Daily Production Research Articles

Cyanohydrin Equilibria Implicate Non-Aromatic Aldehydes in Photochemical Production of Oceanic Carbon Monoxide.

Carbonyls have previously been dismissed as significant precursors for carbon monoxide (CO) photoproduction from natural chromophoric dissolved organic matter (CDOM). Here, we used hydrogen cyanide (HCN), which reacts with carbonyls to form photochemically inert cyanohydrins, as a probe to re-examine the role of carbonyls in CO photoproduction. Adding HCN to low-absorbance euphotic zone seawater decreased CO photoproduction. Modeling [HCN] (∼5 to 364 μM) vs the percent decrease in CO photoproduction (%CO↓) yielded carbonyl-cyanohydrin dissociation equilibrium constants, KD, and maximum %CO↓, %CO↓max values. Four Atlantic and Pacific seawater KDs (66.7 ± 19.6 μM) overlap aqueous aliphatic but not aromatic aldehyde KDs. Phenylacetaldehyde (PA) and other β,γ-unsaturated aldehydes are proposed as prototypical CO precursors. Direct photolysis of ∼10 nM PA can supply the measured daily production of HCN-sensitive CO at an open-ocean site near Bermuda. HCN's %CO↓max was 31 ± 2.5% in North Atlantic seawater vs the 13 ± 2.5% inhibition of CO photoproduction by borohydride, a dilemma since only borohydride affects most ketones. Borohydride also decreased CDOM absorption much more than did HCN. This puzzle probably reflects differing steric and solvation requirements in HCN- and borohydride-CDOM reactions. This study demonstrates cyanophilic aldehydes to be a significant source of open-ocean CO and reveals new clues regarding CDOM photochemistry mechanisms.

Optimizing solar power generation forecasting in smart grids: a hybrid convolutional neural network -autoencoder long short-term memory approach

Incorporating zero-carbon emission sources of energy into the electric grid is essential to meet the growing energy needs in public and industrial sectors. Smart grids, with their cutting-edge sensing and communication technologies, provide an effective approach to integrating renewable energy resources and managing power systems efficiently. Improving solar energy efficiency remains a challenge within smart grid infrastructures. Nonetheless, recent progress in artificial intelligence (AI) techniques presents promising opportunities to improve energy production control and management. In this study, initially, we employed two different Machine learning (ML) models: Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM), to forecast solar power plant parameters. The analysis revealed that the LSTM model performed better than RNN in terms of Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Mean Squared Error (MSE). Following a review of the LSTM model’s graphical results, it was further enhanced by combining Autoencoder with LSTM, creating the Autoencoder LSTM (AELSTM) model. Next, a new hybrid model was introduced: Convolutional Neural Network-Autoencoder Long Short-Term Memory (HCAELSTM), designed to boost prediction accuracy. These models were trained on a one-year real-time solar power plant dataset for training and performance assessment. Ultimately, the hybrid HCAELSTM model surpassed the AELSTM model in terms of MAPE, MAE, and MSE. It excelled in MAPE scores for Daily Power Production, Peak Grid Power Production, and Solar Radiance, achieving low scores of 1.175, 2.116, and 1.592 respectively, demonstrating superior accuracy. The study underscores the importance of AI and ML, in particular, the hybrid model HCAELSTM, in enhancing the smart grid’s ability to integrate renewable energy sources. The hybrid model excels at accurately forecasting key measurements, improving solar power generation efficiency within the smart grid system which also plays a key role in the broader shift toward the fourth energy revolution.

Pharmaceutical waste from a Danish hospital.

The healthcare sector is a major contributor of greenhouse gas emissions, and reduction and proper sorting of healthcare waste is essential to achieve sustainable healthcare. This study aimed to characterize the quantity and composition of pharmaceutical waste from a major Danish hospital. Pharmaceutical waste was collected from Odense University Hospital, including departments located in both Odense and Svendborg. The average daily production of pharmaceutical waste was 1150 g/day in Odense and 5967 g/day in Svendborg, with the operating rooms in Svendborg contributing 3143 g/day. The amount and composition of pharmaceutical waste varied greatly between departments, but some common patterns were identified. Propofol accounted for about one third of the pharmaceutical waste obtained from operating rooms. Antibiotics for systemic use constituted a significant proportion of the pharmaceutical waste from several departments and were the therapeutic group from which most different drugs were identified. Paracetamol accounted for 33.5% of the discarded tablets/capsules in Odense and 12.6% in Svendborg. Medications dispensed by automated dose dispensing accounted for a significant proportion of the discarded tablets/capsules in departments using this service. This study highlights some key areas for reduction and management of pharmaceutical waste and contributes to the currently limited evidence within this area.

Magnesium-L-threonate improves sleep quality and daytime functioning in adults with self-reported sleep problems: A randomized controlled trial

Objective/BackgroundSleep problems challenge overall wellbeing. Magnesium has been implicated to benefit sleep, although the clinical evidences varied based on the magnesium source used. Magnesium L-threonate (MgT) is a promising intervention due to its brain bioavailability and effects on cognition, memory and mood. We investigated MgT supplementation on sleep quality and daily function. Patients/methodsEighty 35–55-year-olds with self-assessed sleep problems participated in a randomized, double-blind, placebo-controlled, parallel-arm study, taking 1 g/day of MgT or placebo for 21 days. Sleep and daily behaviors were measured subjectively using standardized questionnaires including the Insomnia Severity Index, Leeds Sleep Evaluation Questionnaire, and Restorative Sleep Questionnaire, and objectively using an Oura ring. The Profile of Mood States questionnaire and a daily diary were used to evaluate mood, energy and productivity, and record any safety concerns. ResultsThe MgT group maintained good sleep quality and daytime functioning, while placebo declined. From objective Oura ring measurements, MgT significantly (p < 0.05) improved vs placebo deep sleep score, REM sleep score, light sleep time, and activity and readiness parameters activity score, activity daily movement score, readiness score, readiness activity balance, and readiness sleep balance. From subjective questionnaires, MgT significantly (p < 0.05) improved vs placebo behavior upon awakening, energy and daytime productivity, grouchiness, mood and mental alertness. MgT was safe and well tolerated. ConclusionsThis showed MgT improved sleep quality, especially deep/REM sleep stages, improved mood, energy, alertness, and daily activity and productivity. These are consistent with how MgT works in neuron cells and animal models, suggesting broader positive impacts on overall brain health.

Improving Milk Yield, Milk Quality, and Follicular Functionality Behavior in Dairy Cows from the Implementation of Microencapsulated Chili Pepper Supplements in Their Diets.

The present study evaluates the effect of including microencapsulated hot chili pepper (MHCP) in the diet of crossbred dairy cows on the volume and quality of milk and on ovarian morphofunctionality. Twenty-four crossbred females in their lactating period were used. The cows were divided into two experimental groups, a control (CT) and an MHCP -supplemented group (CP) given 1 g a day per animal of microencapsulated hot chili in concentrate for 42 days. Over seven weeks of daily milk production was measured, and sample milk was collected weekly for composition analysis. Animals were subject to an ovulation synchronization protocol on day 0 (D0), and an intravaginal progesterone (P4) implant, estradiol benzoate, and prostaglandin (PGF2α) were administered. On D8, the P4 implant was removed and PGF2α, equine chorionic gonadotropin, and estradiol cypionate were administered to the animals. The ovarian dynamics were evaluated in B mode and color Doppler. There were significant differences (p < 0.05) in the group X time interaction, the volume of milk produced, and the amount in kg/day of milk components. There was a higher percentage of vascularization in the preovulatory follicle in the CP group (p ≥ 0.10). The findings show that the inclusion of MHCP in the diet of dairy cows does influence their milk production and reproduction.

Effect of Postbiotic Supplementation on Nutrient Digestibility and Milk Yield during the Transition Period in Dairy Cows.

The metabolites secreted by probiotics or released after their lysis are called postbiotics. They provide physiological benefits to the host, preventing the colonisation of pathogens by improving the intestinal environment for beneficial commensal bacteria, which reduces the incidence of digestive disorders and improves the immune system. The aim of this work was to evaluate the addition of postbiotics to dairy cow rations during the transition period on nutrient digestibility, composition, and milk yield. The effects of two postbiotics were evaluated in twelve Friesian cows from 30 days before calving to two months of lactation. The animals were randomly allocated to two treatment groups: control (CT) and supplemented with postbiotics (PC and PR). Feeding was ad libitum with 60/40 of forage/concentrate ratio on dry matter basis. Daily feed intake and milk production were recorded individually throughout the study. Two digestibility balances were performed, one before parturition and one after parturition. Colostrum was sampled at first milking and milk was sampled weekly. Data were analysed using a mixed mode in R software 4.4.1. The results suggest that postbiotic supplementation in late gestation and early lactation increase the voluntary intake of dry matter, especially in the PR treatment, with higher apparent total tract digestibility of dry matter, organic matter and neutral detergent fibre. Both treatments including postbiotics induced an increase in colostral immunoglobulin concentration. Milk production of cows receiving the PC treatment was the highest, with high fat and protein yields and a higher persistence of the production curve throughout the lactation.

MOF-801 enabled rapid-cycling atmospheric water harvester with vertically aligned photothermal channel

Sorption-based atmospheric water harvesting (SAWH) with metal–organic frameworks (MOFs) offer a promising solution to freshwater scarcity in arid regions. However, given the powder nature of MOFs, scaling-up such a concept with industrially favorable monolithic MOFs in bulky sizes remains challenging. In this study, a pore vertically aligned nanocomposite sorbent where MOF-801 is anchored at the polymeric structure surface was prepared via Fe3+-induced crosslinking and directional freezing methods. The continuous vertically aligned hierarchical pores not only offer sufficient sites for MOF-801 loading but also provide rapid moisture transport channels, as well as high solar-thermal conversion efficiency, resulting in a rapid ab/desorption duration of 2 h (including an absorption process for 1 h and a desorption process for 1 h under 1 sun irradiation) and multicycle daily water production of 1.40 L kg−1 at low RH of 20 % and 5.57 L kg−1 at higher RH of 80 %, respectively, which is outperformed most of the state-of-the-art MOFs based SAWH materials. This work provides a simple and effective way to shorten MOF-801 ab/desorption duration, which has great means for designing of high-performance solar-driven water harvesting materials.

Performance analysis of an SPD integrated with a stepped pyramid solar still: experimental study

AbstractWater scarcity poses a major challenge for the next generation. Additionally, increasing agricultural and industrial activities are creating an unprecedented demand for freshwater supplies throughout Egypt. The Nile River serves as Egypt’s primary water source, accounting for 69.4% of the total water resources. Moreover, Egypt's annual share of water has decreased to approximately 500 m3 per person because of the completion of the Renaissance dam in Ethiopia and the rapid increase in population growth. Solar distillation has been presented as a long-term and promising method for producing edible drinkable water. The present research goal is to increase the daily freshwater output from a stepped pyramid solar still (SPSS) by coupling with a solar parabolic dish (SPD). The proposed system consists of a conventional pyramid solar still (CPSS) and a modified pyramid solar still (MPSS). The main components of an MPSS include an SPSS, a PV panel, an SPD with a spiral coil, a circulating pump, and an Arduino Mega Unit. The developed system was designed, fabricated, and verified from July to August 2023 at the Faculty Engineering, South Valley University. The results of the experiments indicated that the daily productivity of freshwater from MPSS was greater than that from CPSS and reached 6038 and 2612 mL/day, respectively, with an increase in the daily yield of 131%. The peak energy efficiency is 56% for the MPSS and 24% for the CPSS. The peak exergy efficiency is 4.26% for the MPSS and 3.79% for the CPSS. Additionally, the cost analysis of the suggested system revealed that the estimated cost per liter of the proposed system is 0.01635 USD/L.

A machine vision tool for multi-color H2O2 sensing by MoOx nanoparticles with oxygen vacancies

Improving the ease of operation and portability of hydrogen peroxide (H2O2) detection in daily production and life holds significant application value. However, it remains a challenge to achieve rapid colorimetric detection of H2O2 and color change quantification. In this study, we achieved rapid and visual detection of H2O2 by MoOx (2 ≤ x ≤ 3) nanoparticles with rich oxygen vacancies using machine vision. As the concentration of H2O2 increases, the detection system exhibited a visible multi-color change from blue to green and then yellow and the absorption peak near 680 nm measured by the UV–visible spectrophotometer gradually decreased. With excellent sensitivity, a wide linear range of 0.1–600 μmol/L, concentrations as low as 0.1 μmol/L can be detected with good selectivity towards H2O2. The sensing mechanism of detecting H2O2 by the change of oxygen vacancies in MoOx was revealed through characterization methods such as XPS, EPR, and DFT. In addition, the Hue, Saturation, Value (HSV) visual analysis system based on MoOx was constructed to assist in the rapid, portable, and sensitive monitoring of H2O2 in practical application scenarios. This work offers an easy-to operate, low cost, and convenience for achieving rapid colorimetric determination of H2O2 and has broad application prospects in daily life and industrial production.

Phenology of gross primary productivity in rivers displays high variability within years but stability across years

AbstractModeling and sensor innovations in the last decade have enabled routine and continuous estimation of daily gross primary productivity (GPP) for rivers. Here, we generate and evaluate within and across year variability for 59 US rivers for which we have compiled a 14‐yr time series of daily GPP estimates. River productivity varied widely across (median annual GPP 462 g C m−2 yr−1, range 19–3445 g C m−2 yr−1) and within rivers (CVGPP‐Inter 5.7–37.3%). Within this dataset, we found that five rivers have become consistently more productive over time, while 11 rivers have become consistently less productive. Furthermore, trends in ecosystem phenology were identified, where cumulative annual GPP was reached earlier (n = 3) and later (n = 13) in the year across the 25th, 50th, 75th, and 95th percentiles. Understanding the drivers of productivity trends in rivers will elucidate patterns in river food webs and the functional role of river biogeochemistry.

DETERMINATION OF FERTILITY OF FIRST CALVING COWS UNDER DIFFERENT HOUSING

The best fertilization rate of firstborns was established at the farm “Kutuzivka” under free housing, it was 11% times higher than at the farm “Agroprogres”, and 7% higher than the farm “Dekabrystiv”. The influence of the "farm" factor – 1.0% was probable (p &lt; 0.001). In our opinion, the higher percentage of fertile inseminations of firstborns is explained by the use of an automatic system for determining sexual desire at the farm "Kutuzivka".The season of the year was the most influential factor in the fertilization of the first-born cows among the factors of the external environment. The largest number of inseminations of first-borns at farm “Agroprogres” was in the summer (28%), and the lowest in the winter (23%). On the farm of the farm “Dekabrystiv”, a greater number of inseminations were also carried out in the summer (30%) months, and the smallest share of inseminations – in the winter (21%). The nature of this impact is explained not by the state of the environment, but by the housing technology and feeding animals. During the first insemination after calving, a slightly lower percentage of fertilization of first-borns was observed in all studied farms. During the second insemination, it increased by 4% in the animals of the farm “Agroprogres”, by 7% in the farm “Decembrists”, by 3% in the farm "Kutuzivka". In the subsequent growth of the number of insemination of firstborns in the first two farms did not lead to probable changes in fertility. At the same time, the animals of the the farm "Kutuzivka", starting from the 5th insemination, were fertilized by 11–23% less than from the second insemination. The influence of this factor on the effectiveness of insemination of cows was improbable in the farm “Agroprogres” and amounted to 0.7%, in the farm “Dekabristov” and the farm "Kutuzivka" 0.8%. It was established that in all experimental farms, the number of artificial insemination after the first calving of cows is not the main factor that determines its fertility. The main factor related to the efficiency of insemination is the stage of lactation. Correlation coefficients of the lactation day with the number of artificial insemination in experimental farms ranged from +0.545 to +0.723 and with the daily productivity of first-borns equaled from +0.156 to +0.383. Low fertility in the first 90 days of lactation is primarily due to a high milk dominance, possibly a negative energy balance during this period of lactation.

Early prediction of milk yield from Holstein-Friesian cow lactation in Querétaro, Mexico

Objective: To obtain a machine learning (ML) model to predict the milk yield adjusted to 305 d (MY305) from the same lactation period. Design/methodology/approach: A database of test days (TD) was used, made up by 11,892 records of daily milk production from cows with more than 150 days in milk (DIM), from 19 barns in Querétaro, Mexico. The milk production was standardized to specific DIMs (5, 10, 20, 30 and 40) and estimations of MY305 were obtained with these, using ML models. The following were incorporated as explicative variables of the herd: month of birth of the cow, month of start of lactation, number of lactation, number of days for three daily milking events, and the two first linear scores of somatic cells. Results: The best goodness of fit was achieved with ensemble models, obtaining a deviance of 1503584 in the training with 80% of data chosen randomly, while with 20% of the data reserved to evaluate the deviance model it was 1576776. The rate between data observed and predictions of MY305 of the ensemble models had a coefficient of determination of r2 = 0.79 and RMSE of 1256. In the best individual model (deviance of 2281420) of ‘deep learning’ type, the most important variables were daily milk production of 30, 10, 5 and 20 DIM (19.9, 16.6, 16.2 and 12.8%, respectively). Limitations on study/implications: The value of RMSE is high, although the TD databases are generated systematically and usually there are not many barns represented. Findings/conclusions: For the database examined, milk production in the early phase of lactation together with a set of automatic learning models resulted in an adequate prediction of MY305.

Evaluación económica de un esquema de alimentación complementaria continua en cabras locales en pastoreo

Objective. To evaluate the economic viability of incorporating a long term-supplementary feeding scheme in grazing local goats from the last third of gestation and throughout lactation. Materials and methods. Twenty-four local goats were utilized: control group (CG, n=12) and supplemented group (SG; n=12). Daily milk production (DMP) was recorded in goats, per lactation (MPLT), and per lactation stage [early (MPEL) middle (MPML), and late lactation (MPLL)], duration of lactation (DL; days), final live weight (FLW), and final body condition (FBC). In kids, birth weight (BW), weight at sale (WS), age at sale (AS), and mortality rate were recorded. The economic analysis considered the costs of daily supplementation, labor for handling and care of the animals, and sale prices of milk and kids. Results. The best productive behavior and the highest incomes were obtained in SG (p&lt;0.05) for all variables, except AS and mortality rate. The highest values in CG (p&lt;0.05) were obtained in AS and economic profit in almost all the considered variables, Except in MPEL. Positive profits were obtained in SG in early and middle lactation, and negative in late lactation for SG, but with higher FBC. Conclusions. A long-term supplementary feeding is economically viable to increase income and profits from kids and milk sales and improves the body condition of the dams, which will benefit the productivity of the herd.

Design and assessment of an advanced renewable energy system with hydrogen and monomethylhydrazine production for space shuttles

A new renewable energy-driven advanced plant is designed and developed to produce multiple useful outputs, namely electricity, heat, cooling, hydrogen, and monomethylhydrazine (as space shuttle fuel) using solar and wind energies locally available. A specific location needed for this kind of unique work is identified as the Kennedy Space Center (KSC), which is known as located on Merritt Island, in the state of Florida (USA). It is also one of the National Aeronautics and Space Administration's (NASA) ten field centers. Since December 1968, KSC has been NASA's primary launch center of American spaceflight, research, and technology. This paper presents a comprehensive analysis of the subsystems that constitute the newly proposed system potentially for KSC and highlights their complex relationships and synergies. Leveraging renewable energy sources, such as concentrated solar power (CSP) and wind energy, coupled with the subsystem uniquely, such as the regenerative Rankine cycle, multi-stage flash (MSF) desalination unit, hydrogen production process, and Li–Br absorption chiller, the KSC's system maximizes energy conversion efficiency while minimizing environmental impact. Noteworthy outcomes include daily water production of 177.63 kg/s and hydrogen production of 169.61 kg/day, alongside annual energy contributions of 212,692,688 kWh for CSP and 66,158,552 kWh for wind turbines. More than that, it produces 23.04 kg/s MMH as fuel. Additionally, the overall energy and exergy efficiencies of the system are 30.6% and 33% respectively. Despite challenges, such as low efficiencies in certain subsystems, like mono methyl hydrazine (MMH) production, ongoing research and development efforts aim to optimize processes and enhance sustainability.

Theoretical analysis of upward-heating multiple-effect diffusion solar still with parabolic dish concentrator

Upward-heating multiple-effect diffusion solar still (UHMEDS) coupled with a parabolic dish concentrator (PDC) was proposed and analyzed theoretically. UHMEDS has cover glass, several partitions and preheating cover, and wicks are placed on the upper surfaces of partitions and preheating cover. Saline water preheated on the preheating cover flows down the partitions by gravitational force. The lowermost partition of UHMEDS was heated by solar energy collected by PDC. The basic behavior of UHMEDS at Fukuoka, Japan was analyzed theoretically by modeling heat and mass transfer in the still. The theoretical results show that PDC can collect the sunrays to the lowermost partition of UHMEDS effectively, and the overall daily productivity of the still was predicted to be about 18.5, 24.5 and 10.4 kg/m2day on spring equinox, summer and winter solstice days, respectively, which values are almost equivalent to those of other types of multiple-effect diffusion solar still.

Parent of origin genetic effects on milk production traits in a population of Iranian Holstein cows.

The aim was to estimate the relative contribution of imprinting effects from both paternal and maternal sides to phenotypic variation in milk production traits including 305 days milk yield (MY), average daily milk production (ADM), fat percentage (F%), protein percentage (P%), 305 days fat yield (FY), 305 days protein yield (PY), ratio of fat percentage to protein percentage (F:P) and somatic cell score (SCS) in Iranian Holstein cows. To do this, each trait was analysed with a series of four animal models, which were identical for fixed and additive genetic effects but differed for combinations of paternal and maternal imprinting effects. The log-likelihood ratio test (LRT) and Akaike's information criteria (AIC) were used to select the best model for each trait. Correlations between traits due to additive and imprinting effects were estimated by bivariate analyses. For all traits studied, fitting the imprinting effect led to a better data fit. Also, it resulted in a noticeable decrease in additive genetic variance from 8% (SCS) to 28% (F:P). A significant maternal imprinting effect was detected on all traits studied. Estimates of maternal imprinting heritability ( ) were 0.07 ± 0.02, 0.04 ± 0.01, 0.06 ± 0.01, 0.05 ± 0.01, 0.5 ± 0.01, 0.09 ± 0.02, 0.07 ± 0.02 and 0.06 ± 0.01 for MY, ADM, F%, P%, FY, PY, F:P and SCS, respectively. For F:P, in addition to the maternal imprinting effect, a significant paternal imprinting component was also detected with a 7% contribution to phenotypic variance of F:P. Estimates of direct heritability ( ) were 0.29 ± 0.02, 0.17 ± 0.01, 0.22 ± 0.02, 0.11 ± 0.01, 0.18 ± 0.02, 0.22 ± 0.02, 0.15 ± 0.04 and 0.06 ± 0.01 for MY, ADM, F%, P%, FY, PY, F:P and SCS, respectively. Maternal imprinting correlations (rmi) were in a wide range between -0.75 ± 0.15 (P%-SCS) and 0.95 ± 0.11 (MY-ADM). Additive genetic correlations (ra) ranged between -0.54 ± 0.05 (MY-P%) and 0.99 ± 0.01 (MY-ADM) and phenotypic correlations (rp) ranged from -0.30 ± 0.01 (MY-F%) to 0.93 ± 0.01 (MY-ADM). The Spearman's correlation between additive breeding values including and excluding imprinting effects deviated from unity especially for top-ranked animals implying re-ranking of top animals following the inclusion of imprinting effects in the model. Since including imprinting effects in the model resulted in better data fit and re-ranking of top animals, including these effects in the genetic evaluation models for milk production traits was recommended.

Photovoltaics Energy Potential in the Largest Greek Cities: Atmospheric and Urban Fabric Effects, Climatic Trends Influences and Socio-Economic Benefits

This comprehensive study explores the influence of aerosols and clouds on solar radiation in the urban environments of nine of Greece’s largest cities over the decade from 2014 to 2023. Utilizing a combination of Earth Observation data, radiative transfer models, and geographic information systems, the research undertook digital surface modeling and photovoltaic simulations. The study meticulously calculated the optimal rooftop areas for photovoltaic installation in these cities, contributing significantly to their energy adequacy and achieving a balance between daily electricity production and demand. Moreover, the research provides an in-depth analysis of energy and economic losses, while also highlighting the environmental benefits. These include a reduction in pollutant emissions and a decrease in the carbon footprint, aligning with the global shift towards local energy security and the transformation of urban areas into green, smart cities. The innovative methodology of this study, which leverages open access data, sets a strong foundation for future research in this field. It opens up possibilities for similar studies and has the potential to contribute to the creation of an updated, comprehensive solar potential map for continental Greece. This could be instrumental in climate change mitigation and adaptation strategies, thereby promoting sustainable urban development and environmental preservation.

Solid waste management and resource recovery during the 4-crew 180-day CELSS integrated experiment

In order to explore the management and treatment methods of solid waste in the Controlled Ecological Life Support System (CELSS) of future lunar bases, during the 4-crew 180-day integrated experiment, the Solid Waste Management and Treatment System (SWMTS) was built, in which the treatment of recyclable solid waste such as inedible plant parts and human excrement was completed through a combination of biological aerobic composting and high-temperature oxidation. Basic data on the types and amounts of solid waste generated during the 4-crew 180-day experiment mission were obtained. There were six types of solid wastes, including the work support wastes, the household support wastes, the plant cultivation wastes, the plant-based wastes, and crew feces. The daily average production was 0.67, 1.4, 0.32, 8.48, 0.534 kg/d, respectively. The proportion of plant-based wastes was high as 74.3 %, indicating that it was the most important part. By closed-loop air drying and graded crushing, all 1526.97 kg of plant-based waste was treated, with water recovery (about 1163.87 kg), as well as volume reduction and stabilization treatment. By incineration and aerobic composting treatment, 67.3 % (244.4 kg) of the plant-based wastes (dry weight) and all of the feces (96.26 kg) were converted, providing 339.54 kg carbon dioxide for plant growth. And 90.6 kg organic fertilizer was obtained. The fertilizer was highly mature, met safety requirements, and effectively improved lettuce yield. The recycling rate of renewable solid waste during the experiment reached 89.8 %. The efficient circulation of solid waste had been achieved during the 4-crew 180-day integrated experiment. The long-time experimental results have shown that the established solid wastes management and treatment system can timely treat biomass solid waste such as inedible parts of plants and crew feces, achieve timely recovery of water in such solid waste, and recycle carbon and other elements, which effectively improved the material closure of the system and ensured the successful 4-crew 180-day experiment. This work also maybe lay the foundation for the construction and operation of an ecological life support system for future lunar bases.

Advancements in GERD Treatment: Exploring Innovative Therapies

: Gastroesophageal Reflux Disease (GERD) stands as a prevalent and impactful gastrointes-tinal disorder, affecting a substantial portion of the global population. Characterised by the chronic backflow of stomach acid into the oesophagus, GERD presents itself with symptoms ranging from the discomfort of heartburn to more severe complications, including esophageal inflammation and respiratory issues [1]. The pervasive nature of this condition underscores the critical need for effective and evolving treatment strategies. As individuals worldwide grapple with the challenges posed by GERD, the quest for advancements in treatment options becomes paramount. The prevalence of GERD extends far beyond its physical manifestations, reaching into the realms of daily life, productivity, and overall well-being. Heartburn, regurgitation, and chest pain, hallmarks of GERD, can disrupt sleep patterns, hinder nutritional habits, and compromise the quality of life of an individual [2]. Moreover, the chronic nature of the disease raises concerns about long-term complications, such as Barrett's oesophagus and an increased risk of esophageal cancer. The burden imposed by GERD is not solely physical; it extends to psychological well-being, influencing factors like stress and anxiety. Recognising the multifaceted impact of GERD underscores the urgency of advancing treatment options to address the diverse needs of those affected [3].

A physical constraint-based machine learning model for shale oil production prediction

Shale oil has become a crucial unconventional resource, bolstering energy supply security, and it is important to accurately predict shale oil production dynamics. However, traditional prediction methods are faced with the challenges of large data fluctuations and numerous interference factors, which make accurate prediction difficult. This paper introduces a deep learning approach, specifically a physical constraint-based convolutional neural network combined with long short-term memory and attention mechanism (CNN-LSTM-AM), to predict shale oil production dynamics. Initially, the correlation analysis method is used to analyze the correlation strengths of features with the prediction target, and the data that are most strongly correlated with the daily oil production are screened out and preprocessed. Key features are then extracted; that is, the CNN layer's output is fed into the LSTM layer, the output of which informs a fully connected layer for time-series production predictions. The model integrates an AM to concentrate on vital data aspects. In the “five-linear flow” formula, the imbibition is hard-coded into the shale oil production prediction model. Physical constraints are introduced into the model. Data driven and empirical formulas are used to introduce a loss function as a constraint condition in the training process of a machine learning model to improve the interpretability and predictive ability of the model. During the tuning phase, Bayesian optimization is used to fine-tune the model's hyperparameters. Shapley additive explanation and accumulated local effects analyses are used to further assess and quantify the significance of the essential parameters, thus optimizing the prediction effectiveness.