Capability evaluation of real-time inline COD detection technique for dynamic water footprint management in the beverage manufacturing industry

This paper reports the development of a real-time inline Chemical Oxygen Demand (COD) detection technique in a beverage manufacturing plant in England and the evaluation of its capability for dynamic Water Footprint (WF) management. The inline technique employed Ultraviolet–Visible (UV-VIS) spectroscopy and Moving Window Partial Least Squares (mwPLS), which was then applied to calculating Grey WF for the production activities in the plant, referred to here as WFrt. A traditional offline COD measurement method was also utilised for the Grey WF calculation, to act as the reference method, referred to here as WFtrad. In a method-comparison study (Bland-Altman Plot), the results showed that WFrt detected the order of magnitude variation of WFtrad, and WFtrad was on average between 0.897 and 1.243 times WFrt with no systematic bias. This indicates that WFrt may be used for both short-time frame (minutes to hours) WF monitoring and long-term (weeks to months) analysis of trends and the effect of WF optimisation strategies

Numerical investigation of particle dynamic behaviours in geophysical flows considering solid-fluid interaction

Solid-fluid interaction vitally influences the flow dynamics of particles in a geophysical flow. A coupled computational fluid dynamics and discrete element method (CFD-DEM) is used in this study to model multiphase geophysical flow as a mixture of fluid and solid phases. The two non-Newtonian fluids (i.e., Bingham and Hershcel-Bulkley fluids) and water mixed with particles are considered in the simulation, while dry granular flow with the same volume is simulated as a control test. Results revealed that the solid-fluid interaction heavily governs the particle dynamic behaviours. Specifically, compared to dry case, particles in three multiphase cases are characterized by larger flow mobility and greater shear rate while smaller basal normal force. In addition, a power-law distribution with a crossover to a generalized Pareto Distribution is recommended to fit the distribution of normalized interparticle contact force

Fabrication and properties of flame retardant composites made of abandoned peanut hull, polypropylene and thermoplastic polyurethanes

U radu se razmatra mogućnost recikliranja poljoprivrednog otpada i smanjenje zagađenje okoliša izradom polimernim kompozitima za zaštitu od gorenja od nezbrinutih ljusaka kikirikija kao materijala za pojačavanje, nezbrinutog polipropilena (PP) i nezbrinutih termoplastičnih poliuretana (TPU) kao matričnih materijala i magnezijevog hidroksida kao sredstva za zaštitu od gorenja. Ispitana su mehanička svojstva kao i svojstva zaštite kompozita od gorenja te su analizirana ortogonalnim eksperimentom i eksperimentima faktorske analize. Uključeni su i optimirani sljedeći uvjeti prerade: maseni udio ljuske kikirikija 40 %, omjer PP i TPU 3:1, temperatura vrućeg prešanja 170 °C, pritisak vrućeg prešanja 12 MPa, vrijeme vrućeg prešanja 10 min, vrijeme plastificiranja 5 min. Pri navedenim uvjetima dobivena su sljedeća svojstva kompozita: gustoća 1,12 g/cm3, prekidna čvrstoća 23,85 MPa, čvrstoća na savijanje 46,29 MPa i apsorpcija energije udara 10,19 kJ/m2. Osim toga, dodatkom magnezijevog hidroksida od 100 % (u odnosu na matricu) u optimiranim uvjetima prerade dobivena su sljedeća mehanička svojstva i svojstva zaštite od gorenja kompozita: granični indeks kisika 29,7, gustoća 1,27 g/cm3, prekidna čvrstoća 10,37 MPa, čvrstoća na savijanje 29,42 MPa i apsorpcija energije udara 3,85 kJ/m2.Fabrication and properties of flame retardant composites made of abandoned peanut hull, polypropylene and thermoplastic polyurethanes Lihua Lv*1,2, Xinyue Wang*1,2, Yongling Yu*1,2, and Jing Cui*2 In order to make possible recycling agriculture waste and decrease the environment pollution caused by high polymer, flame retardant composites made of abandoned peanut hull as reinforced materials, abandoned PP (polypropylene) and abandoned TPU (thermoplastic polyurethanes) as matrix materials and magnesium hydroxide as flame retardant reagent were discussed in this article. And the mechanical properties and flame retardant properties of composites were tested and analyzed by orthogonal experiment and single factor analysis experiments. And the optimized processing conditions were concluded as follows: mass fraction of peanut hull 40 %, ratio of PP and TPU 3:1, hot pressing temperature 170 ℃, hot pressing pressure12 MPa, hot pressing time10 min, and plasticizing time 5 min. Under above conditions, properties of composites were as follow: density 1.12 g/cm3, tensile strength 23.85 MPa, bending strength 46.29 Mpa and impact energy absorption 10.19 kJ/m2. Besides, with magnesium hydroxide whose dosage was 100 % (compared to the matrix) being added under the optimized processing conditions, the mechanical properties and flame retardant properties of composites were as follow: LOI 29.7, density 1.27 g/cm3, tensile strength 10.37 MPa, bending strength 29.42 MPa and impact energy absorption 3.85 kJ/m2

PVO: Panoptic Visual Odometry

We present PVO, a novel panoptic visual odometry framework to achieve more comprehensive modeling of the scene motion, geometry, and panoptic segmentation information. Our PVO models visual odometry (VO) and video panoptic segmentation (VPS) in a unified view, which makes the two tasks mutually beneficial. Specifically, we introduce a panoptic update module into the VO Module with the guidance of image panoptic segmentation. This Panoptic-Enhanced VO Module can alleviate the impact of dynamic objects in the camera pose estimation with a panoptic-aware dynamic mask. On the other hand, the VO-Enhanced VPS Module also improves the segmentation accuracy by fusing the panoptic segmentation result of the current frame on the fly to the adjacent frames, using geometric information such as camera pose, depth, and optical flow obtained from the VO Module. These two modules contribute to each other through recurrent iterative optimization. Extensive experiments demonstrate that PVO outperforms state-of-the-art methods in both visual odometry and video panoptic segmentation tasks.Comment: CVPR2023 Project page: https://zju3dv.github.io/pvo/ code: https://github.com/zju3dv/PV

Explainable Graph Neural Network for Alzheimer's Disease And Related Dementias Risk Prediction

Alzheimer's disease and related dementias (ADRD) ranks as the sixth leading cause of death in the US, underlining the importance of accurate ADRD risk prediction. While recent advancement in ADRD risk prediction have primarily relied on imaging analysis, yet not all patients undergo medical imaging before an ADRD diagnosis. Merging machine learning with claims data can reveal additional risk factors and uncover interconnections among diverse medical codes. Our goal is to utilize Graph Neural Networks (GNNs) with claims data for ADRD risk prediction. Addressing the lack of human-interpretable reasons behind these predictions, we introduce an innovative method to evaluate relationship importance and its influence on ADRD risk prediction, ensuring comprehensive interpretation. We employed Variationally Regularized Encoder-decoder Graph Neural Network (VGNN) for estimating ADRD likelihood. We created three scenarios to assess the model's efficiency, using Random Forest and Light Gradient Boost Machine as baselines. We further used our relation importance method to clarify the key relationships for ADRD risk prediction. VGNN surpassed other baseline models by 10% in the area under the receiver operating characteristic. The integration of the GNN model and relation importance interpretation could potentially play an essential role in providing valuable insight into factors that may contribute to or delay ADRD progression. Employing a GNN approach with claims data enhances ADRD risk prediction and provides insights into the impact of interconnected medical code relationships. This methodology not only enables ADRD risk modeling but also shows potential for other image analysis predictions using claims data

A novel household fill material fabricated from waste peanut shells and polyurethane with flame retardant and antibacterial functions

Velike količine ljuski kikirikija odbacivale su se svake godine u Kini i spaljivale ili odlagale na odlagališta otpada, čime se nije samo zagađivao okoliš, već su to bila i neiskorištena prirodna bogatstva. Zato je izrađen materijal za ispunu s funkcijama protiv gorenja i antibakterijskim svojstvima od nezbrinutih ljuski kikirikija i nezbrinutog termoplastičnog poliuretana primjenom plastifikacije, miješanja i vrućeg prešanja. Glavni faktori koji utječu na učinak sredstva protiv gorenja i regresijski model graničnog indeksa kisika dobiveni su analizom odzivne površine. Regresijski model pomogao je u predviđanju sposobnosti sredstva protiv gorenja i postizanju optimalnog uvjeta pripreme koji su sljedeći: maseni udio ljuski kikirikija 49,5%, maseni udio amonijevog fosfata 4,4 %, maseni udio sredstva protiv gorenja od termoplastičnog poliuretana (TPU) 14,2 %, i u tim uvjetima granični indeks kisika materijala bio je 32,78 %. Nakon dodatka 3 % viskera tetraigličastih ZnO u istim uvjetima, granični indeks kisika bio je 32,7 %, a antimikrobni stupanj Staphylococcus aureus, Escherichia coli i Salmonelle iznosio je 96,03, 96,98 odn. 92,33 %.Large amounts of peanut shells were abandoned each year in China and the abandoned peanut shells were subjected to incineration or landfill, which not only polluted environment, but also wasted resources. So, a household fill material with flame retardant and antibacterial functions was fabricated with abandoned peanut shell and discarded thermoplastic polyurethane by plasticizing, blending and hot pressing. The main factors that affect the flame retardant performance and the regression model of limiting oxygen index were obtained by response surface analysis. The regression model helped to predict materials flame retardant ability and achieve the optimal preparation condition, which as follows: the peanut shell mass fraction 49.5 %, ammonium polyphosphate mass fraction 4.4%, Thermoplastic polyurethanes (TPU) flame retardants mass fraction 14.2 %, under these conditions, the limiting oxygen index of materials was 32.78 %. While after added 3wt% Tetra-needle like ZnO whiskers at the same condition, the limiting oxygen index was 32.7 %, and the antimicrobial ratio of staphylococcus aureus, escherichia coli and salmonella reached 96.03, 96.98 and 92.33 % respectively

Process Optimization of Total Saponins from Adventitious Roots of Ginseng and Their Antioxidant and Anti-fatigue Effects

Objective: To investigate the optimal extraction process and antioxidant and anti-fatigue activities of ginseng adventitious roots total saponins (GARS). Methods: GARS was extracted using ethanol reflux method, and the effects of different factors such as ethanol concentration, solvent-to-solid ratio, extraction temperature, and extraction time on the content of GARS were studied by orthogonal test. The antioxidant and anti-fatigue activities of GARS were determined by measuring the scavenging abilities of DPPH, ABTS+, PTIO, ·OH, and O2−·, as well as the reducing power. The effects of GARS on the swimming and climbing times of mice, the contents of muscle/gallbladder glycogen, lactate (LD), and urea nitrogen (BUN) were determined to evaluate its anti-fatigue ability. Results: The optimal extraction conditions were ethanol concentration of 70%, solvent-to-solid ratio of 1:30 g/mL, extraction temperature of 70 ℃, and extraction time of 40 min, with a GARS content of 107.85 mg/g. GARS showed significant antioxidant and anti-fatigue activities, with its antioxidant and reducing power being positively correlated with its concentration. GARS significantly prolonged the swimming and climbing times of mice (P<0.05), increased the contents of muscle/gallbladder glycogen (P<0.05), decreased the levels of LD and BUN, and increased the activity of lactate dehydrogenase (LDH), indicating its anti-fatigue ability. Conclusion: The optimal extraction process of GARS was ethanol reflux method with ethanol concentration of 70%, solvent-to-solid ratio of 1:30 g/mL, extraction temperature of 70 ℃, and extraction time of 40 min. GARS showed significant antioxidant and anti-fatigue activities, which can support the development of ginseng adventitious roots as a food or health supplement ingredient

Valley-polarized Exitonic Mott Insulator in WS2/WSe2 Moir\'e Superlattice

Strongly enhanced electron-electron interaction in semiconducting moir\'e superlattices formed by transition metal dichalcogenides (TMDCs) heterobilayers has led to a plethora of intriguing fermionic correlated states. Meanwhile, interlayer excitons in a type-II aligned TMDC heterobilayer moir\'e superlattice, with electrons and holes separated in different layers, inherit this enhanced interaction and strongly interact with each other, promising for realizing tunable correlated bosonic quasiparticles with valley degree of freedom. We employ photoluminescence spectroscopy to investigate the strong repulsion between interlayer excitons and correlated electrons in a WS2/WSe2 moir\'e superlattice and combine with theoretical calculations to reveal the spatial extent of interlayer excitons and the band hierarchy of correlated states. We further find that an excitonic Mott insulator state emerges when one interlayer exciton occupies one moir\'e cell, evidenced by emerging photoluminescence peaks under increased optical excitation power. Double occupancy of excitons in one unit cell requires overcoming the energy cost of exciton-exciton repulsion of about 30-40 meV, depending on the stacking configuration of the WS2/WSe2 heterobilayer. Further, the valley polarization of the excitonic Mott insulator state is enhanced by nearly one order of magnitude. Our study demonstrates the WS2/WSe2 moir\'e superlattice as a promising platform for engineering and exploring new correlated states of fermion, bosons, and a mixture of both

Dynamic Prognosis Prediction for Patients on DAPT After Drug-Eluting Stent Implantation: Model Development and Validation

BACKGROUND: The rapid evolution of artificial intelligence (AI) in conjunction with recent updates in dual antiplatelet therapy (DAPT) management guidelines emphasizes the necessity for innovative models to predict ischemic or bleeding events after drug-eluting stent implantation. Leveraging AI for dynamic prediction has the potential to revolutionize risk stratification and provide personalized decision support for DAPT management. METHODS AND RESULTS: We developed and validated a new AI-based pipeline using retrospective data of drug-eluting stent-treated patients, sourced from the Cerner Health Facts data set (n=98 236) and Optum\u27s de-identified Clinformatics Data Mart Database (n=9978). The 36 months following drug-eluting stent implantation were designated as our primary forecasting interval, further segmented into 6 sequential prediction windows. We evaluated 5 distinct AI algorithms for their precision in predicting ischemic and bleeding risks. Model discriminative accuracy was assessed using the area under the receiver operating characteristic curve, among other metrics. The weighted light gradient boosting machine stood out as the preeminent model, thus earning its place as our AI-DAPT model. The AI-DAPT demonstrated peak accuracy in the 30 to 36 months window, charting an area under the receiver operating characteristic curve of 90% [95% CI, 88%-92%] for ischemia and 84% [95% CI, 82%-87%] for bleeding predictions. CONCLUSIONS: Our AI-DAPT excels in formulating iterative, refined dynamic predictions by assimilating ongoing updates from patients\u27 clinical profiles, holding value as a novel smart clinical tool to facilitate optimal DAPT duration management with high accuracy and adaptability