru, Rh, Pd, Pt). We show that in addition to nanoparticles, single atoms are also present on the surface of carbon substrates. These observations raise questions about the role that single atoms play in conventional nanoparticle PGM/C catalysts. We critically discuss the observations with regard to the quickly developing field of single atom catalysis.

Sustainability in material purchasing is a growing area of research. Goods purchasing decisions strongly affect transportation path flows, vehicle consolidation, inventory levels and related obsolescence costs. Within a global sourcing context, companies experience the need of new decision making approaches capable to consider a large variety of factors, also linked with society and environment. Environmental impact assessment has become a key requirement for materials purchasing and transportation decisions since global warming is a rising concern both in academic and industrial researches. In fact, it is well known that the freight transport industry is responsible for large amounts of carbon emissions contributing to global warming. In this paper, we initially analyse and compare the environmental economic policies established by the International Governments in relation to the carbon trading systems adopted. Then, we develop a multi-objective lot sizing approach useful in practice to define the sustainable quantity to purchase when a Cap and Trade mitigation policy is present. We further analyse the model behaviour according to different carbon price values by demonstrating that carbon prices are still far too low to motivate managers towards sustainable purchasing choices.

A potassium bromide KBr material, which has been widely used as the key element in the Fourier spectrometers and as the output window of the IR-lasers, has been studied with good advantage via applying the carbon nanotubes in order to modify the potassium bromide surface. The laser oriented deposition method has been used to place the carbon nanotubes at the matrix material surface in the vertical position at the different electric field varied from 100 to 600 V×cm-1. The main idea of the improvement of the spectral properties of the potassium bromide structure is connected with the fact that the refractive index of the carbon nanotubes is substantially less than the refractive index of the studied material, and the small diameter of the carbon nanotubes allows one to embed these nano-objects in the voids of the lattice of the model matrix systems. Moreover, the mechanical characteristics and wetting features of the potassium bromide structures have been investigated under the condition mentioned above. Analytical and quantum-chemical simulations have supported the experimental results.

A new type of low cost, environmentally friendly wood-plastic composites (WPC) containing carbon nanotubes(CNT)of low content 0%, 0.05wt%, 0.1wt% and 0.15wt%, wood fibers of 14wt% and polymer PES of 86wt% was manufactured by the selective laser sintering (SLS) approach of 3D printing. The experimental results showed that the incorporating of CNTs could obviously increase the mechanical properties of the wood/PES composites material. The tensile strength, bending strength and elasticity modulus were 76.3%, 227.9% and 128.7% higher with 0.1wt% CNTs than without CNTs. And the mechanical properties of specimens firstly increased and then decreased with the increasing contents of CNTs. The SEM results of the specimens’ fracture morphology indicated that the preferable bonding interfaces between wood flour grains and PES grains were achieved by adding CNTs to the composites. There are two reasons to explain why the composites possessed the superior mechanical properties: CNTs could facilitate the laser sintering process of wood plastic composites due to their thermal conductivities; also, CNTs could directly reinforce the WPC composites as reinforcement.

The use of braces equipped with dissipative devices for the seismic strengthening of seismically prone reinforced concrete frames is among the most widespread methods, as it allows for high reductions in seismic vulnerability with inexpensive, quickly executed interventions, which can often be carried out mainly by the exterior, resulting in interruptions of use limited both in time and to only small portions of the building. The design methods of dissipative devices are based on the extensive use of pushover analyses capable of highlighting the structural deficiencies of the building and comparing the performances achievable by developing designs according to different methods and sizing criteria. In the present work, with reference to a case study represented by a four-story spatial frame having characteristics representative of design and construction common practice of the 1970s in Southern European countries, the performance of three different design methods is compared. The examined procedures differ for: methods for estimating the peak displacement response of the nonlinear systems, namely the well-known Equal Displacement Rule and the Equivalent (secant) stiffness and Damping rule; and criteria for distributing stiffness and strength of the bracing along the height, namely the distribution of stiffness and strength proportionally to those of the frame, and methods that vary the stiffness and strength along the height in order to minimize the eventual irregularity in elevation of the bare frame. The effectiveness of the procedures is checked by static pushover analysis and nonlinear response history analysis, performed by unidirectional and bidirectional input. Pros and cons of each procedure are summarized, all of them able to provide bracing designs that meet the performance requirements set during the design phase.

The development of a rapid, sensitive, specific method for detecting foodborne pathogens is paramount in supplying safe food to enhance public health safety. Despite the significant improvement in pathogen detection methods, key issues are still associated with rapid methods, such as distinguishing living cells from dead, pathogenic potential or health risk of the analyte at the time of consumption, detection limit, and sample-to-result. The mammalian cell-based assays analyze pathogens' interaction with the host cells and are responsive to only live pathogens or active toxins. In this study, a human embryonic kidney (HEK293) cell line expressing Toll-Like Receptor 5 (TLR-5) and chromogenic reporter system (HEK dual hTLR5) was used for the detection of viable Salmonella in a 96-well tissue culture plate. This cell line responds to low concentrations of TLR5 agonist flagellin. Stimulation of TLR5 ligand activates nuclear factor-kB (NF-κB) - linked alkaline phosphatase (AP-1) signaling cascade inducing the production of secreted embryonic alkaline phosphatase (SEAP). With the addition of a ρ-nitrophenyl phosphate as a substrate, a colored end product representing a positive signal is quantified. The assay's specificity was validated with the top 20 Salmonella enterica serovars and 19 non-Salmonella spp. The performance of the assay was also validated with spiked food samples. The total detection time (sample-to-result), including shortened pre-enrichment (4 h) and selective enrichment (4 h) steps with artificially inoculated outbreak-implicated food samples (chicken, peanut kernel, peanut butter, black pepper, mayonnaise, and peach) was 15 h when inoculated at 1–100 CFU/25 g sample. These results show the potential of HEK-DualTM hTLR5 cell-based functional biosensors for rapid screening of Salmonella.

Plug and abandonment (P&A) operations demand valuable time and resources with operational procedures and materials to establish the well barrier element. This study proposes the use of a water-based fluid as a liquid well barrier element for temporary abandonment, based on esti-mates of its lifespan and survival probability on downhole conditions acquired through acceler-ated life tests. For that, the water-based formulation was tested and exposed to 95, 110, 140 and 150°C, for time intervals ranging from 1 to 10 days. After temperature exposure, the fluid prop-erties were verified and failure was detected by accounting for any deterioration in rheological parameters and/or a substantial increase in filtrate volume. Statistical analysis of failure data was performed in R software using Weibull Model, and fluid average lifespan and survival probability were estimated for P&A temperatures. The results obtained demonstrates that the degradation of the fluid only was observed for 140 and 150°C and is mostly related to the CMC deterioration. The reliability metrics shows that until 80°C the fluid is a promising alternative for temporary abandonment, with no need of monitoring. For higher downhole temperatures, theses metrics should be considered to define the maximum time of operation and requirements for monitoring management in the operation design.

Perennial ryegrass (Lolium perenne) is an underutilized lignocellulosic biomass that has several benefits such as high availability, renewability, and biomass yield. The grass press-juice obtained from the mechanical pretreatment can be used for the bio-based production of chemicals. Lactic acid is a platform chemical that has attracted consideration due to the broad area of applications. For this reason, a more sustainable production of lactic acid is expected to increase. In this work, lactic acid was produced using complex medium at bench- and reactor scale, and the results were compared to those obtained using an optimized press-juice medium. Bench-scale fermentations were carried out in a pH-control system and lactic acid production reached about 22 g/L in com-plex medium, and 26.61 ± 1.2 g/L in press-juice medium. In bioreactor, the production yield was 0.91 ± 0.07 g/g, corresponding to 1.4-fold increase respect to the complex medium with fructose. As comparison to the traditional ensiling process, whole grass fractions of different varieties har-vested in summer and autumn was performed. Ensiling showed variations in lactic acid yields, with a yield up to 15.2% dry mass for the late-harvested samples, surpassing typical silage yields of 6-10% dry mass.

The yield of logging in a forest management area using only the volume variable is not sufficient to assess effective wood productivity. The objective was to evaluate production using different logging variables to point out possible divergences between what was planned and what was actually carried out, in order to indicate adjustments to annual operating plans (AOPs) in the Amazon. The database comes from a community forest management area in the Tapajós National Forest, con-sidering three sources: 100% inventory, AOP planning and the post-exploration report. The pro-duction yield considering the number of trees felled and the effective volume were compared with the AOP planning. The volume estimated by the generic equation was compared with the cubed volume and the commercial height variable was evaluated. Of the 6,267 trees selected, 5,090 trees were harvested (3.2 trees.ha-1), indicating a yield of 85.0% of the estimated volume. Of the 25 commercial species logged, the most representative were: Manilkara huberi, Vochysia maxima, Lec-ythis lurida, Couratari guianensis and Hymenaea courbaril. It can be concluded that the number of trees, cubed volume and commercial height are post-exploration yield response variables and not just volume, as is currently used in SFMPs.

Frailty is a common condition in older adults that negatively impacts health and quality of life. This study evaluated a comprehensive, personalized, and coordinated intervention under the Value-Based Care approach to address frailty's multidimensional nature in older people. It em-ployed a pre-post randomized controlled design, involving frail individuals over 65 years in Valencia, Spain. Assessments were conducted at baseline, 12 months (immediately post-intervention), and 18 months. The intervention included a personalized care plan supported by technology, with monthly motivational follow-ups and plan updates by health professionals and participants. Outcomes were measured using an assessment questionnaire that included the International Consortium for Health Outcomes Measurement dataset for the older population: physical health, physical functioning, general mental health, satisfaction with social activities and relationships, ability to carry out usual social roles and activities, pain, general quality of life, loneliness, physical frailty, psychological frailty and social frailty. The study found signifi-cant improvements in physical frailty, quality of life, reduced health resource use and hospitali-zations, and lower levels of pain and depression/anxiety compared to baseline. The findings suggest further research into value-based care approaches, emphasizing the development and activation of personalized, comprehensive programs for older individuals with frailty.

Cancer cells are believed to be recognized and controlled by the immune system of the host, especially the adaptive immune system. Cancers may be initiated by a single gene defect in stem cells or regenerating cells during the replacement of old or injured tissue cells, and additional gene defect(s) then occur. To compensate for missing proteins intracellular homeostasis systems, such as proteostasis, may work with activation of new genes. The risk or predisposing factors in cancers may be related to the number of tissue cell renewals (the turnover time) and the probability of gene defect(s). Oncological symptoms may be associated with substances that are derived from antigens expressed on cancer cells and those derived from damaged cancer cells with corresponding immune reactions. In patients with cancers or other conditions, inadequate immune components to control these substances may result in chronic inflammatory or autoimmune diseases. Cancer cells communicate with host cells, including the immune cells such as T cells, for survival and proliferation and may need assistance from immune cells against intracellular infections or physicochemical insults. T cell-based immune therapeutics have been developed and clinically used in some cancers, especially in advanced cancers. We further discuss the oncogenesis, pathophysiology of cancers, and unresolved issues in tumor immunology and reinterpret the effectiveness of T cell-based therapies under the protein-homeostasis-system hypothesis.

Accurate measurement of water vapor is essential for researches and applications of meteorology, climatology and hydrology. Based on the BeiDou PPP-B2b service, real-time precipitable water vapor (PWV) is retrieved with the precise point positioning (PPP) software. The experiment was conducted in Beijing in January, 2023. Three solutions are designed with PPP using the BeiDou system only, the GPS system only and the BeiDou-GPS combined solution. Real-time PWVs for the 3 solutions are validated with the ERA5 reanalysis data. Between PWVs from single BeiDou and ERA, there are a bias of 0.7 mm and a RMSE of 1.8 mm. While for the GPS case, the bias is 0.73 mm and RMSE is 1.97 mm. The biases are less than 1 mm and RMSEs are less than 2mm. Both the BeiDou and the GPS processing performs very well. But little improvement is found for the BeiDou-GPS combined solution, comparing with the BeiDou system only and the GPS system only solution. This may be due to the poor handling of two different kinds of errors for the GPS and the BeiDou systems in our PPP software. A better PWV estimation with the two systems is to estimate PWV with single system at first step, and then to obtain the optimization by Bayesian model averaging.

The rapid evolution of technology has reshaped the global economy, intensifying competition and prompting industry players to embrace innovation for sustained competitiveness. In Indonesia, Business Incubators, overseen by Universities and Provincial/City Governments, are pivotal. However, understanding the determinants of Business Incubator Performance remains limited. This study investigates 24 statement items aiming to uncover the factors influencing Technology Business Incubators in Indonesia, with a focus on knowledge management as a mediating variable derived from a knowledge-based perspective. Employing a quantitative approach, Structural Equation Modeling (SEM) is utilized to scrutinize these factors' impact on Business Incubator Performance. The findings reveal that knowledge management fully and partially mediates between funding support, government assistance, incubator governance, and Business Incubator Technology Performance. This research offers valuable insights for entrepreneurs and stakeholders, emphasizing the significance of funding, governmental backing, incubator governance, and knowledge management in enhancing Incubator Business Technology Performance in Indonesia.

YouTube is a very popular social media platform in today’s digital landscape. The primary focus of this study is to explore the underlying sentiment in user comments on food-related videos on YouTube, specifically within two pivotal food product categories: Plant-Based and Hedonic. Our research involved labelling comments using sentiment lexicons such as TextBlob and VADER. Furthermore, the sentiments of the comments were classified using advanced Machine Learning (ML) algorithms, namely Support Vector Machine (SVM), Multinomial Naive Bayes, Random Forest, Logistic Regression, and XGBoost. The evaluation of these models encompassed key macro average metrics, including accuracy, precision, recall, and f1-score. Results from VADER showcased a high accuracy level, with SVM achieving 93% accuracy in the plant-based dataset and 96% in the hedonic dataset. In addition to sentiment analysis, we delved into user interactions within the two datasets, measuring crucial metrics such as views, likes, comments, and engagement rate. The findings illuminate significantly higher levels of views, likes, and comments in the hedonic food dataset, but the plant-based dataset maintains a superior overall engagement rate.

Despite the advances in stent design, coating drugs, polymers, the use of intravascular imaging, special devices (such as intravascular lithotripsy, ultra-high-pressure non-compliant balloons, and drug-coated balloons), and improved operator skills and techniques, the problem of in-stent restenosis (ISR) and stent thrombosis (ST) remains a challenge for interventional cardiologists. Atherosclerotic lesions, ISR, and ST, are systemic metabolic chronic inflammatory illnesses with a local manifestation in the coronary vessels. Therefore, we propose managing these systemic illnesses systemically through plant-based diet (PBD) intervention. We explain how PBD intervention can help mitigate atherosclerosis, ISR, and ST. Thus, PBD intervention can overcome biological and epigenetic problems in managing ISR and ST similarly to reversing atherosclerosis. Our team is pioneering the implementation of PBD for cardiology patients who have undergone coronary intervention. So far, the results have been very promising. We hope that in the future, many research studies will be performed to investigate further the role of PBD in decreasing the incidence of ISR and ST.

The PAX8/PPARγ rearrangement, producing the PAX8–PPARγ fusion protein (PPFP), is thought to play an essential role in the oncogenesis of thyroid follicular tumors. To identify PPFP-targeted drug candidates and establish an early standard of care for thyroid tumors, we performed ensemble docking-based compound screening. Specifically, we investigated which pocket structure should be adopted to search for a promising ligand compound for the PPFP; the position of the ligand binding pocket on the PPARγ side of the PPFP is common to that of PPARγ; however, the shape is slightly different between them due to environmental factors. We developed a method for selecting a PPFP structure with a relevant pocket and high prediction accuracy for ligand binding. This method was validated using PPARγ, whose structure and activity values are known for many compounds. Then, we performed docking calculations to PPFP for 97 drug or drug-like compounds with a thiazolidine backbone, which is one of the characteristics of ligands that bind well to PPARγ, registered in the DrugBank database. Furthermore, the binding affinities of promising ligand candidates were estimated more reliably using the molecular mechanics-Poisson–Boltzmann surface area method. Thus, we propose promising drug candidates for the PPFP with a thiazolidine backbone.

Abstract: Lipidic carriers employed for the entrapment, encapsulation and precise targeting the bioactive compounds offer significant advantages to the biomedical, pharmaceutical, nutraceutical and cosmeceutical industries. These lipid and phospholipid-based vesicles can improve the solubility, permeation, pharmacokinetics, pharmacodynamics and bioavailability of the encapsulated material. As a matter of fact, nano-sized phospholipid vesicles have attained the highest success rate in commercialization among the available drug encapsulation technologies. Nevertheless, developing industrially acceptable products still remains a challenge. Among the main disadvantages of conventional lipidic carriers are their instability in the biological environments, and their sensitivity to changes in the pH, temperature and enzymes. To overcome these problems, the new generation of phospho/lipid-based carrier systems was developed using ingredients from microorganisms living in extremely harsh environments (i.e., archeaeobacteria). The present entry aims to review unique features of archaeolipids and recent developments in their application for the encapsulation of genetic material as well as their role and function as adjuvants and vaccine candidates.

Objectives: The study aimed to develop a deep learning-based edge AI model deployed on electrocardiograph (ECG) devices for real-time detection of atrial fibrillation (AF)-risk during sinus rhythm (SR) using standard 10-second 12-lead electrocardiograms (ECGs). Methods: A novel approach was used to convert standard 12-lead ECGs into binary images for model input, and a lightweight convolutional neural network (CNN)-based model was trained using data collected by the Japan Agency for Medical and Research Development (AMED) between 2019 and 2022. Patients over 40 years old with digital, SR ECGs were retrospectively enrolled and divided into AF and non-AF groups. Data labeling was supervised by cardiologists. The dataset was randomly allocated into training, validation, and internal testing datasets. External testing was conducted on data collected from other hospitals.　Results: The best-trained model achieved an AUC of 0.82 and 0.80, sensitivity of 79.5% and 72.3%, specificity of 77.8% and 77.7%, precision of 78.2% and 76.4%, and overall accuracy of 78.6% and 75.0% in the internal and external testing datasets, respectively. The deployed model and app package utilized 2.5MB and 40MB of the available ROM and RAM capacity on the edge ECG device, correspondingly. Processing time for AF-risk detection was approximately 2 seconds.　Conclusion: The model maintains comparable performance and improves its suitability for deployment on resource-constrained ECG devices, thereby expanding its potential impact to a wide range of healthcare settings. Its successful deployment enables real-time AF-risk detection during SR, allowing for timely intervention to prevent AF-related serious consequences like stroke and premature death.

In this study, a simple machine learning-based quantitative structure-activity relationship (QSAR) model was developed to predict the inhibitory potency (pIC50 values) of FLT3 tyrosine kinase inhibitors, pivotal in treating Acute Myeloid Leukemia (AML). Distinctively, our model leverages an extensive and diverse dataset, 14 times larger than those employed in prior studies within this field, enabling an unparalleled scope of compound analysis. This vast dataset, combined with further exploration of molecular descriptors, enabled predictions of extraordinary precision, covering a broader spectrum of FLT3 inhibitors than was previously possible. The Random Forest Regressor (RFR) algorithm, selected for its superior predictive performance, was trained with 1080 inputs and validated through comprehensive external and internal methods. It achieved an remarkable coefficient of determination (R^2) of 0.941 and a standard deviation of 0.235 on a test set of 270 compounds, highlighting the efficacy of model in predicting FLT3 inhibitory activity. Key molecular descriptors were identified, enhancing our understanding of structural requirements for inhibitor potency. Additionally, we developed a user-friendly computational tool that enables the rapid prediction of pIC50 values. Utilizing this tool, potential FLT3 inhibitors were identified through ligand-based virtual screening. This study represents a major advancement in FLT3 inhibitor discovery by utilizing a simple QSAR-machine learning model. It enables more efficient and precise identification of potential drug candidates at an early stage, promising a faster development of targeted therapies and streamlining the ligand-based drug design process.

The need for industrial and commercial machinery to maintain high torque while accurately following a variable angular speed is increasing. To meet this demand, induction motors (IMs) are commonly used with variable speed drives (VSDs) that employ a field-oriented control (FOC) scheme. Over the last thirty years, IMs have been replacing independent connection direct current motors due to their cost-effectiveness, reduced maintenance needs, and increased efficiency. However, IMs and VSDs exhibit nonlinear behavior, uncertainties, and disturbances. This paper proposes a robust combined adaptive passivity-based control (CAPBC) for this class of nonlinear systems that applies to angular rotor speed and stator current regulation inside an FOC scheme for IMs´ VSDs. It uses general Lyapunov-based design energy functions and adaptive laws with σ-modification to assure robustness after combining control and monitoring variables. Lyapunov’s second method and Barbalat Lemma prove that the control and identification error tends to be zero over time. Moreover, comparative experimental results with standard Proportional Integer controller (PIC) and direct APBC, show the proposed CAPBC effectiveness and robustness under normal and changing conditions

Using the potent tools of information geometry (IG), the current research analyses the information geometrics of the five-dimensional HTBFC Manifold. By enabling a unique revolutionary analysis for the performance of Human-Trust Based Feedback Control (HTBFC), the integration of information geometry into HTBFC facilitates the relativistic analysis of the five-dimensional HTBFC manifold. Both the two- and three-dimensional manifold subcases are investigated. This work provides a ground-breaking approach to IG unification of Human- Machine Interactions (HMIs). Some potential applications of HTBFC to robotics are presented.Conclusions are provided, along with some emerging open problems and recommendations for future research.

The task of human action recognition (HAR) based on skeleton data is a challenging yet crucial technique owing to its wide-ranging applications in numerous domains, including patient monitoring, security surveillance, and observation of human-machine interactions. While numerous algorithms have been proposed in an attempt to distinguish between a myriad of activities, most practical applications necessitate highly accurate detection of specific activity types. This study proposes a novel and highly accurate spatiotemporal graph autoencoder network for HAR based on skeleton data. Furthermore, an extensive investigation was conducted employing diverse modalities. To this end, a spatiotemporal graph autoencoder was constructed to automatically learn both spatial and temporal patterns from human skeleton datasets. The powerful graph convolutional network, designated as GA-GCN, developed in this study, notably outperforms the majority of existing state-of-the-art methods when evaluated on two common datasets, namely NTU RGB+D and NTU RGB+D 120. On the first dataset, the proposed approach achieved accuracies of 92.3\% and 96.8\% for the cross-subject and cross-view evaluations, respectively. On the more challenging NTU RGB+D 120 dataset, GA-GCN attained accuracies of 88.8\% and 90.4\% for the cross-subject and cross-set evaluations, respectively.

AbstractBackground: Empowering Communities, Combating HIV/AIDS: The Indispensable Role of Community-Based Strategies in Awareness and PreventionObjective: Highlight the significance of community-based approaches in addressing the ongoing HIV/AIDS pandemic through enhanced awareness, reduced stigma, and effective prevention measures.Method: This article provides a theoretical analysis of community-based HIV/AIDS interventions, drawing upon key strategies and successful examples from diverse settings.Results: Community-based initiatives emerge as critical factors in empowering individuals and communities to take ownership of their health. Through culturally-sensitive education, accessible testing, and comprehensive support services, these initiatives effectively diminish the social stigma surrounding HIV/AIDS and promote preventative behaviors. Examples of successful programs illustrate the potential of community-driven approaches to achieve measurable reductions in transmission rates and improved health outcomes.Conclusion: The fight against HIV/AIDS necessitates robust community engagement. Community-based interventions, grounded in local knowledge and addressing specific needs, offer a promising pathway towards fostering health equity, reducing stigma, and ultimately curtailing the spread of the virus. Further research and investment in these initiatives are crucial to ensuring their scalability and sustained impact on global HIV/AIDS prevention efforts.

This study explores the interplay between web-based professional development, teachers' self-efficacy, resilience, and work values in the context of the Davao Region. The investigation reveals significant findings in three key areas: web-based professional development, teachers' resilience, and teachers' work values. The survey indicates that web-based professional development significantly impacts teachers' self-efficacy, with interaction self-efficacy registering the highest mean score. Moreover, teachers' resilience is manifested across emotional, motivational, and social dimensions, with emotional resilience scoring the highest mean. Teachers' work values, encompassing physical facilities, social environment, job security, new challenges, professional growth, personal growth, and interpersonal relationships, all demonstrate high descriptive equivalents. The study establishes a significant positive correlation between web-based professional development and teachers' self-efficacy, as well as a significant relationship between teachers' resilience and their work values. Furthermore, both web-based professional development and teachers' resilience exert a significant influence on teachers' work values.The conclusions drawn from the study highlight the prominence of web-based professional development, teachers' resilience, and work values in the educational landscape of the Davao Region. The findings underscore the interconnectedness of these factors and their crucial role in shaping the teaching profession. In light of these results, recommendations are made to inform policy decisions and enhance professional development programs for teachers in the region. The study encourages a holistic understanding of the relationships between web-based professional development, self-efficacy, resilience, and work values, aiming to contribute to the continuous improvement of education quality in the Davao Region. Additionally, future research is suggested to delve deeper into specific aspects of teachers' general self-efficacy, social resilience, and interpersonal relationships for a more comprehensive understanding.

This study addressed the need for efficient data collection in micro-mobility using low-end hardware, focusing on developing a system capable of handling large real-time data streams. We proposed a novel data collection system that leverages incremental learning techniques, enabling AI-based services for micro-mobility passengers through smartphones. Our approach utilizes 3-axis acceleration sensors integrated into micro-mobility devices, collecting motion data in real-time. This data is processed through an incremental learning pre-processing server, employing the Broad Learning System algorithm, to ensure robust performance in noisy, real-world environments. The use of incremental learning is pivotal, allowing our system to adapt continuously to new data, enhancing the accuracy and relevance of the AI services provided to passengers. This research not only advances the technical capabilities in micro-mobility data handling but also has the potential to enhance the overall passenger experience through improved AI-based services.

Violence against women or gender-based violence (GBV) is a major public health issue facing women and girls in different settings and is reported to have worsened globally during the COVID-19 pandemic. Despite the impact of the COVID-19 pandemic on increased violence against women in general, which has been reported in many settings globally, there is a paucity of evidence of its impact on violence against highly vulnerable women living with HIV or tuberculosis (TB). Using a qualitative design, this study aimed to explore the views and experiences of women living with HIV (n=19) or TB (n=23) in Timor Leste regarding the GBV they faced during the COVID-19 pandemic. They were recruited using the snowballing sampling technique. Data were collected using one-on-one in-depth interviews and focus group discussions. The five steps of qualitative data analysis suggested in Ritchie and Spencer’s analysis framework were employed to guide the analysis of the findings. Findings indicated that women in this study experienced intensified physical, verbal, sexual and psychological violence by their partners, spouses, in-laws, and parents or other family members during the COVID-19 pandemic. Several prominent risk factors that worsened violence against women during the pandemic were (i) HIV or TB-positive status, (ii) traditional gender roles or responsibilities and expectations, (iii) economic and financial difficulties reflected in the loss of jobs and incomes due to the pandemic, and (iv) individual factors such as jealousy and increased alcohol drinking developed during the lockdowns. The findings underscore the urgent need for multifaceted interventions to address GBV, which should encompass challenging traditional gender norms, addressing economic inequalities, and targeting individual-level risk factors. The findings also indicate the need for the development of robust monitoring and evaluation systems to assess the effectiveness of policies and interventions addressing GBV where the results can inform future improvement. The findings also indicate the need to include GBV in the protocol or guidelines for HIV and TB management. Future large-scale quantitative studies to capture the magnitude and specific drivers of GBV against women living with HIV and TB during the pandemic are recommended.

Natural fibers have attracted increasing interest as an alternative to produce environmentally friendly and sustainable materials. Particularly, hemp fibers have revealed to be widely used in various industrial applications due to their extremely unique properties. However, hemp can generate a large amount of agro-waste, and results in an attractive source of biopolymers for the development of low-cost materials in alternative to the raw materials and conventional petroleum-based plastics. In addition, deep eutectic solvents (DESs), a new type of truly green solvents, have been tested to remove gums, lignin, and other non-cellulosic components from hemp fibers. Reusing these components dissolved into the DESs to fabricate new materials directly by electrospinning is a very attractive but still unexplored endeavor. Thus, this innovative research to venture new upcycling pathways is focused on the fabrication of composite nanofibers by electrospinning of a gel-based blend of Poly(vinyl alcohol) (PVA) and hemp agro-waste (HW) dissolved into choline chloride (ChCl) : Glycerol (1:2) and ChCl : Urea (1:2) DES mixtures. The results obtained revealed that the produced nanofibers displayed uniform appearance with diameters ranging from 257.72 ± 65.63 nm to 380.78 ± 133.98 nm. In addition, the mechanical properties of the electro-spun composite nanofibers produced from the gel-based blends of HW dissolved in DESs and PVA (HW-DESs_PVA) were found to be superior, resulting in an enhanced tensile strength and Young’s modulus. Furthermore, the incorporation of HW into the nanofibers was able to provide bioactive antioxidant and antibacterial properties. Overall, this study demonstrated a promising, more sustainable, and eco-friendly way to produce electrospun composite nanofibers using HW in a circular economy perspective.

As drone technology is rapidly becoming accessible to school children in terms of both low cost and ease-of-use, primary and secondary school teachers are beginning to consider where modern drones can play an important role in schooling. To date, there is very little empirical education research printed in the education research literature guiding innovative curriculum developers in the incipient domain of drone education. As a result, teachers interested in including emerging technologies in their classrooms are often at a loss of where to begin. Through clinical interviews with schoolteachers in the United States, our study identified five readily accessible “departure points” to include drones in contemporary STEM & vocational technology (CTE) school classrooms that help teachers address common curricular goals. Taken together, these interviews reveal that teachers using drones follow one of several distinct pathways as a first step toward achieving a widespread goal of teaching students to use modern technologies to construct, pursue, and communicate findings of fruitful research inquiries—the prevalence of which is not reflected in a comprehensive literature review. The five pathways for starting a successful drone education emerging from the interview data as dominant were: timed-racing trials; precision-flight obstacle courses; computer coding; videography; and domain-specific knowledge of drone operations laws and ethics.

Nature-based solutions (NbS) rapidly develop globally to address societal challenges and provide human well-being and biodiversity. Watershed restoration plays an essential role in enhancing the ecological and socio-economic benefits of the region. The design and implementation of watershed restoration projects play a crucial role in their effectiveness, and NbS has been applied as a concept in ecosystem-related projects. This paper provides an idea to evaluate the design or implementation of watershed restoration projects based on the eight criteria proposed by the IUCN Global Standard for Nature-Based Solutions and to adjust the projects based on the evaluation results. The aim is to standardize the implementation process of watershed restoration projects to achieve more significant benefits and practically apply the concept of NbS in watershed restoration. implementation scheme of the Shan-Shui Initiative in the Jiulong River Watershed and concludes that the method is feasible in evaluating and improving the design and implementation scheme of ecological restoration projects in the actual basin. According to the assessment results, the degree of matching based on NbS for the implementation scheme of the Jiulong River Watershed Shan-Shui Initiative is 73%, which meets the criteria of NbS but needs to be improved in terms of monitoring and assessment, synergistic management, and benefit trade-offs.

The port environments have some structures that still need regular control. However, the nature of these environments presents a number of challenges in determining an accurate vehicle position and consequently performing successful image similarity detection. These scenarios, however, can have poor visibility conditions and are highly dynamic, making place recognition difficult because the appearance of a place can change over time. Under these operational conditions, imaging sonars are a promising solution. There are some factors that affect the quality of the captured images, but the fact that they do not suffer from haze is a plus. Therefore, in this paper, a purely acoustic similarity detection method based on forward-looking sonar (FLS) data is proposed to overcome the inherent perceptual problems in a port scenario. Considering the lack of available data in this context and to facilitate the variation of environmental parameters and sensor configurations, a port scenario was simulated using the Stonefish simulator. Experiments were conducted on predefined trajectories near structures. The place recognition approach performs better compared to the results obtained with optical imagery. In general, it provides a good compromise in terms of distinctiveness and achieves a performance of 87.5% when appropriate constraints and assumptions are made.

This study presents a combined electrochemical impedance spectroscopy (EIS) and atomic force microscopy (AFM) and Scanning Kelvin Probe Force Microscopy(SKPFM) investigation on the natural passivation process of stainless steels (AISI 316 and AISI 304) and nickel-based alloy (INCONEL 600) as a function of immersion time in an alkaline medium (pH = 13). EIS measurements show an increase in the resistance of the film with increasing immersion time in the electrolyte. After 3 days of immersion, for the three alloys, the films formed constitute an effective barrier to the dissolution of the alloys as shown by AFM and SKPFM. The oxide film growth on each substrates is only influenced by trenches formed during the polishing step and don’t depend on the chemical composition.

Interactions between proteins and ions are essential for various biological functions like structural stability, metabolism, and signal transport. Given that more than half of all proteins bind to ions, it becomes crucial to identify ion-binding sites. Accurate identification of protein-ion binding sites helps us to understand proteins’ biological functions and plays a significant role in drug discovery. While several computational approaches have been proposed, this remains a challenging problem due to the small size and high versatility of metals and acid radicals. In this study, we propose IonPred, a sequence-based approach that employs ELECTRA (Efficiently Learning an Encoder that Classifies Token Replacements Accurately) to predict ion binding sites using only raw protein sequences. We successfully fine-tuned our pretrained model to predict the binding sites for nine metal ions (Zn2+, Cu2+, Fe2+, Fe3+, Ca2+, Mg2+, Mn2+, Na+, and K+) and four acid radical ion ligands (CO32−, SO42−, PO43−, NO2−). IonPred surpassed six current state-of-the-art tools by over 44.65% and 28.46% respectively in F1 score and MCC when compared on an independent test dataset. Our method is more computationally efficient than existing tools producing prediction results for a hundred sequences for a specific ion in under ten minutes.

The influence of cuff inflations on nighttime measurements during 24h ambulatory blood pressure (BP) measurements is unknown. We investigated the potential effect of cuff-inflations on asleep parameters using measurements taken simultaneously with a cuffless device using pulse-transit-time (PTT). On the first day of measurement, standard cuff-based 24h BP and cuf-fless measurements were simultaneously performed on right and left arms (CUFF/PTT-D). 1-2 days after the first measurement, the cuffless device was worn alone (PTT-D). Only data from the cuffless device was analyzed. The following mean asleep parameters were analyzed: systolic and diastolic BP, arousals, sleep efficiency, total arousals, arousal per hour and desaturations. 21 in-dividuals were prospectively enrolled. Mean (SD) age was 47(±15) years and 57% were female. Mean systolic asleep BP for the during CUFF/PTT-D and during PTT-D were 131(±21) and 131(±26) mmHg respectively. Mean diastolic asleep BP values during CUFF/PTT-D and during PTT-D were 80(±14) and 84(±14) mmHg respectively (p=0.860, p=0.100 respectively). Systolic and diastolic asleep mean difference was 0.1 (±18.0) and -3.6 (±9.8) mmHg, respectively. There were significantly more total arousals during PTT-D (p=0.042). There was no significant difference seen in sleep efficiency (p=0.339) or desaturations (p=0.896) between the two measurements. We could not show any significant impact from cuff inflations during asleep PTT-D measurements.

This study investigates the cyclic load application impact on fly ash-based geopolymer composites that are reinforced with a low amount of fibre reinforcement. For reinforcement purposes, PVA and steel fibres are used. For testing purposes, four geopolymer composite mixes were made, 3 of which had fibre reinforcement. Simultaneously specimens were tested for shrinkage, static load-induced creep, and cyclic load-induced creep. For static and cyclic creep testing, specimens were loaded with 20% of their strength. For cyclic creep testing, load application and release cycles were seven days long. When each cycle was introduced, the load was added in steps. In 5 minutes, by 25% steps of the necessary load, the specimens were loaded or unloaded. Only plain specimens show that static creep strains are within cyclic creep strains. For all the other specimens, the static load is higher than the cyclic load-induced creep amplitude. Also, 1% PVA fibre-reinforced specimens show the most elastic characteristics under cyclic load, and 1% steel fibre-reinforced specimens appear to be the most resistant to the cyclic load introduction.

A new intervention model for promoting healthy ageing grounded on integrated value-based care was developed and tested in the city of Valencia (Spain). Its implementation raised relevant barriers for older adults in their access to health, health promotion, and health self-management linked with their health and digital literacy. This new intervention model included several aspects. On the one hand, researchers together with older adults and their informal caregivers participating in the study, designed personalised care plans, based on older adults’ specific needs, to be implemented with the support of a digital solution. On the other hand, researchers and health and social professionals implemented a series of six workshops in different locations of the city to encourage the sense of community among participants, reinforcing their trust in the new care model and increasing their adherence. Social activities were at the core of the workshops to understand older people’s interaction with the health and social services provided in the neighbourhood. Qualitative and quantitative methods were combined to extract information from older participants on how to engage them as active actors of their own health and understanding their values and preferences. Qualitative results show that after a post-pandemic situation, they were more concerned about social isolation and desired face-to-face contact with their professional care team; however, feelings of loneliness and/or sadness were not considered amongst the reasons to visit health professionals. Some of the conclusions reveal that the use of technology as a supportive tool is well received but with a stress on its role as “supportive”, and not replacing the close contact with healthcare professionals. Professionals recognised the benefits of this new approach but required more time and incentives to dedicate the effort needed.

Indonesia's government anticipates waste problems by managing them through a large number of waste reduction programs. From a governance perspective in Indonesia, it is known that actors from diverse governmental levels and across sectors are involved in waste management, and their involvement largely depends on their institutional goals and problems. Hence, they are expected to coherently collaborate together by developing and implementing sustainable instruments and resources for improving waste management problems. However, it is necessary to understand how supportive and restrictive the governance of waste reduction programs is to encourage successful trash reduction. This study is designed to evaluate and examine the application of Community-Based Solid Waste Management (CBSWM) in Makassar. In this research, The Governance Assessment Tool (GAT) was applied to analyze the governance context of the waste reduction programs in Makassar qualitatively. From the assessment, contexts can be described as either supportive or restrictive of the processes of implementing public policies on waste management. As a result of such an assessment, this study shed light on some opportunities to improve the governance of waste management implementation's impact on reducing waste in Makassar, South Sulawesi, Indonesia. The conclusion of this study depicts that the most important actors are affiliated with the local government and the community. Moreover, this study shows that the assessment of contextual governance is predominantly restrictive.

In recent years, in addition to the commonly known wire-based processes of Directed Energy Deposition using lasers, a process variant using the electron beam has also developed to industrial market maturity. The process offers particular potential for processing highly conductive, reflective or oxidation-prone materials. However, for industrial usage there is a lack of comprehensive data on performance, process limits and possible applications. The present study deals with this problem using the example of the high-strength aluminum bronze CuAl8Ni6. Multi-stage test welds are used to determine the physically possible process limits and draw conclusions about the suitability of the parameters for additive manufacturing. For this purpose, optimal ranges for energy input, possible welding speeds and the scalability of the process were investigated. Finally, additive test specimens in the form of cylinders and walls are produced and the hardness profile, microstructure and mechanical properties are investigated. It is found that the material CuAl8Ni6 can be well processed by wire electron beam additive manufacturing. The microstructure is similar to a cast structure, the hardness profile over the height of the specimens is constant and the tensile strength and elongation at fracture values achieved correspond to the specification of the raw material.

Quantitative polymerase chain reaction (qPCR) is the gold standard approach for detecting a variety of pathogens either via probe-based or SYBR green detection chemistry. Generally, probe-based detection is more specific than SYBR green chemistry. This report describes work done in using a FAM probe targeting a specific segment of the human KRAS gene for detecting presence and relative abundance of the gene in human gastric CloTest samples. Results indicate ineffective detection of the human KRAS gene in samples that are shown to harbor the gene through a similar qPCR assay using SYBR green detection chemistry. Such observations point to inability of the designed FAM probe to bind to target segment of the human KRAS gene that suggests a region of rapid genomic evolution that require close surveillance in the public health community. Given its rapid genomic evolution, this probe region may be an important region in the human KRAS gene playing critical functional roles in either molecular recognition or other structural functions. Overall, human KRAS gene may be undergoing rapid genomic evolution, and qPCR probes targeting specific segment of the gene may be rendered ineffective in short time clinically, thereby, making SYBR green qPCR assay a more reliable choice.

This study aimed to evaluate the effects of a palygorskite-based antibacterial agent (PAA) as an alternative to antibiotics on growth performance, blood parameters, rumen fermentation, and rumen microbiota in sheep. A total of 120 sheep were randomly divided into five groups of six replicates with four sheep each. Sheep were fed a basal diet, an antibiotic diet supplemented with 500 g/t chlortetracycline (CTC), and a basal diet supplemented with 500, 1,000, and 2,000 g/t PAA for 80 d, respectively. Supplementation with 2,000 g/t PAA and 500 g/t CTC increased the average daily gain (ADG) of sheep compared with the control group (P < 0.05). Diets supplemented with 2,000 g/t PAA and 500 g/t CTC reduced (P < 0.05) the feed: gain (F/G ratio) in the overall periods. Dietary Supplementation with 1,000 g/t PAA significantly increased albumin and total protein (P < 0.05). A significant positive correlation was found between growth hormone concentration and PAA supplementation (P < 0.05). In addition, compared to the control group, the CTC group had higher growth hormone concentration and lower lipopolysaccharide concentration (P < 0.05). No dif-ference was observed between the five groups in terms of rumen fermentation characteristics (P > 0.05). At the phylum level, the relative abundance of Proteobacteria was lower in the PAA 2000 and CTC 500 groups than that in the control and PAA 500 groups (P < 0.05). At the genus level, a sig-nificant decrease (P < 0.05) in the relative abundance of RuminococcaceaeUCG-010 was observed in the PAA 1000, PAA 2000, and CTC 500 groups compared with that in the control group. In addition, the relative abundance of Prevotella1 (P < 0.05) was higher in the PAA 2000 group than that in the control group. These findings indicate that dietary supplementation with PAA has ameliorative effects on growth performance, blood parameters, and rumen microbiota, with an optimal dosage of 2,000 g/t for sheep.

The chiral recognition of enantiomers is a topic of great importance. In this study, a straightforward and effective chiral detection strategy was developed for distinguishing between tryptophan (TRP) enantiomers. The approach involved the preparation of a magnetic molecularly imprinted chitosan (MMIC) for the sample preparation, which was combined with a nitrocellulose membrane (paper-based analytical device, PAD) integrated with D-TRP covalently-grafted polymethacrylic acid (PAD_PMA_D-TRP). The discrimination between TRP enantiomers was achieved using AuNPs as a colorimetric probe. Indeed, the presence of D-TRP rapidly induced the aggregation of AuNPs due to its strong affinity to PAD-PMA_D-TRP, resulting in a noticeable color change of the AuNPs from red to purple. On the other hand, L-TRP did not induce any color change. The chiral analysis could be easily performed with the naked eye and/or a smartphone. The developed method exhibited a detection limit of 3.3 µM and it was successfully applied to detect D-TRP in serum samples, demonstrating good recovery rates. The proposed procedure is characterized by its simplicity, cost-effectiveness, rapidity, and ease of operation.

For effective warehouse management, various criteria should be taken into consideration in terms of inventory management, demand, transportation distance, usage area, delivery-waiting times, and minimizing the costs that may arise from them. Because, the purpose of the product assignment problem is to minimize the total transport distance during storage and to reduce the space required for product assignment. However, the storage location assignment problem (SLAP) occurs simultaneously with production planning activities in real life. In this study, an integrated mixed-integer nonlinear (MINLP) mathematical model is proposed, which includes the assignment of products to storage areas and production planning, using the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) class based policy for storage systems where multiple criteria are important. In the TOPSIS class-based policy, weight and waiting time criteria are taken into account, as well as the COI (Cube-per-order index index), which is widely used in the literature. In addition, a decision support system flow has been proposed that takes into account the single criterion, multiple criteria and COI index criteria of the developed model and provides its integration with different product assignment policies. Detailed literature research; It has been revealed that the proposed mathematical model and decision support system flow have the feature of applying an existing field of science to a new problem.

Neovascular age-related macular degeneration (nAMD) leads to visual impairment if not treated timely. Intravitreal anti-VEGF drugs have revolutionized nAMD treatment in two decades. We evaluated visual outcomes of anti-VEGF treatment in nAMD. A real-life population-based cohort study. Data included parameters for age, sex, age at the diagnosis, laterality, chronicity, symptoms, visual outcomes, lens status, and history of intravitreal injections. A total of 1088 eyes (827 patients) with nAMD were included. Visual acuity was stable or improved in 984 eyes (90%) after an average 36±25 months follow-up. Bevacitzumab was the first-line drug in 1083 (99.5%) eyes. Vision improved ≥15 ETDRS letters in 377 (35%), > 5 ETDRS letters in 309 (28%) and was stable (±5 ETDRS letters) in 298 (27%) eyes after anti-VEGF treatment. The loss of 5-<15 ETDRS letters in 44 (4%) and ≥15 ETDRS letters in 60 (6%) eyes was noted. At the diagnosis of nAMD, 110 out of 827 patients (13%) fulfilled the criteria of visual impairment, whereas 179 patients (22%) were visually impaired after the follow-up. Improvement or stabilization in vision was noted in 90% of the anti-VEGF-treated eyes with nAMD. In addition, anti-VEGF agents are crucial in diminishing nAMD-related visual impairment.

Prostate cancer (PCa) is the most commonly diagnosed cancer and the second most common cause of death due to cancer. About 30% of patients with PCa who have been castrated develop a castration-resistant form of the disease (CRPC), which is incurable. In the last decade, new treatments that control the disease have emerged, slowing progression and spread, and prolonging survival while maintaining the quality of life. These include immunotherapies; however, we do not yet know the optimal combination and sequence of these therapies with the standard ones. All therapies are not always suitable for every patient due to co-morbidities or adverse effects of therapies or both, so there is an urgent need for further work on new therapeutic options. Advances in cancer immunotherapy with an immune checkpoint inhibition mechanism (e.g., ipilimumab, an anti-CTLA-4 inhibitor) have not shown a survival benefit in patients with CRPC. Other immunological approaches have also not given clear results, which has indirectly prevented breakthrough for this type of therapeutic strategy into clinical use. Currently, the only approved form of immunotherapy for patients with CRPC is a cell-based medicine, but it is only available to patients in some parts of the world. Based on the experience gained from recently completed clinical research on immunotherapy with dendritic cell-based immunohybridomas, the aHyC dendritic cell vaccine, for patients with CRPC, we highlight the current status, and possible alternatives that should be considered in the future.

The worldwide population is undergoing a fundamental change in its age structure, which challenges the health and social services system. The need to migrate towards a more person-centered and coordinated model of care that supports the optimization of abilities and capacities for older people has to be matched. In this sense, eHealth technologies can play a fundamental role. In this paper, through a questionnaire-based data collection on 30 primary (older people) and 32 secondary (informal caregivers) end-users, we share the sustainable way to develop a product optimizing the user experience and ensuring adoption. Both older adults and caregivers consider the use of smartphone and smartwatch to be very important, but also the use of digital devices for healthcare can be helpful. Seniors care about self-monitoring of health parameters using wearable devices, regardless of their health status, and would like to be included in the process of making good health decisions, because they need to feel in control of their healthcare process. Digital solutions in health and care can support the wellbeing of older adults in many areas of their daily lives at home and in their communities, but only if such innovation is designed around the natural voice of the intended target.

This thesis presents the effects of work done on a software project for generative models and spreadsheets, allowing for a quick creation of the conceptual model of the aircraft. The subject of the work is a response to the current trends and needs prevailing in the field of computer design engineering CAD and aviation. In the initial chapters, theoretical issues related to the work being carried out were introduced and the methodology of creating software for construction and verification of the structure of aircraft along with the needs of interchange between databases of generative models was presented. In the next stages, the concepts and selected solutions for the user interface supporting the knowledge base were presented along with a set of procedures for its operation. Furthermore, the method of database integration with the methods of determining design features for the developed generative models and with the Siemens NX system. Furthermore, problems encountered in software development, as well as solution examples for model application are specified. The results obtained and the models generated on their basis were subjected to a strength analysis using Autodesk Inventor software and analysed in terms of meeting the initial assumptions. In the end, conclusions and observations resulting from the effects of the work presented in the project were formulated.

Nickelate superconductors, R1-xAxNiO2 (where R is a rare earth metal and A = Sr, Ca), experimentally discovered in 2019 exhibit many unexplained mysteries as the existence of a superconducting state with Tc (up to 18 K) in thin films and its absence in bulk materials. Another unexplained mystery of nickelates is their temperature-dependent upper critical field, Bc2T, which can be nicely fitted to two-dimensional (2D) models; however the deduced film thickness, dsc,GL, exceeds the physical film thickness, dsc, by a manifold. To address the latter, it should be noted that 2D models assume that dsc is less than the in-plane and out-of-plane ground state coherence lengths, dsc&lt;ξab0 and dsc&lt;ξc0, respectively, and, in addition, that the inequality ξc0&lt;ξab0 satisfies. Analysis of the reported experimental Bc2T data showed that at least one of these conditions does not satisfy for R1-xAxNiO2 films. This implies that nickelate films are not 2D superconductors, even despite though that the superconducting state is observed only in thin films. Based on this, here we proposed analytical three dimensional (3D) model for global data fit of in-plane and out-of-plane Bc2T in nickelates. The model is based on a heuristic expression for temperature dependent coherence length anisotropy: γξT=γξ01-1a×TTc, where a&gt;1 is a unitless free-fitting parameter. The proposed expression for γξT, perhaps, has a much broader application because it has been successfully applied to bulk pnictide and chalcogenide superconductors.

The appropriate maintenance of highway roads is critical for the safe operation of road networks and conserves maintenance costs. Multiple methods have been developed to investigate the surface of roads for various types of cracks and potholes, among other damage. Like road surface damage, the condition of expansion joints in concrete pavement is important to avoid unexpected hazardous situations. Thus, in this study, a new system is proposed for autonomous expansion joint monitoring using a vision-based system. The system consists of the following three key parts: (1) a camera-mounted vehicle, (2) indication marks on the expansion joints, and (3) a deep learning-based automatic evaluation algorithm. With paired marks indicating the expansion joints in a concrete pavement, they can be automatically detected. An inspection vehicle is equipped with an action camera that acquires images of the expansion joints in the road. You Only Look Once (YOLO) automatically detects the expansion joints with indication marks, which has a performance accuracy of 95%. The width of the detected expansion joint is calculated using an image processing algorithm. Based on the calculated width, the expansion joint is classified into the following two types: normal and dangerous. The obtained results demonstrate that the proposed system is very efficient in terms of speed and accuracy.

Infectious disease like COVID-19 will have a great impact on human society. An effective prevention and control measure is to quickly isolate the source and reduce the population’s exposure to the infection. Therefore, it is very important to quickly trace and identify the virus carrier and its close contacts in order to effectively suppressing transmission. This article provides a method, system and storage medium for locating the source of infection based on big data. We adopt the method of combining Bluetooth and GPS positioning to track the action trajectory of each individual. The method stores the close contact and location information locally and update on the cloud platform for an appropriate period of time. The method and system can provide personal risk levels and regional risk levels through related algorithms. When entering a high-risk area or if the personal risk level within the Bluetooth interconnection range is greater than the set threshold, an alarm function is provided. We can also generate reports on the spread of the epidemic over a period of time. As a result, the system can quickly locate the source of infection and close contacts, providing a reference for quick and highly efficient epidemic prevention and control.

The objective of this study was to compare the pathogens and susceptibilities of the current automated rule-based technology (RBT) antibiogram with one manually collected through chart review with additional rules applied. This study was a 2-year, retrospective cohort study and included all bacterial cultures within the first 30 days from patients admitted to a single Burn Center. The current RBT antibiogram served as the control and new antibiogram versions were created using additional rules and compared to the control. Six-hundred fifty-seven patients were admitted (61% excluded for lack of cultures). Fifty-nine percent had at least one hospital acquired risk factor with over one-third having recent illicit drug use and one-third having a recent hospitalization. Of the 410 cultures included, 57% were Gram-negative and half were from wound infections. Sensitivities were significantly different when comparing manual and the RBT version after including factors, such as days since admission, presence of hospital acquired risk factors, or previous antibiotic courses. Recommended empiric Gram-negative antibiotics changed from double coverage to a single β-lactam with > 90% susceptibility. The susceptibilities between first and subsequent courses were dramatically different. Before developing an antibiogram or interpreting the output, it is important to consider which automated criteria are utilized, especially for units with extended lengths of stay.

The COVID-19 pandemic has posed a significant global threat, leading to several initiatives for its control and management. One such initiative involves wastewater-based epidemiology, which has gained attention for its potential to provide early warning of virus outbreaks and real-time information on its spread. In this study, water samples from two wastewater treatment plants (WWTPs) located at the south east of Spain (Region of Murcia) namely Murcia, and Cartagena, were analyzed by RT-qPCR, Phylogenetic Analysis, and Machine Learning Approach. The aim was to determine whether SARS-CoV-2 detection in the WWTPs of these two cities could serve as a proxy for the virus's spread in the population. The results confirmed that the levels of SARS-CoV-2 in these wastewater samples changed concerning the number of SARS-CoV-2 cases detected in the population and variant occurrences were in line with clinical reported data. Additionally, the phylogenetic analysis showed that samples obtained in close sampling times exhibited a higher similarity than those obtained more distantly in time. A second analysis using a machine learning approach based on the mutations found in the SARS-CoV-2 spike protein was also conducted. Hierarchical Clustering (HC) was used as an efficient unsupervised approach for data analysis. Results indicated that samples obtained in October 2022 in Murcia and Cartagena were significantly different, which corresponded well with the different virus variants circulating in the two locations. The proposed methods in this study are adequate for comparing the Accumulated Natural Vector (ANV) of the SARS-CoV-2 sequences as a preliminary evaluation of potential changes in the variants that are circulating in a given population at a specific time point.

This research investigates the effect of pre-soaking treatment on plant-based aggregate using a wet grout binder to formulate a high-strength lightweight concrete (HSLWC). The surface modification utilising a novel grout soaking technique with various water-to-cement (w/c) ratios indicated a new method of approach for the recent development of lightweight plant-based aggregate (LWPA). In this experiment, the fresh and hardened properties of modified LWPA lightweight concrete were assessed by verifying their workability, densities, compressive and split tensile strengths towards the modulus of elasticity. The results showed that pre-soaking plant-based lightweight aggregate (w/c: 0.6, 0.8, 1.0, and 1.2) slightly increased the density of the samples compared to untreated LWPA. The oven-dry density of treated and untreated LWPA is controlled in the range of HSLWC. The outcomes indicated that the workability of the surface- modified LWPA has significant improvement up to 40% in 6 min for (TDS)/0.6 sample compared to the original LWPA. The mechanical properties of the LWPA concrete with surface modification method exhibit substantial increment of compressive strength, split tensile strength, and modulus of elasticity were recorded at 22%, 26%, and 34%, respectively. Significantly, the findings from this scientific method revealed that the pre-soaking treatment method on LWPA has shown to be a highly recommended technique in improving interfacial bonding while performing as one of the most promising solutions to improve the properties of lightweight concrete.

Aim: This study aimed to investigate whether baseline 18F-FDG PET-derived semiquantitative parameters could predict disease-free survival in patients with grade III breast cancer of different molecular subtypes undergoing neoadjuvant chemotherapy (NAC). Materials and Methods: Patients were retrospectively retrieved from the databases of two Italian hospitals (University of Padua and University Hospital of Ferrara). Inclusion criteria were: 1) histologically proven diagnosis of grade III breast cancer; 2) history of NAC; 3) execution of a baseline 18F-FDG PET/CT scan before the start of NAC; 4) adequate follow-up time to compute the 3Y-DFS. For each 18F-FDG-PET/CT scan, the following parameters were calculated in the primary tumor (SUVmax, SUVmean, MTV, TLG) and whole-body (WB_SUVmax, WB_MTV, and WB_TLG). Receiver operating characteristic (ROC) analysis was used for each parameter to assess the prediction capability for 3Y-DFS. DFS differences between groups were assessed using Kaplan-Meier curves and compared by log-rank test. Results: Ninety-five grade III breast cancer patients with different molecular types were retrieved (luminal A: 5; luminal B: 34; luminal B-HER2: 22; HER2-enriched: 7; triple-negative: 27). In the whole group of patients, WB_SUVmax was the best predictor of recurrence (AUC: 0.66; best cut-off: WB_SUVmax>9.43; SS: 96%; SP: 38.1%; p<0.008). Patients with luminal A and Her-2-enriched breast cancer types were excluded from the molecular subgroup analysis due to the small study samples (5 and 7, respectively). In luminal B patients (n=34), WB_MTV (AUC: 0.75; best cut-off: WB_MTV>195.33; SS: 55.56%, SP: 100%; p=0.002) and WB_TLG (AUC: 0.73; best cut-off: WB_TLG>1066.21; SS: 55.56%, SP: 100%; p=0.05) were the best predictors of 3Y-DFS. Patients had a higher probability of DFS in case of a WB_MTV≤195.33 (3429±210 vs. 446±144 days; p<0.001) or WB_TLG≤ 1066.33 (937.35±19 vs. 496±131 days; p<0.001). In luminal B-HER2 patients (n=22), WB_SUVmax was the only predictor of 3Y-DFS (AUC: 0.857; best cut-off: WB_SUVmax>13.12; SS: 100%; SP: 71.43%; p<0.001); patients with WB_SUVmax≤13.12 had a significantly longer DFS (3857 days) than patients with a WB_SUVmax >13.12 (1693±430 days; p<0.004). No parameter resulted significant in the prediction of 3Y-DFS in patients with grade III triple-negative cancer. Conclusion: Volume-based parameters, extracted from baseline 18F-FDG PET, seem to be promising tools in the prediction of recurrence within 3 years in patients with grade III luminal B and luminal B- HER2 breast cancer undergoing NAC. Larger study samples are needed to confirm these preliminary findings.

The detection and quantification of protein–protein interactions (PPIs) is a crucial technique that often involves the use of recombinant proteins with fusion-protein tags, such as maltose-binding protein (MBP) and glutathione-S-transferase (GST). In this study, we improved the cohesive and sticky properties of gelatinized starch by supplementing it with agarose, resulting in a harder gel that could coat the bottom of a microtiter plate. The resulting gelatinized starch/agarose mixture allowed for the efficient immobilization of MBP-tagged proteins on the coated plates, enabling the use of indirect ELISA-like PPI assays. By using the enzymatic activity of GST as an indicator, we succeeded in determining the dissociation constants between MBP-tagged and GST-tagged proteins on 96-well microtiter plates and a microplate reader without any expensive specialized equipment.

Novel strategies in coastal protection are needed to cope with climate change-induced sea level rise. They aim at the sustainable development of coastal areas in light of an intensification and land use changes. A promising approach is the design of nature-based solutions (NbS), complementing the safety levels of technical infrastructures. However, NbS lack a widespread and large-scale implementation. To address this deficit, co-design concepts are needed that combine experiences from science and practice. This work presents and discusses the approach of a coast-specific real-world laboratory (RwL) addressing the inclusive design of ecosystem-based coastal protection. Strategies of RwLs are applied for the first time in a coastal context along the North Sea coastline in Germany. We found the concept of RwLs suitable for coastal transdisciplinary research, although adaptions in the spatial reference level or flexibility in location and time of experimentation are necessary. A profound actor analysis is indispensable to specify participatory processes and interaction levels. A criteria-based cooperative selection of RwL sites helps to reveal and solve conflicting interests to achieve trust between science and practice. Addressing site-specific characteristics and practitioners’ needs, our coastal RwL provides a mutual learning space to develop and test NbS to complement technical coastal protection.

Protein phosphorylation and ADP-ribosylation (ADPr), as two types of post-translational modifications (PTM), are the process of adding phosphate group and ADP-ribose moieties to proteins, respectively. Although both PTM types can occur on many amino acid types, serine is the most common. Serine phosphorylation (pS), serine ADPr (SADPr), and their in situ crosstalks (pSADPr) play essential roles in biological processes. Although in silico classifiers have been developed for predicting pS and SADPr sites, the classifier for predicting pSADPr sites is unavailable. In this study, we developed classifiers to predict pSADPr sites. Specifically, we collected 3250 human pSADPr, 7520 SADPr, 151,227 pS and 80,096 unmodified serine sites. Based on them, we investigated the characteristics of pSADPr sites and constructed three classifiers to predict pSADPr sites from the pS dataset, the SADPr dataset and the protein sequences separately. We built and evaluated five deep-learning classifiers in ten-fold cross-validation and independent test datasets. Three of them (e.g. Convolutional Neural Network with the One-Hot encoding, dubbed CNNOH) performed better than the rest two. For instance, CNNOH had the AUC values of 0.700, 0.914 and 0.954 for recognizing pSADPr sites from the SADPr, pS and unmodified serine sites.Therefore, it is challenging to distinguish pSADPr sites from SADPr sites compared to the other two. It is consistent with our observation that pSADPr's characteristics are more similar to those of SADPr than the rest. Furthermore, we used the classifiers as base classifiers to develop a few stacking-based ensemble classifiers to improve performance. However, none of the ensemble classifiers showed better performances, suggesting that the base classifiers have good enough performances. Finally, we developed an online tool for extensively predicting human pSADPr sites based on the CNNOH classifier, dubbed EdeepSADPr. It is freely available through http://edeepsadpr.bioinfogo.org/.

Physiologically Based Pharmacokinetic Models (PBPK) are mechanistical tools generally employed in the pharmaceutical industry and environmental health risk assessment. These models are recognised by regulatory authorities for predicting organ concentration-time profile, pharmacokinetic and daily intake dose of xenobiotics. Extension of PBPK models to capture sensitive populations like pediatric, geriatric, pregnant females, fetus etc. and diseased population like renal impairment, liver cirrhosis etc. is a must. However, the current modelling practice and existing models are not mature enough to confidently predict the risk in these populations. A multidisciplinary collaboration between clinicians, experimental and modeler scientist is vital to improve the physiology, and calculation of biochemical parameters for integrating the knowledge and refining existing PBPK models. Specific PBPK covering compartments like cerebrospinal fluid, and hippocampus are required to gain mechanistic understanding about xenobiotic disposition in these sub-parts. The PBPK model assists in building quantitative adverse outcome pathways (qAOPs) for several endpoints like developmental neurotoxicity (DNT), hepatotoxicity and cardiotoxicity. Machine learning algorithms can predict physicochemical parameters required to develop in-silico models where experimental data is unavailable. Integrating machine learning with PBPK carries the potential to revolutionize the field of drug discovery and development and environmental risk. Overall, this review tried to summarize the recent developments in the in-silico models, building qAOPs, use of machine learning for improving existing models along with a regulatory perspective. This review can act as a guide for toxicologists who wish to build their careers in kinetic modeling.

Background: Health inequity (HI) remains a major challenge in public health. Improving the health of children with low socioeconomic status (SES) can help to reduce overall HI in children. Childhood obesity is a global problem, entailing several adverse health effects. It is crucial to assess influencing factors for adoption, implementation and sustainment of interventions. This review aims to identify articles reporting about influencing factors for the implementation of school-based interventions promoting obesity prevention behaviors in children with low SES. It aims to critically appraise the articles’ quality, assess influencing factors, categorize and evaluate them, and to discuss possible implications. Methods: A systematic search was conducted in 7 databases with the following main inclusion criteria: 1) school-based interventions and 2) target group aged 5–14 years. The Consolidated Framework for Implementation Research, its five domains (intervention characteristics, inner setting, outer setting, characteristics of individuals, process) along with 39 constructs within these domains were used to deductively analyze the data. We grouped the articles with regard to the characteristics of the interventions in simple and complex interventions. For each domain, and for the groups of simple and complex interventions, the most commonly reported influencing factors are identified. Results: 6452 articles were screened, and 16 met all eligibility criteria. Included articles applied mixed methods (n=10), qualitative (n=5) and quantitative design (n=1). Of these, five were considered to report simple interventions and eleven were considered to report complex interventions. In total, 295 influencing factors were assessed. Aspects of the inner setting were reported in every study, aspects of the outer setting were the least reported domain, and in the group of simple interventions not reported at all. In the inner setting, most reported influencing factors were time (n=7), scheduling (n=6) and communication (n=6). Conclusion: This review found a wide range of influencing factors for implementation. Most important influencing factors need to be assessed for every setting. Including all stakeholders involved in the implementation process enhances the prioritization of the most important influencing factors for the specific setting. More empirical research and practical guidance are needed to promote obesity prevention behaviors among children with low SES.

Over 130 countries have committed to reaching net-zero CO2 or GHG emissions by 2050, yet this ambition is rarely underpinned by robust policies. By applying a detailed integrated assessment modelling approach for Brazil, we assess, for the first time, the extent to which the existing and planned local policies could put Brazil on the path to its net zero pledge. This includes quantifying the role of nature-based solutions, such as protection and restoration, and engineered solutions, such as bioenergy with carbon capture and storage (BECCS). We show protection is the single most important climate mitigation measure at relatively low costs, whereas relying heavily on engineered solutions would jeopardise Brazil’s chances of achieving its net zero pledge. We also show that the mismatch between Brazil's short- and long-term climate targets reflects current weak environmental governance. Our analysis reinforces the urgent need for Brazil to eliminate illegal deforestation and go beyond to help fight climate change whilst curbing biodiversity loss.

The current paradigm shift in orthodontic treatment planning is based on facially driven diagnostics. This requires an affordable, convenient, and non-invasive solution for face scanning. Therefore, utilization of smartphones` TrueDepth sensors is very tempting. TrueDepth refers to front-facing cameras with a dot projector in Apple devices that provide real-time depth data in addition to visual information. There are several applications that tout themselves as accurate solutions for 3D scanning of the face in dentistry. Their clinical accuracy has been uncertain. This study focuses on evaluating the accuracy of the Bellus3D Dental Pro app, which uses Apple's TrueDepth sensor. The app reconstructs a virtual, high-resolution version of the face, which is available for download as a 3D object. In this paper, sixty TrueDepth scans of the face were compared to sixty corresponding facial surfaces segmented from CBCT. Difference maps were created for each pair and evaluated in specific facial regions. The results confirmed statistically significant differences in some facial regions in amplitudes greater than 3 mm, suggesting that current technology has limited applicability for clinical use. The clinical utilization of facial scanning for orthodontic evaluation, which does not require accuracy in the lip region below 3 mm, can be considered.

Newly emerging SARS-CoV-2 variants may escape monoclonal antibodies (mAbs) and antiviral drugs. By using live virus assays, we assessed the ex vivo inhibition of the B.1 wild type (WT), delta and omicron BA.1 and BA.2 lineages by post-infusion sera from 40 individuals treated with bamlanivimab/etesevimab (BAM/ETE), casirivimab/imdevimab (CAS/IMD) and sotrovimab (SOT) as well as the activity of remdesivir, nirmatrelvir and molnupiravir. mAbs and drug activity were defined as the serum dilution (ID50) and drug concentration (IC50), respectively, showing 50% protection of virus-induced cytopathic effect. All pre-infusion sera were negative for SARS-CoV-2 neutralizing activity. BAM/ETE, CAS/IMD and SOT showed activity against the WT (ID50 6,295 [4,355-8,075] for BAM/ETE; 18,214 [16,248-21,365] for CAS/IMD and 456 [265-592] for SOT) and the delta (14,780 [ID50 10,905-21,020] for BAM/ETE, 63,937 [47,211-79,971] for CAS/IMD and 1,103 [843-1,334] for SOT). Notably, only SOT was active against BA.1 (ID50 200 [37-233]) while BA.2 was neutralized by CAS/IMD (ID50 174 [134-209] ID50) and SOT (ID50 20 [9-31] ID50) but not by BAM/ETE. No significant inter-variant IC50 differences were observed for molnupiravir (1.5±0.1/1.5±0.7/1.0±0.5/0.8±0.01 μM for WT/delta/BA.1/BA.2, respectively); nirmatrelvir (0.05±0.02/0.06±0.01/0.04±0.02/0.04±0.01 μM) and remdesivir (0.08±0.04/0.11±0.08/0.05±0.04/0.08±0.01 μM). Continued evolution of SARS-CoV-2 requires updating the mAbs arsenal, however antivirals have so far remained unaffected.

Being hospitalized is a threatening and stressful experience for many children. From a psychological point of view, children may experience increased feelings of anxiety and fear that can negatively interfere with behavioral, cognitive, and emotional outcomes. To limit these impacts on children's well-being and mental health, interventions that could contribute to protecting the emotional domain of hospitalized children are welcomed. The present research reported a single-setting case study intervention to evaluate the impact of educational play-based activity with a CoderBot robot in a pediatric short-term recovery ward (N=61). The methodology included multiple sources of data (i.e., children, parents, nurses), observations on the field, and a sequential (quantitative-qualitative) mixed-method approach to data analysis. Results supported the idea that robot-based activities were associated with increased participant well-being (particularly positive emotions). The conclusions of this pilot study discuss the strengths, limitations, and further developments of using robots with hospitalized children.

Social safety, security, and comfort of school-going children during the travel time to school becomes a subject of anxiety to the parents and is a crucial issue in recent times. In this regard, community-based transport can be a significant way to address social security issues in travel at a reasonable cost and reduce the burden on private mode. In Dhaka city, school van service already exists but due to some sort of problems the service has not been proved an efficient and formal mode of transport for solving mobility problems. This study seeks to identify the existing problems and prospects of the school van service and provide a unique, healthy, safe, and reliable transport mode for children. Applying different tools of the Participatory Rural Appraisal (PRA) method, the problems and solutions have been drawn from the community. The recommendations of this study will help the school van services (a community-managed para-transit system) to be more functional in playing a vital role in solving the problems of short-distance travel. This service has great potentialities to be adopted in other trips such as trips to and from offices which will lessen the road congestion at the peak periods.

Achieving global goals on sustainable nutrition, health, and wellbeing will depend on delivering enhanced diets to humankind. This will require, among others, instantaneous access to information on food quality at key points within agri-food systems. Although stationary methods are usually used to quantify grain quality (wet-lab chemistry, benchtop NIR spectrometer); these do not suit many required user-cases, such as stakeholders in decentralized agri-food-chains that are typical for emerging economies. Therefore, we explored new technologies and models that might aid these particular user-cases. For this purpose, we generated the NIR spectra of 328 grain samples from multiple cereals (finger millet, foxtail millet, maize, pearl millet, sorghum) with a standard benchtop NIR Spectrometer (DS2500, FOSS) and a novel mobile NIR-based sensor (HL-EVT5, Hone). We explored a range of classical deterministic and novel machine learning (ML)-driven models to build calibrations out of the NIR spectra. We were able to build relevant calibrations out of both types of spectra. At the same time, ML-based methods enhanced the prediction capacity of calibration models compared to classical deterministic methods. We also documented that the prediction of grain protein content based on NIR spectra generated by a mobile sensor (HL-EVT5, Hone) was highly relevant for quantitative protein predictions (R2 = 0.91, RMSE = 0.97, RPD = 3.48). Thus, the findings of this study lay the foundations on which to expand the utilization of NIR spectroscopy applications for agricultural research and development.