The construction time of concrete structures in hydraulic tunnels is long, the construction environment is complex, and there are many influencing factors. The requirements for construction quality are high, not only to meet the strength requirements, but also to meet the design requirements of erosion resistance, crack resistance, and seepage resistance according to its specific operating environment. Therefore, evaluating the construction quality of concrete structures in hydraulic tunnels is of great significance. Considering the randomness and fuzziness of factors affecting the construction quality of concrete structures in hydraulic tunnels, this paper proposes a comprehensive evaluation model based on Combined Weighting (CWM) and Uncertainty Measurement Theory (UM). The IAHP method and CRITIC method are used to determine the subjective and objective weights of evaluation indicators, combined weighting is based on the principle of minimum entropy, and the UM method is used to evaluate the construction quality level. Finally, taking a hydraulic tunnel as an example, its construction quality grade is calculated to be III according to the evaluation model proposed in this paper, which matches with the engineering reality, and a comparative study is made with the mixture element topology theory at the same time. It is verified that the evaluation model can scientifically and reasonably evaluate the construction quality level of concrete structures in hydraulic tunnels.

The importance of knowing the advance time in drip irrigation lines lies in its precision, as this influences the proper management of water and nutrients delivery. The mathematical calculation to determine the advance time is based on the general hydraulic flow equation, which relates the fluid velocity and the cross-sectional area of the pipe to the volume of water flowing through it. However, in irrigation pipes, where the flow is a mixture of dripper properties, its pressure, the approach to solving the advance time is different, as it is the sum of individual advance times for each segment. To address this issue, Python 3.11 was used along with various libraries such as Pandas, NumPy, Matplotlib, Seaborn, and Scikit-learn to create a modeling environment and run algorithms that could predict outcomes. A program was developed that calculates the advance time for each segment of the drip line using partial velocities, and a dataset was generated that was used to train and test machine learning models. Several machine learning algorithms such as Linear Regression, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Decision Trees, and Random Forests were implemented to predict the advance time. Additionally, SciPy optimize was used to obtain multivariable equations that describe the advance time in drip irrigation lines. Results showed that the dripper flow has the greatest influence on the advance time, followed by the diameter and distance. Decision Tree and SVM models had the best accuracy with scores above 98%. Equations were found to calculate the advance time in the complete drip line and in 95% of its length, with coefficients of determination close to 99.33%. This study demonstrated the importance of understanding the relationship between dripper parameters and travel time in drip irrigation, as well as the utility of machine learning and optimization tools for predicting and modeling this phenomenon.

Exploring the growth and physiological response mechanisms of rice under continuous drought stress circumstances can provide a significant scientific foundation and technological assistance for meeting drought difficulties, improving drought resistance and rice (Oryza sativa L.) output, and ensuring food security. In this study, a rice field experiment was conducted under a rain shelter with five different treatments set up: P1 (drought stress from tillering stage), P2 (drought stress from jointing-booting stage), P3 (drought stress from heading-flowering stage), P4 (drought stress from grain filling stage), and CK (adequate water management throughout the growth stage). Continuous drought stress from different growth stages with four levels (mild, medium, serious, and extreme). The results showed that the effects of different drought stress treatments on rice growth varied significantly. Compared with the CK treatment, plant height was reduced by 12.10%, 8.14%, 3.83%, and 1.06% in the P1, P2, P3, and P4 treatments, respectively; and the number of tillers was reduced by 23.83%, 18.91%, 13.47%, and 8.68%, respectively. With the increase in drought stress levels, SPAD values and Rubisco activity of rice leaf continued to decrease; SOD activity showed a decreasing trend, but the decreasing trend of POD and CAT activities was not significant, while MDA content showed an increasing trend. For yield components, continuous drought stress significantly reduced spike length, effective number of spikes, thousand grain weight, grain number per spike, and fruiting rate of rice compared to CK treatments during the growth period. In general, continuous drought stress during the early growth period affected the effective spike number and the grain number per spike. Continuous drought stress after the grain filling stage had the least effect on yield and water use efficiency was much higher than other treatments. The results of these studies reveal the response of rice growth and physiology to continuous drought stress, which is important for agricultural practices.

Irrigation scheduling is used by growers to determine the right amount and timing of water application. In most parts of Mozambique, 90% of the total yearly precipitation occurs from November to March. The El Niño Southern Oscillation (ENSO) phenomenon influences the climate in Mozambique and affects the water demand for crop production. The objectives of this work were to quantify the effects of ENSO phenomenon on tomato crop water requirements, and to create the AgroClimate irrigation tool (http://mz.agroclimate.org/) to assist farmers in improving irrigation management. This study was based on daily grid-based climate information from 1983 to 2016 from the Climate Forecast System Reanalysis. Daily crop evapotranspiration was calculated by Hargreaves equation and crop coefficients. This tool is available online and considers different planting dates, ENSO phases, and crop growing season lengths. Irrigation needs varied from less than 250 mm per growing cycle during winter to 550 mm during spring. Both El Niño and La Niña influenced the irrigation scheduling, especially from November to March. El Niño periods were related with increased water demand due to drier and warmer conditions while the opposite was observed for La Niña. The ENSO information might be used to understand climate variability and improve tomato irrigation scheduling in Mozambique.

Detecting and monitoring changes in soil salinity through remote sensing provides an opportunity for field assessment in regions where on-site measurements are limited. This investigation, carried out in Siwa Oasis, Egypt, aimed to assess the efficacy of several soil salinity indices derived from Landsat 5 and 7 satellite images. To achieve this, 56 on-site ground measurements were utilized for evaluation purposes. The study aimed to improve the correlation between EC and index values and explore the relationship between salinity and changes in land cover. Eleven spectral indices were calculated for nine scenes captured in different months from August to December. The first approach involved stacking the data to identify the index with the strongest correlation with ground EC values, without the need for complex analysis or EC value ranging. In the second approach, the ground EC measurements were classified into seven different salinity categories, and the correlation coefficient was calculated between each index and each salinity category. The third approach analyzed the data temporally, considering different salinity levels. Lastly, a spatial correlation analysis was conducted between EC and index values in the fourth scenario. The initial approach revealed a weak correlation due to substantial variation in EC values. However, the SI index demonstrated the highest correlation coefficient of 0.38. In the second scenario, the S2 index exhibited the highest correlation of 0.96 for moderate salinity samples. The third scenario showed that the S1 index achieved the highest correlation value of 0.99 for moderately saline areas. In the fourth scenario, the SI index exhibited the strongest correlation among all four ponds with correlation coefficients of 0.23, 0.23, 0.18, and 0.61. Notably, the correlations observed in the second and third scenarios demonstrated higher correlation coefficients compared to both the first and fourth scenarios. The study highlighted significant variations in EC levels related to land cover types and pond elevation. Additionally, remote sensing methods detected a 48% increase in total vegetated area over 17 years, showing the potential of remote sensing techniques in salinity monitoring for expanding agriculture and improving land management.

The most recent National Research Council Nutrient Requirements for Sheep and Goats was published in 2007, one of the most consequential nutrient requirement recommendations for sheep and goats in the world. It is pertinent to continue the refinement and discussion of nutrient utilization and requirements for the purposes of enhancing the efficiency of production systems, maximizing resource economy, and minimizing the carbon footprint, among others. Progress has been made in the estimation of energy and protein requirements in sheep and goats, mainly utilizing empirical feeding experimentation, comparative slaughter technique and minimum endogenous loss method. In sheep, newer estimates of energy and protein requirements for maintenance and growth and partial efficiencies had been reported since 2007. There were suggestions that energy and protein requirements could have been affected by breed, wool growth, gender and size; with these reported values being similar or lower than the recommended values in international feeding systems such as NRC, ARC, INRA and AFRC. In goats, energy and protein requirements for growing goats were reported to be either higher or lower than the established recommendations, depending upon meat or dairy breeds. Effect of gender on energy requirement appeared to be related to the stage of growth or degree of maturity. Newer data also suggested that the guidelines of the major feeding systems for the entire growth phase may not be adequate for non-pregnant and non-lactating pubertal females. In multiparous pregnant goats, energy and protein requirements for maintenance did not appear to be affected by days of pregnancy, but efficiencies of metabolizable energy and metabolizable protein utilization for pregnancy were. There were suggestions that that metabolizable protein synthesis in the rumen can be predicted from energy intake using combined equations that encompass both sheep and goats, but more data on goats were called for to account for specific differences in nutrition. In addition to sulfur, there has been progress made on the estimation of maintenance and growth requirements of calcium, phosphorus, potassium and magnesium in goats, with suggestions on the consideration of gender and breed differences. Species, level of Intake, genotype, mature size, age, gender, body composition, use of tissue energy, activity, season, and acclimatization continue to be important considerations for the estimation of nutrient requirements in sheep and goats, with emerging factors such as climate change, heat stress, parasitism and secondary plant compounds. Model equations and partial efficiencies used by NRC to predict energy and protein requirements for maintenance, growth, lactation, and fiber have been highlighted and discussed for the purpose of a more focus discussion and refinement for the future. Potential and limitation of both traditional and emerging methodologies in determining the nutrient requirements in sheep and goats were discussed. To justify the research investment, emerging methodologies such as nutrigenomics will have to be linked more directly to the improvement of production efficiency via more precise prediction of nutrient requirement.

Our study aimed to assess sprinkler irrigation's impact on the activity of selected soil enzymes concerning nitrogen metabolism and oxidation-reduction processes in the soil with different doses of inorganic nitrogen fertilizers. The soil Alfisol was sampled from the experimental field of spring barley in the University Research Center in the central part of Poland in the moderate transitory climate during the growing seasons 2015–2017. The index resistance (RS) was derived to recognise the resistance enzymes of drought. In the maturity phase, nitrate reductase activity was at an 18% higher level in irrigated soils and the activity of other enzymes was higher in no-irrigated treatments by 25% in dehydrogenase, 22% in peroxydase 33% in catalase and 17% in urease case. The development stage of barley influenced nitrate reductase activity. Enzymatic activity has been changing in the examined years of the research, depending on the weather conditions. During the maturity stage, the soil's lower ammonium nitrogen content resulted from higher spring barley uptake due to drought stress. Irrigation probably contributes to increased leaching of nitrate in soil. The highest index of resilience was presented in the soil catalase activity.

Following technological advances, autonomous and remote-controlled ships are performing trials in some sea areas; a mandatory goal-based MASS code, expected to enter into force on 1 January 2032, is under discussion in the International Maritime Organization (IMO). However, there are no unified standards regarding verification methods to define operating limitations for cutting-edge technologies, which are expected to be elements of MASS, so it is difficult to assess the risk factors for the management and operation of MASS. This paper outlines the regulatory framework for MASS, followed by a discussion of its application, considering the differences between MASS and automated driving, and how to develop safety management and operation using standards for automated driving. A requirements management framework developed as a system and software engineering standards to build safety management and operation of MASS is proposed. It is also suggested that the requirements management framework could be used not only for MASS development but also for regulatory review from a requirements management view.

Drip irrigation systems are becoming more and more mature and are present extensively applied to increase crop yield and water use efficiency in Xinjiang, northwest China. To investigate the effects of irrigation quota on maize growth, the grain yield, and the irrigation water use efficiency (IWUE), a field experiment with four irrigation quotas (T1 4200m3·hm-2, T2 4800m3·hm-2, T3 5400m3·hm-2 and T4 6000m3·hm-2.) were conducted from 2013 to 2021 in Xinjiang China. The re-sults showed significant changes in maize growth, yield, and irrigation water use efficiency in response to different irrigation quotas. The plant height, leaf area index, SPAD, biomass, yield and harvest index of maize at different irrigation quotas all showed a “single peak curve”, and its change was closely related to the irrigation level. The growth index, dry matter accumulation, yield and irrigation water use efficiency with T3 were the highest. The dry matter transfer effi-ciency, contribution of dry matter translocation to grain and the harvest index with T3 showed significant increase of 13.86%, 26.06%, 29.93% and 7.62% compared to T1, respectively. In com-parison to T1, T2 and T4, the yield of T3 increased by 32.17%, 13.54% and 11.27% respectively, and the irrigation water use efficiency (IWUE) increased by 2.80%, 0.93% and 23.63% respectively. The significant correlations established between the maize yield and irrigation quotas could be sim-ulated by kuznets-style relation. The maize yield was negative correlated with irrigation quotas, When the irrigation quota (x) was 5376.73m3·hm-2, the maize yield (y) was 15841.00m3·hm-2. These results demonstrate that the optimized irrigation quota (5400m3·hm-2 treatment) can effectively improve the growth, yield and irrigation water use efficiency of drip irrigation maize in North-west China. In the meanwhile, it can provide theoretical reference and data support for the op-timal irrigation amount of drip irrigation maize in Northwest Xinjiang.

Variable flow emitters are a new type of emitter for underground drip irrigation that are used in locations where soil moisture is difficult to transport to the surface, which leads to crop emer-gence difficulties. The flow channel structure parameters of emitters affect the hydraulic per-formance of the emitter and its anticlogging ability. In this study, the variable flow emitter at the conventional low–flow water supply stage is taken as the research object. A combination of CFD numerical simulation of a two–phase flow of water and sand and clear water testing of test samples is applied to study the influence laws of 10 groups of variable flow emitter structural combinations on the effluent flow rate (q), flow index (x), particle passage rate and flow field inside the flow channel. The results show that the Realizable k–ε turbulence model can be used to simulate the flow field inside the variable flow emitter flow channel. The nRMSE between the measured and simulated values of q is 11.23%, and the relative error between the measured and simulated values of x is 4.66%, which gives a high simulation accuracy. The polar analysis shows that the tooth angle A has the smallest effect on the effluent flow rate q0.1 under 0.1 MPa, x, and particle passage rate of the variable flow emitter. The depth of the flow channel D, the spacing of the teeth B, and the height of the teeth E have a different order of precedence in the influence of the three indices, which are D > B > E > A, B > E > D > A, and E > B > D > A, respectively. The value of particle passage rate is positively correlated with the mean flow velocity (v) and the mean turbulent kinetic energy (k) in the flow channel, and particle passage rate tends to increase and then decrease with the increase of x. The retention time (t) of the particles in the flow channel is closely related to the magnitude of v and k. Three multivariate linear regression equations (R2 = 0.883 ~ 0.995) were constructed for q0.1, x, and particle passage rate versus the flow channel structural parameters. Using the scipy.optimize.minimize function in Python, we de-termined the three optimal combinations of flow channel structural parameters corresponding to better hydraulic performance: q0.1 is 1.5 or 2 L/h (objective function y = min x), better clogging resistance (y = max particle passage rate), and both (y = min (0.5 x - 0.5 particle passage rate)). The research results can provide a ref-erence for the optimal design of variable flow emitters.

The aim of this work was to describe the physiological limitations induced under drought conditions in young sweet orange trees, including the crop capability to recuperate its physiological status once re-watering, and after a water withholding. The trial was conducted during two consecutive months (July and August) in two-years old sweet orange trees. Three irrigation treatments were arranged: A full irrigation treatment; (FI); a low-frequency deficit-irrigation treatment (LFDI); and a sustained-deficit irrigation (SDI) treatment. The obtained results showed that for Stem values below -1.5 MPa, significant decreases in gas exchange rates, evidencing diffusive limitations to drought stress. Additionally, there was evidence of increased osmolyte synthesis, compatible as a defensive response to prevent oxidative damage. Likewise, the isohydric behaviour observed also resulted in significant decreases in gs even for moderate stress situations. In addition, it was observed that in the LFDI treatment, the stress levels reached caused a significant increase in the levels of Pro and MDA, which were not equal to those detected in FI during the recovery periods, which leads us to assume that potential values below -1.8 MPa would be causing significant oxidative damage with no capacity for subsequent recovery, and probably affecting the final yield.

Crop production in Afghanistan suffers from limited phosphorus (P) availability, which severely hinders national agriculture sustainability. This present study hypothesized that deep fertilizer placement could significantly enhance uptake of immobile P and thus tissue P accumulation and crop yield. A two-year pot experiment growing two maize (Zea mays) hybrid cultivars (Xida-789 and Xida-211) was therefore conducted to test these hypotheses under three contrasting fertilizer placement methods (broadcast, side band, and deep band). In doing so, P concentrations in both maize tissues and soils were compared at 45, 60, and 115 days after sowing (DAS) under nine combinations of nitrogen (N) and P fertilizer rates (kg ha-1: N112P45, N112P60, N112P75, N150P45, N150P60, N150P75, N187P45, N187P60, N187P75). Results have shown that deep band placement significantly increased P uptake efficiency, leading to greater P concentration and accumulation in maize tissues compared to other two fertilization methods. This improved P uptake was attributed to several factors associated with deep placement, including reduced P fixation, enhanced root access to P, and moisture availability for P uptake. Additionally, deep band placement combined with higher N application rates (N187 and N150) further enhanced plant P uptake by promoting P availability and utilization mechanisms. Deep band placement also resulted in significantly higher total soil P, Olsen-P, and P use efficiency than broadcast and side band methods, indicating a more efficient P fertilization strategy for maize that can improve growth and yield. This present study also found positive correlations between P concentration in plant organs and soil Olsen-P, highlighting the importance of adequate soil P levels for optimal plant growth. Overall, our results have showed that deep band fertilizer placement emerged as a superior strategy for enhancing P uptake efficiency, utilization, and maize productivity compared to broadcast and side band placement. This approach can potentially optimize P fertilizer utilization, improve maize growth and yield, while minimizing environmental P losses associated with broadcast applications.

Collaborative robots have experienced low acceptance in applications, especially in industry. 1 This fact has attracted the attention of researchers and practitioners, who point to different causes 2 for this low acceptance. One of the main reasons is the difficulty in converging on suitable methods 3 for modeling collaborative interactions between robots and their surrounding context during the 4 requirements phase. These interactions must be elicited and modeled during requirements modeling 5 to maximize value creation through collaboration and must be formally verified, taking into account 6 the risks of human-robot interaction. However, such modeling is often not present in collaborative 7 robot design, and the choice of an appropriate approach remains an open problem. In this paper, 8 this problem is addressed from a requirements engineering perspective, a goal-oriented model and 9 a service-based approach supported by an additional verification method based on Petri nets is 10 proposed. A case study based on collaborative robots used in a hospital environment is presented.

Excess nitrogen in agricultural drainage poses a serious threat to the water quality safety of the Yellow River basin. Utilizing aquatic plants to modify the rhizosphere microbial community structure and facilitate nitrogen transformation is a crucial strategy for mitigating regional water eutrophication. Our results revealed that the denitrification rate (DR), dissimilatory nitrate reduction to ammonium rate (DNRAR) and nitrogen fixation rate (NFR) of rhizosphere sediments exhibited a significant increase in artificially cultivated ditch. Compared with those in natural ditches, the richness of the bacterial community and the relative abundances of Bacteroidota, Verrucomicrobiota, Firmicutes, Anaeromyxobacter and Geobacter, which are involved mainly in the nitrogen cycle, in the rhizosphere sediments of artificially cultivated ditch were significantly greater due to improvements in environmental conditions. In artificially cultivated ditch, the dominant functional genes affecting DRNARs in the rhizosphere sediments of P. australis were nrfC and nrfA, while DRs were driven mainly by norB and napA, which were influenced by the nitrogen and carbon levels; and the dominant functional genes affecting NFRs in the rhizosphere sediments of T. orientalis were nifD, nifK and nifH. Our results provide a scientific basis for the use of aquatic plants for mitigating excess nitrogen levels in agricultural drainage.

Rotavirus (RV) replicates within viroplasms, membrane-less electron-dense globular cytosolic inclusions with liquid-liquid phase properties. In these structures occur the virus transcription, replication, and packaging of the virus genome in newly assembled double-layered particles. The viroplasms are composed of virus proteins (NSP2, NSP5, NSP4, VP1, VP2, VP3, and VP6), single and double-stranded virus RNAs, and host components such as microtubules, perilipin-1 and chaperonins. The formation, coalescence, maintenance, and perinuclear localization of viroplasms rely on their association with the cytoskeleton. A stabilized microtubule network involving microtubules and kinesin Eg5 and dynein molecular motors is associated with NSP5, NSP2, and VP2, facilitating dynamic processes such as viroplasm coalescence and perinuclear localization. Key post-translation modifications, particularly phosphorylation events of RV proteins NSP5 and NSP2, play pivotal roles in orchestrating these interactions. Actin filaments also contribute, triggering the formation of the viroplasms through the association of soluble cytosolic VP4 with actin and the molecular motor myosin. This review explores the evolving understanding of RV replication, emphasizing the host requirements essential for viroplasm formation and highlighting their dynamic interplay within the host cell.

Since December 2019, the world is fighting against coronavirus disease (COVID-19). This disease is caused by a novel coronavirus termed as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This work focuses on the applications of machine learning algorithms in the context of COVID-19. Firstly, regression analysis is performed to model the number of confirmed cases and death cases. Our experiments show that autoregressive integrated moving average (ARIMA) can reliably model the increase in the number of confirmed cases and can predict future cases. Secondly, a number of classifiers are used to predict whether a COVID-19 patient needs to be admitted to an intensive care unit (ICU) or semi-ICU. For this, classification algorithms are applied to a dataset having 5644 samples. Using this dataset, the most significant attributes are selected using features selection by ExtraTrees classifier, and Proteina C reativa (mg/dL) is found to be the highest-ranked feature. In our experiments, random forest, logistic regression, support vector machine, XGBoost, stacking and voting classifiers are applied to the top 10 selected attributes of the dataset. Results show that random forest and hard voting classifiers achieve the highest classification accuracy values near 98%, and the highest recall value of 98% in predicting the need for admission into ICU/semi ICU units.

Privacy and information insurance have consistently been a priority for the European Union lawmaker. This paper presents whether the NIS prevails in its underlying endeavors to remain the congruity among residents and companies. It distinguishes current difficulties that specific business structures may experience while conforming to the Regulation. The exploration traces certain reasons which could impede the opportune reception of the new lawful ideas inside the system of the organizations. The exploration incorporates what is unique about the NIS; how it overlaps with existing frameworks; and how security requirements in the GDPR influence the NIS Directive. This paper furnishes different point of views of experts with the aim of setting up an entire examination

In arid and semi-arid regions like Tunisia, irrigation water is typically saline, posing a soil and crop salinization risk and yield reduction. This research aims to study the combined effects of soil matric and osmotic potential stresses on tomato root water uptake. Plants were grown in pot and field experiments in loamy-clay soils and were irrigated with three different irrigation water qualities: 0, 3.5, and 7 dS/m. The Hydrus-1D model was used to simulate the combined dynamics of subsurface soil water and salts. Successful calibration and validation of the model against measured water and salt profiles enabled the examination of the combined effects of osmotic and matric potential stresses on root water uptake. Relative yields, indirectly estimated from actual and potential transpiration, indicated that the multiplicative stress response model effectively simulated measured yields and the impact of saline water irrigation on crop yields. The gradual salinization of the root zone further evidenced this effect. The scenario considering a temperature increase of 2°C had no significant impact on crop yields in the pot and field experiments.

The increasing demand for water in agriculture and the need for more efficient irrigation systems has led to the development of smart irrigation systems based on the Internet of Things. In this system, an Arduino microcontroller is used to control and monitor the watering of crops. The system is equipped with sensors that measure soil moisture which are used to optimize the irrigation process. The data collected by the sensors is transmitted to the Arduino, which then adjusts the watering schedule based on the information received. The use of IoT in smart irrigation systems provides several benefits, including reduced water waste, increased crop yields, and improved plant health. The implementation of this system using an Arduino platform is relatively simple, cost-effective, and can be easily integrated into existing irrigation systems. Overall, the use of IoT in smart irrigation using an Arduino platform has the potential to revolutionize the way we manage water resources in agriculture.

The Automatic Dependent Surveillance-Broadcast (ADS-B) protocol stands as a cornerstone in modern aviation surveillance and Aircraft Traffic Control systems, integral to the NextGen project initiated by the US Department of Federal Aviation Administration (FAA) in 2005. ADS-B utilizes data links to autonomously broadcast Aircraft navigational and identification information to augment air transportation system capacity and safety. However, concerns regarding the protocol's security persist, particularly as its adoption expands. The unencrypted nature of transmitted aircraft data and the absence of robust authentication mechanisms render the protocol susceptible to exploitation by malicious actors. In this study, we comprehensively reviewed existing research in the field, identifying a crucial gap necessitating a holistic survey. Prior surveys have predominantly focused on specific aspects, such as vulnerabilities, attacks, or critiques of existing solutions. Our survey addresses this gap by thoroughly exploring the aviation system, providing readers with a nuanced understanding of ADS-B security. Utilizing a detailed SWOT analysis diagram, our paper delves into the vulnerabilities inherent in the ADS-B protocol outlines potential threats and scrutinizes various attack scenarios. We systematically categorize and analyze existing security solutions, considering cost-effectiveness, scalability, implementation complexity, and coverage against diverse attack vectors. Furthermore, we critically evaluate these solutions, elucidate ADS-B security requirements, discuss current challenges, and propose future research directions. This survey serves as a comprehensive resource for researchers and practitioners alike, shedding light on the multifaceted landscape of ADS-B security and paving the way for enhanced aviation system resilience in the face of evolving cybersecurity threats.

Balsam fir trees are a specialty agricultural species that are largely used as Christmas trees. Soil or tissue sampling is seldom performed to assess balsam fir nutrition needs, which can contribute to over or under fertilization and subsequent environmental challenges. Remote sensing to determine the normalized difference vegetation index (NDVI) has had strong relationships with nutrition in other plants. This research aimed to determine whether there is a relationship between NDVI and nutrition in balsam fir. A random sample of 45 trees was selected in autumn of 2021 and then another 70 trees selected in spring of 2022. Soil and needle tissue samples were analyzed for N, P, K, Ca, Mg, S, Fe, Zn, Mn, B, and Cu. An uncrewed aerial vehicle equipped with a multispectral camera was flown over the orchard to determine the NDVI for each tree. Nutrition and NDVI were analyzed for correlations through all sampled trees. Separate correlation analyses were repeated for mature trees (greater than 5 years old) and young trees (less than 5 years old). Soil nutrients N, P, K had a significant relationship with NDVI in autumn sampling. Only N was correlated consistently in both autumn and spring, with R2 of 37% and 33% respectively. Relationships were much stronger when only mature trees were considered, increasing to 60% and 51% respectively. NDVI was found to offer predictive power for tissue N that can be useful to producers to help optimize fertilizer input. However, further work is needed to refine these relationships.

Critical phases in socio-technical systems (STS) design are the definition of functional requirements for automated or software supported human activities while also addressing social and human interaction issues. To define automation support for human operations, STS designers need to ensure that specifications will satisfy not only non-functional requirements (NFR) of the system but also of its human actors such as human reliability/workload. However, such human factors aspects are not addressed sufficiently with traditional STS design approaches which could lead to systems failure or rejection. This paper proposes a new STS design method that addresses this problem and introduces a novel type of requirements namely Task Support Requirements (TSR) that assists in specifying the functionality that the system should have to support human agents in undertaking their tasks by addressing human limitations. The proposed method synthesizes a requirements/software engineering approach to STS design with functional allocation and an HCI perspective which facilitate application of human factors knowledge in conceptual models and evaluation through VR simulation. A case study methodology is employed in this work that allows in-depth, multi-faceted explorations of the complex issues that characterize STS.

The paper presents modelling of relationships between wind velocity distributions observed in Poland and technical requirements for wind power stations design. The wind velocities distributions for various locations in Poland are determined, and expressed in Weibull distribution parameters. Theoretical computations concerning the dependence of wind power station as function of wind speed and aire physical properties are presented. Conclusions important for design of power stations fitted to atmospheric conditions in Poland are given. Wind power plants have been an important source of electricity generation for many years.Wind power modelling with the use of wind speed is used to improve the accuracy of forecasts and analyses of the power production of wind farms.

Heat requirements play an important role in plant flowering, and control the flowering period of woody ornamental plants to a certain extent. In this study, the perennial potted seedlings of four typical Prunus mume cultivars 'Xiaolve', 'Baixuzhusha', 'Fenghou' and 'Danfenghou' were used as materials. The way of regulating the cultivation temperature is adopted to achieve the purpose of relieving the natural dormancy and forcing the blooming of P. mume. Its content includes the research on the heat requirements of P. mume cultivars widely planted in Beijing, and the analysis of the influence of different facility cultivation temperatures on the flowering characteristics of P. mume, providing a theoretical basis for further exploring the cultivation techniques of P. mume bonsai flowering regulation. The results showed that, according to the Growing degree-hour model, the HR of 'Xiaolve', 'Baixuzhusha', 'Fenghou' and 'Danfenghou' flower buds were 3583.70 GDH℃, 3217.30 GDH ℃, 3996.50 GDH ℃, 4732.20 GDH ℃, respectively. According to the effective accumulated temperature model, the HRs of above cultivars were 350.65 ℃, 319.30 ℃, 510.30 ℃, 558.50 ℃, respectively. In addition, the temperature of different cultivation facilities did not significantly change the flowering quality such as flower diameter and fragrance. Temperature also affects the growth and development speed, affecting the flowering time. Under the facility environment with higher temperatures, the flowering process is faster and the flowering period is shorter.

Requirements are predominantly written in Natural Language (NL), which makes them accessible to stakeholders with varying degrees of experience, as compared to a model-based language which requires special training. However, despite its benefits, NL can introduce ambiguities and inconsistencies in requirements, which may eventually result in system quality degradation and system failure altogether. The system complexity that characterizes current systems warrants an integrated and comprehensive approach to system design and development. This need has brought about a paradigm shift towards Model-Based Systems Engineering (MBSE) approaches to system design and a departure from traditional document-centric methods. While, MBSE shows great promise, the ambiguities and inconsistencies present in NL requirements hinder their conversion to models directly. The field of Natural Language Processing (NLP) has demonstrated great potential in facilitating the conversion of NL requirements into a semi-machine-readable format that enables their standardization and use in a model-based environment. A first step towards standardizing requirements consists of classifying them according to the ``type'' (design, functional, performance, etc.) they represent. To that end, a language model capable of classifying requirements needs to be fine-tuned on labeled aerospace requirements. This paper presents the development of an annotated aerospace requirements corpus and its use to fine-tune BERT$_\text{BASE-UNCASED}$ to obtain aeroBERT-Classifier: a new language model for classifying aerospace requirements into design, functional, or performance requirements. A comparison between aeroBERT-Classifier and bart-large-mnli (zero-shot text classification) showcased the superior performance of aeroBERT-Classifier on classifying aerospace requirements despite being fine-tuned on a small labeled dataset.

Even though groundwater-based irrigation has increased agricultural productivity and economic activity in the United States (US), the current rate of groundwater withdrawals from major aquifers could significantly affect the sustainability of agricultural production systems in the near future. In the major agricultural regions, producers are now facing challenges to irrigate to meet full crop water needs. There is an increasing need to strategize irrigation management under various climatic and environmental conditions to optimize water use in agriculture while optimizing crop yields. This study reviews some of the major challenges facing irrigated agriculture in the US and the potential measures to ensure the sustainability of groundwater-based irrigation. Identified challenges included diminishing quantity and quality of groundwater resources, frequent droughts, low adoption rates of precision irrigation technologies, and rising energy requirements for irrigation abstractions. Irrigation efficiency improvements, shifting to high water use efficiency crops, wastewater reuse, groundwater monitoring, availing incentives, and policy changes, were identified as promising water management strategies to ensure irrigation sustainability. The success of these strategies will depend on the uptake and adoption by the producers in the affected agricultural regions.

In a constantly changing environment brought about by the climate crisis and escalated anthropogenic perturbations driven by the growing population, harmful algal bloom dynamics and their impacts are expected to shift, necessitating adaptive management strategies and comprehensive research efforts. Similar to primary productivity, HABs have been thought to be driven primarily by major nutrients such as N, P, and Si. However, recent investigations on the role and importance of micronutrients as limiting factors in aquatic environments have been highlighted. This paper provides a review of metal and phytoplankton interactions, with a specific emphasis on pertinent information on the influence of trace nutrients on growth, toxin production, and other underlying mechanisms related to the dynamics of HABs. Low to near-depleted levels of essential nutrients including Fe, Cu, Zn, Se, Mn, Co, and Mo, negatively impact cell growth and proliferation of various marine and freshwater HAB species. However, evidence shows that at elevated levels, these trace elements along with other non-essential ones, could still cause toxic effects to certain HAB species manifested by decreased photosynthetic activities, oxidative stress, ultrastructure damage, and cyst formation. Interestingly, while elevated levels of these metals mostly result in increased toxin production, Co (i.e., yessotoxins, gymnodimine, and palytoxins) and Mn (i.e., isodomoic acid, okadaic and diol esters) enrichments revealed otherwise. In addition to toxin production, releasing dissolved organic matter (DOM) including dissolved organic carbon (DOC) and humic substances was observed as an adaptation strategy, since these organic compounds have been proven to chelate metals in the water column, thereby reducing metal-induced toxicity. Whilst current research center on free metal toxicity of specific essential elements such as in Cu and Zn, a comprehensive account of how trace metals contribute to the growth, toxin production, and other metabolic processes under conditions reflective of in-situ scenarios of HAB-prone areas, would yield new perspectives on the roles of trace metals in HABs. With the growing demands of the global population for food security and sustainability, substantial pressure is exerted on the agriculture and aquaculture sector, highlighting the need for effective communication of information regarding the interactions of macro- and micronutrients with HABs to improve existing policies and practices.

Context: In Software Engineering, there must be clarity in communication among interested parts to elicit the requirements aimed at software development through frameworks to achieve the behaviors expected by the software. Problem: Lack of clarity in the requirements elicitation stage can impact subsequent stages of software development. Solution: We proposed a case study to achieve the proposed goal focusing on the performance efficiency characteristic expressed in the ISO/IEC/IEEE 25010 Standard using Behavior-Driven Development (BDD). Method: The case study was performed with professionals who use BDD to elicit the non-functional requirements of a company that develops software. Summary of Results: The results obtained were aspects related to the elicitation of non-functional requirements aimed at the performance efficiency characteristic of the ISO/IEC/IEEE 25010 Standard using BDD through the point of view of industry professionals. Contributions and impact: The article’s main contribution is to demonstrate the effectiveness of using BDD to elicit non-functional requirements about the performance efficiency characteristic of the ISO/IEC/IEEE 25010 Standard.

Keywords: Requirements prioritization, Next release plan, Software product planning, Decision support, MoSCoW, AHP, K-Menas, GMM, BIRCH, PAM, Hierarchical, Clustering, Clusters Evaluation

Visually impaired people encounter many impediments and challenges in their lives such as related to their mobility, education, communication, use of technology, and others. This paper reports the results of an online survey to understand the requirements and challenges blind and visually impaired people face in their daily lives regarding the availability and use of digital devices. The survey was conducted among the blind and visually impaired in Saudi Arabia using digital forms. A total of 164 people responded to the survey most of them using the VoiceOver function. People were asked about the use of smart devices, special devices, operating systems, object recognition apps, indoor and outdoor navigation apps, virtual digital assistive apps, the purpose (navigation, education, etc.) of and difficulty in using these apps, the type of assistance needed, the reliance on others in using the assistive technologies, and the level of satisfaction from the existing assistive technologies. The majority of the participants were 18 – 65 years old with 13% under 18 and 3% above 65. Sixty-five percent of the participants were graduates or postgraduates and the rest only had secondary education. White Cane, mobile phones, Apple iOS, Envision, Seeing AI, VoiceOver, and Google Maps were the most used devices, technologies, and apps used by the participants. Navigation at 39.6% was the most reported purpose of the special devices followed by education (34.1%) and office jobs (12.8%). The information from this survey along with a detailed literature review of academic and commercial technologies for the visually impaired was used to establish the research gap, design requirements, and a comprehensive understanding of the relevant landscape, which in turn was used to design smart glasses called LidSonic for visually impaired.

Water resources worldwide are limited, especially in desert areas like Saudi Arabia. Therefore, the rational and sustainable management of irrigation water supply is an imperative required by the conditions of space and time. This study evaluates the irrigation system through partial root-zone drying (PRD) and regulated deficit irrigation (RDI). The effect of applying the PRD and RDI systems on the efficiency and rationalization of irrigation water consumption in a greenhouse and open field cucumber was studied in addition to using the surface (S) and subsurface (SB) drip irrigation methods on irrigation efficiency. The productivity of the RDI-S regular drip irrigation treatment was considered 100% as the control on which the results of all treatments are measured. The results showed that in terms of irrigation technique, the PRD-SB technique had the highest productivity during the winter season in greenhouse cucumbers, with a general average of 13.8 kg m-2 for all irrigation levels, while the RDI-SB technique had the highest productivity during the summer season, with a general average of 16.1 kg m-2. Regarding irrigation level results, the study showed that an irrigation level of 100% is the highest productivity in all irrigation techniques, with a general average of 14.9 kg m-2 for all irrigation techniques during the winter season and 16.4 kg m-2 during the summer. However, the increase in the irrigation level to 150% did not increase productivity. In comparison, the application of the deficit irrigation technique by reducing the level of irrigation to 50% was not accompanied by a similar decrease in productivity. The study results also showed that irrigation with the PRD-SB system was the most productive in the open field at all irrigation levels, with a general average of 7.3 kg m-2. The general average of the percentage decrease in productivity in all irrigation techniques compared to reducing the irrigation percentage to 50% and 75% was 22% and 3 %. The irrigation efficiencies were higher when applying the PRD (PRD-SB) in winter. In contrast, applying a deficit irrigation system (RDI-SB) in the summer season had the highest efficiency. It could be concluded that using PRD and RDI systems for indoor and outdoor cucumber can save irrigation water.

The use of zeolite in agriculture as a soil conditioner is becoming an important field of research in crop growth. To study the effect of synthetic zeolite and deficit irrigation on sweet pepper (Capsicum annuum L.) cultivation, an experiment in a controlled environment was conducted. In particular, sweet pepper was cultivated in a glasshouse using polypropylene pots filled with sandy-loam soil to which 2% of zeolite was added. The zeolite employed in the experiments was obtained using coal fly ash as raw material. The experiment was planned in order to have two main treatments consisting of a) soil with zeolite at 2% (Z) and b) soil without zeolite, as control (C), and three subplot treatments consisting of 1) full irrigation at 100% of available water content (AWC) (100); 2) deficit irrigation at 70% of AWC (70); 3) deficit irrigation at 50% of AWC (50). Sweet pepper cultivation started on the 24th April 2023 and lasted until the 23th June 2023 and during the trial environmental data, such as soil humidity, air temperature and relative humidity, and some crop parameters, such as plant height, leaf number, SPAD index, were monitored. At the end of the trial, fresh and dry plant weight, dry matter content, and leaf water potential were measured. Results showed that for plant fresh weight and dry matter content, no significant differences were observed by treatments and their interactions whereas for the other parameters the statistical analysis returned significant differences. The study suggests that the soil structural benefits, resulting from the zeolite application, are not followed by an equal positive effect in terms of sweet pepper growth under deficit irrigation conditions.

Recycled wastewater is considered as a sustainable source of irrigation water. Despite commendable safety records, viral contamination of agricultural products has occurred the past causing disease outbreaks. This review examines the apprehension that the novel coronavirus (SARS-CoV-2) may also spread through recycled wastewater irrigation (RWI) industry. The novel SARS-CoV-2 is now perceived as an enteric pathogen, and has been found to remain stable in the wastewater for days. Mounting evidences also suggest that viral particles shed by infected individuals through sewage, and greywater is much higher (up to 10 Log₁₀) than the amount typically removed (6-7 Log₁₀) through the recycled water disinfection processes. Such gap indicated an increased risk of infection through fecal-oral transmission route. This study also identified greywater irrigation schemes posing a higher risk of transmission of SARS-CoV-2. It was recommended that countries putting greywater in the ‘low risk’ category may rewrite the safety guidelines in post COVID-19 times. This review also suggest that the choice of irrigation method could be critical in protecting the farmers, and the consumers from possible infections during the pandemic. In this regard, irrigation methods (i.e. sprinkler) that generate airborne droplet (leading to aerosols) may be operated with caution when public spaces are in the vicinity. The study also indicated that the developing countries should regulate surface irrigation practice that pump water from polluted rivers during the pandemic.

The implementation of green roofs as sustainable urban drainage systems provides benefits for stormwater control and the environment and is always more encouraged. In this paper, the estimation of the probability of vegetation survival without irrigation has been proposed as a guide to choose the proper values for the design parameters; in particular the growing medium thickness has been related to the average return interval of the water content at the end of the dry period. Moreover the study represents an improvement of the analytical probabilistic approach since a chain of consecutive rainfall events has been considered, in order to take into account the possibility that the storage capacity is not completely available at the beginning of each event because of the pre-filling from more than one previous rainfall as typically happens for green roofs. Finally, developed equations have been validated by means of their application to two case studies, respectively in northern and southern Italy.

The motivations for the move to electrified vehicles are discussed with reference to their improved energy efficiency, their potential for lower CO2 emissions (if the electricity system is decarbonized), their lower (or zero) NOx/particulate matter (PM) tailpipe emissions, and the lower overall costs for owners. Some of the assumptions made in life-cycle CO2 emissions calculations are discussed and the effect of these assumptions on the CO2 benefits of electric vehicles are made clear. A number of new tribological challenges have emerged, particularly for hybrid vehicles that have both a conventional internal combustion engine and a battery, such as the need to protect against the much greater number of stop-starts that the engine will have during its lifetime. In addition, new lubricants are required for electric vehicle transmissions systems. Although full battery electric vehicles (BEVs) will not require engine oils (as there is no engine) they will require a system to cool the batteries – alternative cooling systems are discussed, and where these are fluid based, the specific fluid requirements are outlined.

This paper presents a requirements specification analysis for driving the design of new systems that will allow 3D media creators to further promote and monetize from their work. The provided requirements analysis is based on the IEEE 830 standard for requirements specification. It allows us to elucidate system requirements through existing (AS-IS) and envisioned (TO-BE) scenarios affected by the latest trends on design methodologies and content promotion in social media. A total of 30 tools for content creation, promotion and monetization are reviewed. The target groups, i.e. creator groups, are divided in 10 types according to their role in 3D media production. Based on this division 10 candidate TO-BE scenarios have been identified and out of these 10 scenarios, we have selected 6 scenarios for validation by media creators. The validation was performed through a survey of 24 statements on a 5 Likert scale by 47 individuals from the domains of Media, Fine arts, Architecture, and Informatics. Useful evaluation results and comments have been collected that can be useful for future systems design.

Higher body weight is known to negatively impact plasma vitamin C status. However, despite this well-documented inverse association, recommendations on daily vitamin C intakes by health authorities worldwide do not include particular reference values for people of higher body weight. This suggests that people of higher body weight and people with obesity may be insufficient in vitamin C in spite of ingesting the amounts recommended by their health authorities. The current preliminary investigation sought to estimate how much additional vitamin C people with higher body weights need to consume in order to attain a comparable vitamin C status to that of a lower weight person consuming an average Western vitamin C intake. Data from two published vitamin C dose-concentration studies were used to generate the relationship: a detailed pharmacokinetic study with seven healthy non-smoking men and a multiple depletion-repletion study with 68 healthy non-smoking men of varying body weights. Our estimates suggest that an additional intake of 10 mg vitamin C/day is required for every 10 kg increase in body weight to attain a comparable plasma concentration to a 60 kg individual with a vitamin C intake of ~110 mg/day, which is the daily intake recommended by the European Food Safety Authority (EFSA). Thus, individuals weighing e.g. 80 and 90 kg will need to consume ~130 and 140 mg vitamin C/day, respectively. People with obesity will likely need even higher vitamin C intakes. As poor vitamin C status is associated with increased risk of several chronic diseases including cardiovascular disease, these findings may have important public health implications. As such, dose-finding studies are required to determine optimal vitamin C intakes for overweight and obese people.

Rice production is important for global food security but given its large water footprint, efficient irrigation management strategies need to be developed. Expansion of rice growing area is larger than any other crop in Africa due to increasing demand for rice. Three rice irrigation management alternatives with the system of rice intensification (SRI) were field-evaluated against the conventional continuously flooded system (CF) in Tanzania. Production systems included: (1) CF (50 mm ponding depth for the entire season); (2) SRI (40 mm ponding for 3 days and no irrigation for next 5 days); (3) 80% SRI (80% of the SRI ponding); and (4) 50% SRI (50% of the SRI ponding). Experimental evaluation of the four systems was conducted for both wet and dry seasons. For the dry season, the SRI and 80% SRI produced higher yields of 9.68 tons/ha and 11.45 tons/ha and saved 26% and 35% of water, respectively compared to the CF (8.69 tons/ha). The yield advantage of the 80% SRI and SRI over the CF was less during the wet season with 6.01 tons/ha and 5.99 tons/ha of production, and water savings of 30% and 14%, respectively compared to the CF (5.64 tons/ha). The 50% SRI had lowest yield of all for both seasons, 7.48 tons/ha and 4.99 tons/ha for the dry and wet seasons, respectively. Statistically, the 80% SRI treatment outperformed all other treatments over the two seasons with an additional yield of 1.57 tons/ha and 33% (345 mm) water savings compared to the CF. Economic productivity of water (US$/ha-cm) over two seasons was highest for the 80% SRI ($20.27/ha-cm), while it was lowest for the CF ($12.89/ha-cm). Water saved by converting from the CF to the 80% SRI (1.98 million ha-cm) can support a 50% expansion in the current rice irrigated area in Tanzania. Even without irrigation expansion, the 80% SRI can increase rice production by 1.5 million tons annually while enhancing water availability for industrial and environmental uses (e.g., ecological preserves) and help achieve food security in Tanzania and the greater sub-Saharan Africa.

Many requirements engineering tools have been existing for gathering, documenting and tracing requirements that can even be further processed for such purposes as analysis and transformation. In this study, we analysed 56 different requirements engineering tools for a comprehensive set of features that are categorised into multiple viewpoints (i.e., project management, specification, collaboration, customisation, interoperability, methodology, knowledge management). The analysis results led to many interesting findings. Some of them are as follows: (i) the project planning and execution activities are rarely supported, (ii) multi-user access and versioning are highly supported, (iii) the top-popular specification technique is natural languages, while precise specification via modeling languages is rarely supported, (iv) requirements analysis is rarely supported, (v) requirements transformation is considered for generating documents only, (vi) tool customisation via the tool integration and API support is highly popular, while customising the notation set is rarely supported, (vii) exchanging requirements is popular in such standards as ReqIF and Excel/CSV, while no single standard are accepted by all the tools, (viii agile development is very common, while other methodologies (e.g., MDE and SPLE) are rarely supported, and (viii) user-guide, telephone, e-mail, videos are the top methods for sharing knowledge. The analysis results will be useful for different stakeholders including practitioners, tool vendors and researchers.

Disasters such as the 2015-2018 drought in South Africa usually negatively impact agricultural water, especially in smallholder farming systems. This study assessed the availability of irrigation water, performance of irrigation infrastructure, and water governance systems in Genadendal, Western Cape, with a focus on smallholder farmers. Data for streamflow, dam levels, and rainfall were acquired from water institutions and analysed using Microsoft Excel. The performance of the infrastructure and water governance were assessed based on the perceptions of smallholder farmers and key informants. A questionnaire was administered to eight smallholder farmers, followed by a focus group discussion with 15 smallholder farmers. Interviews were conducted with eight key informants who worked in water-related institutions. Qualitative data from the interviews were analysed using thematic content analysis. The study showed adequate agricultural water resources during the 2015-2018 drought period for the smallholder farmers who relied on water from dams. However, smallholder farmers who relied on the Riviersonderend River experienced severe water shortages during the same period. The findings also showed that there was poor performance of irrigation water infrastructure due to inadequate maintenance. Lack of coordination among institutions, insufficient funding, political interference, fragmentation of roles and responsibilities, lack of human resources, and farmers’ lack of participation and commitment in water resources management contributed to the dysfunctionality of irrigation water governance systems, leading to water insecurity. This study recommends adequate funding for the operation and maintenance of infrastructure. Furthermore, water institutions need to support smallholder farmers with training skills in agricultural water management and infrastructure maintenance. Further studies are recommended to quantify the water lost due to the poor performance of water infrastructure and to develop effective water governance in Genadendal.

Inappropriate use of fertilizers is one of the major production constraints in sesame. Studies to optimize the use of N fertilizer on sesame were conducted at Humera Agricultural Research Center (HuARC) under rain fed and irrigation conditions. Thirteen (13) N doses were evaluated in a Randomized Complete Block Design (RCBD) during 2016, 2017 and 2018. In the rain fed condition; results showed higher number of days to 50% flowering on the higher N application. The 31-121.5 kg N ha-1 have scored higher seed yield, where the lower (18 kg N ha-1) and higher (156 kg N ha-1) N doses scored lower seed yield. In the irrigation condition; higher N application rate prolonged the flowering time to some extent and days to 90% maturity was reduced as N rate increased. Heavier seed weights and maximum seed yield were recorded on 64 and 75 kg N ha-1 and the lower N doses scored low seed yield. Agronomic efficiency of N fertilizer (N-AE) was higher on 64 kg N ha-1 under both growing conditions. The partial budget analysis revealed that 64 kg N ha-1 (rain fed) and 75 kg N ha-1 (irrigation) had the higher net profit, MRR and residual ranking. Therefore, for the greater sesame productivity 64 kg N ha-1 has to be recommended for rain fed and 64 to 75 kg N ha-1 for the irrigated sesame.

Special machinery engineering is of great importance to the manufacturing industry and makes a comparatively large contribution to many economies. Digital transformation is already well advanced in many producing industries, and modern ICT technologies such as agents, service orientation, digital twins and artificial intelligence are being used with increasing success. In addition to improving specific product characteristics such as reliability or flexibility, the adoption of modern ICT technologies to ensure sustainability is being intensively discussed. So far, however, there has been little uptake of these technologies in the special machinery industry; sustainability receives little attention. This article examines in detail the reasons for and impediments to the adoption of modern ICT technologies based on a study among special machinery manufacturers. Observations of existing challenges were gathered during daily work, described in detail, and used to derive conclusions about causal barriers. From this, detailed requirements are derived to promote the adoption of modern ICT technologies in the special machinery engineering sector and, ultimately, to bring sustainability more into focus.

The perceived poor performance of publicly traded companies on their sustainability commitments and the quality of sustainability reporting has prompted stakeholders to consider the economic, environmental, and social impacts of corporate activities. Economic activities have led to various threats in the form of climate change, pollution, greenhouse gas emissions, natural disasters, and other issues that have negatively impacted the environment and stakeholders. Companies are expected to report to stakeholders on their sustainability performance, but reality proves that present reporting falls below stakeholders’ expectations mainly due to its still voluntary nature. The present study aims to provide a literature review of the relationship between sustainability reporting and the role of companies governance, especially observing if climate change requirements and energy-needed changes are being accounted. Results highlight mixed evidence for the influence of board governance attributes, providing interesting insights for research advancement. The study has practical implications for businesses, regulators, governments, and other stakeholders in their policy deliberations and investment decisions. Further empirical studies are recommended to re-examine sustainability reporting using the variables identified as important factors and gaps in this study and other board characteristics to improve the generalizability of the results.

Background: medical devices are designed, tested and placed on the market in a highly regulated environment. Wearable sensors are crucial components of various medical devices: design and validation of wearable sensors, if managed according to international standards, can foster innovation while respecting regulatory requirements. Material and methods: the purpose of this paper is to take into consideration the upcoming EU Medical Device Regulation 2017/245 and the current and future IEC and ISO standards that set methods for design and validation of medical devices, with a focus on wearable sensors. Risk classification according to the regulation is described. The international standards IEC 62304, IEC 60601, ISO 14971 and ISO 13485 are reviewed to define regulatory restrictions during design, pre-clinical validation and clinical validation of devices that include wearable sensors as crucial components. Results: current and future regulatory restrictions are described, and an integrated method for design planning, validation and clinical testing is described Discussion: application of this method to design wearable sensors should be evaluated in the future in order to assess its potentially positive impact to fostering innovation and to the time-to-market of the device.

The use of visual signals in horticulture has attracted significant attention and encompassed a wide range of data types such as 2D images, videos, hyperspectral images, and 3D point clouds. These visual signals have proven to be valuable in developing cutting-edge computer vision systems for various applications in horticulture, enabling plant growth monitoring, pest and disease detection, quality and yield estimation, and automated harvesting. However, unlike other sectors, developing deep learning computer vision systems for horticulture encounters unique challenges due to the limited availability of high-quality training and evaluation datasets necessary for deep learning models. This paper investigates the current status of vision systems and available data in order to identify the high-quality data requirements specific to horticultural applications. We analyse the impact of the quality of visual signals on the information content and features that can be extracted from these signals. To address the identified data quality requirements, we explore the usage of a deep learning-based super-resolution model for generative quality enhancement of visual signals. Furthermore, we discuss how these can be applied to meet the growing requirements around data quality for learning-based vision systems. We also present a detailed analysis of the competitive quality generated by the proposed solution compared to cost-intensive hardware-based alternatives. This work aims to guide the development of efficient computer vision models in horticulture by overcoming existing data challenges and paving a pathway forward for contemporary data acquisition.

Technology advancements are evident in the healthcare field; numerous new opportunities and applications have emerged during the last years based on embedded intelligence and related to real-time diagnosis of medical issues, tele-care and telemedicine, remote monitoring of patients, computer-assisted “smart” transportation in case of emergencies, as well as new types of remotely controlled surgical operations. This paper aims to provide an overview of the application of embedded intelligence in the field of healthcare; it gathers the main critical requirements, related technologies and research advancements up to date, and it presents the most important challenges that are yet to be faced. The main focus is given in the development and optimization of Body Area Networks (BANs) based on advanced embedded sensing devices, the optimization of smart gateways in such networks, and the provision of holistic scalable and secure solutions in the healthcare domain. In addition, the paper presents two principal use cases that stem from the combination of novel information and communication technologies with classic healthcare practices, explaining the functional and non-functional requirements, as well as the mixed criticality characteristics of the associated systems.

The study was conducted to evaluate the agronomic performance, forage yield, and nutritive values of Desho grass varieties under supplementary irrigation at Dambi Dollo University experimental site, Western Ethiopia. Three Desho grass varieties (Areka /DZF#590, Kindu kosha-1/DZF #591, and Kulumsa /DZF #592) were arranged in randomized complete block design (RCBD) with three replications. The CROPWAT 8.0 model, local climate data, forage data, and soil data were used to determine Desho grass water requirements and irrigation schedules. The agronomic parameters, yield, chemical composition, and in vitro digestibility of the feed samples at standardization cut were determined following the standard procedures. The results showed significant differences (p

In the context of crowds innovation and the generative design driven by big language models, the personalized requirements has become the best engine for innovative design. The exploration of personalized requirements has become a key in significantly improving product innovation, concepts feasibility, and design interaction efficiency. To mine a large number of vague and unexpressed implicit requirements of personalized products, a domain knowledge graph-based method is proposed in this research. First, based on the classical theory of design science, the characteristics and categories of personalized implicit requirements are analyzed. Next, in order to improve the practicability and construction efficiency of the domain knowledge graph, a more informative ontology is constructed, and better performing NLP models are proposed. Then, a series of mining methods based on knowledge graph are proposed for the personalized implicit requirements of different categories. Finally, A platform was developed based on the technical solution proposed in this study, and an example verification was conducted in the field of electromechanical engineering. The efficiency improvement of the training model proposed in the research was analyzed, and the practicality of implicit requirement mining methods were discussed.

Recurrent neural networks (RNNs) transmit information over time through recurrent connections. In contrast, biological neural networks use many other temporal processing mechanisms. One of these mechanisms is the inter-neuron delays caused by varying axon properties. Recently, this feature was implemented in echo state networks (ESNs), a type of RNN, by assigning spatial locations to neurons and introducing distance-dependent inter-neuron delays. These delays were shown to significantly improve ESN task performance. However, thus far it is still unclear why distance-based delay networks (DDNs) perform bettern than ESNs. In this paper, we show that by optimizing inter-node delays, the memory capacity of the network matches the memory requirements of the task. As such, networks concentrate their memory capabilities to the points in the past which contain the most information for the task at hand. Moreover, we show that DDNs have more total linear memory capacity, with the same amount of non-linear processing power.

The increased complexity of modern systems is calling for an integrated and comprehensive approach to system design and development and in particular, a shift towards Model-Based Systems Engineering (MBSE) approaches for system design. The requirements that serve as the foundation for these intricate systems are still primarily expressed in Natural Language (NL), which can contain ambiguities and inconsistencies that hinder their direct translation into models. The colossal developments in the field of Natural Language Processing (NLP) in general and Large Language Models (LLMs) in particular can serve as an enabler for the conversion of NL requirements into semi-machine-readable requirements. This is expected to facilitate their standardization and use in a model-based environment. This paper discusses a two-fold strategy for converting NL requirements into semi-machine-readable requirements using language models. The first approach involves creating a requirements table by extracting information from free-form NL requirements. The second approach is an agile methodology that facilitates the identification of boilerplate templates for different types of requirements based on observed linguistic patterns. For this study, three different LLMs were utilized. Two of these models were fine-tuned versions of Bidirectional Encoder Representations from Transformers (BERT), specifically aeroBERT-NER and aeroBERT-Classifier, which were trained on annotated aerospace corpora. Another LLM, called flair/chunk-english, was utilized to identify sentence chunks present in NL requirements. All three language models were utilized together to achieve the standardization of requirements. To demonstrate the effectiveness of the methodologies, requirements from Parts 23 and 25 of Title 14 Code of Federal Regulations (CFRs) were employed, and a total of two, five, and three boilerplate templates were identified for design, functional, and performance requirements, respectively.

The activity of prioritizing software requirements should be done as efficiently as possible. Selecting the most stable requirements for the most important customers for the development company can be a positive factor when we consider that the available resource does not always encompass the implementation of all requirements. Quantitative methods for reaching software prioritization in releases are many in the field of Search-Based Software Engineering (SBSE). However, we show that it is possible to use qualitative Verbal Decision Analysis (VDA) methods to solve this same type of problem. Moreover, we will use the ZAPROS III-i methods to prioritize requirements considering the opinion of the decision-maker, who will participate in this process. Finally, the results obtained in the VDA structured methods were quite satisfactory when compared to the methods using SBSE. A comparison of results between quantitative and qualitative methods will be made and discussed later.

Antibiotic beads were used to treat surgical infections. In this study, polylactide–polyglycolide (PLGA) was mixed with vancomycin, osteogenic enhancer lithium chloride (LiCl), and hot com-pressing to form PLGA-vancomycin-LiCl delivery beads to treat bone infection. An elution method was employed to characterize the in vitro release characteristics of the vancomycin and the Li over a 42-day period. The released profiles lasted for more than 42 days for vancomycin and 28 days for Li. The concentration of vancomycin in each sample was well above the breakpoint sensitivity. Lithium co-treatment enhanced the bactericidal effect of vancomycin. Released Li and vancomycin increased mRNA or protein expressions of osteogenic markers of mesenchymal stem cells (MSCs). In vivo, the PLGA delivery systems were implanted into the rabbit distal femoral cavities and the cavity fluid content was aspirated and analyzed at each time point. The released Li and vancomycin lasted more than 6 weeks, and vancomycin concentrations were much above the breakpoint sensitivity. Four rabbits in each group were killed at 8 weeks for histologic observation. More mature bone tissue was shown in the Li treatment group. This study provides a PLGA drug delivery system to meet the requirements of patients with bone infections.

Behavior analysis is a widely used non-invasive tool in the practical production routine, as the animal acts as a biosensor capable of reflecting its degree of adaptation and discomfort to some environmental challenge. Predominantly, conventional statistics use occurrence data for behavioral evaluation and well-being estimation, disregarding the temporal sequence of events. The Generalized Sequential Pattern (GSP) algorithm is a data mining method that identifies recurrent sequences that exceed a user-specified support threshold, whose potential has not yet been investigated for broiler chickens in enriched environments. Enrichment aims to increase environmental complexity with promising effects on animal welfare, stimulating priority behaviors and potentially reducing the deleterious effects of heat stress. The objective was to validate the application of the GSP algorithm to identify temporal correlations between heat stress and the behavior of broiler chickens in enriched environments through proof of concept. Video image collection was carried out automatically for 48 continuous hours, analyzing a continuous period of seven hours, from 12:00 PM to 6:00 PM, during two consecutive days of tests for chickens housed in enriched and non-enriched environments under comfort and stress temperatures. Chickens at comfort temperature showed high motivation to perform the behaviors of Preening (P), Foraging (F), Lying down (Ld), Eating (E), and Walking (W) whose sequences <{Ld,P}>; <{Ld,F}>; <{P,F,P}>; <{Ld,P,F}> and <{E,W,F}> were the only ones observed in both treatments. All other sequential patterns (comfort and stress) were distinct, suggesting that environmental enrichment alters the behavioral pattern of broiler chickens. Heat stress drastically reduced the sequential patterns found at the 20% threshold level in the tested environments. The behavior of lying laterally "Ll" is a strong indicator of heat stress in broilers and was only frequent in the non-enriched environment, which may suggest that environmental enrichment provides the animal with better opportunities to adapt to stress-inducing challenges, such as heat.

In recent years, using pre-sprouted seedlings for sugarcane establishment has been developed in Brazil. Despite yielding promising results, the optimal irrigation management for this method during the Brazilian winter planting season has yet to be determined. Additionally, information regarding water demand and seedling behavior when subjected to different irrigation levels remains unclear. This study aimed to assess the response of pre-sprouted sugarcane seedlings to different irrigation levels during the winter in southeastern Brazil. Pre-sprouted sugarcane seedlings were subjected to different irrigation levels during the winter season in the region of Piracicaba, using a sprinkler irrigation system. Periodic evaluations were conducted, such as seedlings mortality rate, root size, root dry mass, number of sugarcane tillers sprouted per meter, total number of tillers, plant height, leaf area index, shoot dry mass, and total leaf chlorophyll index. The findings of this investigation indicate that a 10 mm irrigation level facilitated optimal fixation depth and achieved a 97.8% survival rate. Moreover, a correlation between seedling mortality rate and irrigation management was observed, emphasizing the critical role of irrigation in establishing and developing seedlings. Based on these findings, a 10 mm irrigation may represent the optimal irrigation strategy for pre-sprouted sugarcane seedlings in this scenario.

The sustainability of irrigated agriculture depends on the quality of irrigation water used. The electrolyte concentration (EC) of irrigation water may lead to the accumulation of salts in the root zone layers and affect the physiological functions of the crop by osmotic and ion toxicity effects. Further, the cationic and anionic composition of the water may alter the exchangeable cation composition of the soil and as well as its pH. Because of the dominance of sodium salts in many sources of irrigation water, parameters related to sodium such as exchangeable sodium percentage (ESP) of soils and sodium adsorption ratio (SAR) of soil solutions have been commonly used to study the effects of sodium in irrigation water on soil structural stability. Quirk and Schofield concept of ‘threshold electrolyte concentration’ (TEC) has shown the importance of electrolytes in preventing the effects of sodium on soil structure. Based on this concept, several models have been proposed to relate ESP or SAR with EC to predict the possible impacts of irrigation water on soil structural stability. However, many research reports indicate that this relationship varies with soils and a given model is not suitable for all types of soils. Further, the effects of potassium and magnesium in the processes leading to clay dispersion are disregarded in these models. This essay analyses all the factors involved in the structural failure of soils with different cationic composition, identify the defects in these TEC models and re-defines TEC on the basis of new insights on dispersive and flocculating charges of soils. This review does not deal with EC effects on crops and also the role of contaminant ions not involved with soil structural stability.

In the software development process, the decision-maker (DM) has a range of problems inherent to its function. Wrong choices during software planning can bring great risk to the project. Therefore, the planning of software releases to be delivered to the customer should be well done. This is not an easy task because releases are made up of many requirements that contain complex variables that must be considered, such as precedence, cost, requirement stability, among other features that make the requirements-selection process challenging. To make this process less exhaustive, DM can use tools that facilitate this work. In software engineering, we can find fields of research specialists in this context, such as Search-Based Software Engineering (SBSE). The SBSE makes use of advanced metaheuristics to search for optimal solutions or the closest to it. In this work, we try to use another field of research to solve this same problem type, the Verbal Decision Analysis (VDA). To do this, we elaborate a workflow that will use the same source data, execute two solutions using the two search fields (SBSE and VDA) and compare the results. In the end, we evaluated and commented on the results.

The limited availability of water resources worldwide presents obstacles, to the progress of ag-riculture. This situation calls for the implementation of water saving technologies to address these challenges effectively. The quest for technologies that conserve water in agriculture, par-ticularly within the context of cotton production, is related to the study of the impact of drought on the growth, leaf surface area, accumulation of dry matter, boll number, and productivity. The benefits of local drip irrigation have been established. Various models and strategies for opti-mizing and improving the efficiency of how we use water for irrigation purposes. In a market driven economy the effective utilization of water assetsis closely linked to the improvement of managing mineral nutrients. To enhance both the quantity and quality of production, nitrogen is the key factor element that plays a role in the process. The formation of highly efficient cotton crops by adapting the growing technology to the specific soil-climatic conditions of the area, in-cluding controlling nitrogen fertilizing and irrigation, is the primary method to achieve con-sistent harvests. The results of irrigation with water deficit and feeding plants with different fer-tilizer rates are presented. This matter is presently significant, in achieving dependable crop harvests supporting the economic growth of farms and safeguarding the well being of agricul-tural environments.

The effects of climate change on yield and quality for different climate regions had high uncertainty. Risk assessment is an effective measure to assess the seriousness of the projected impacts for decision-makers. The modified quality model was used to simulate integrated impacts of climate change, environment and management on wheat yield and quality. Then, the Canadian Earth System Model (CanESM2) was used to forecast the daily meteorological data, and Statistical Down Scaling Model was used for downscaling. CERES-Wheat was combined with the forecasted meteorological data to simulate the future wheat yield and grain protein concentration (GPC). The risk of wheat yield and quality in three climatic regions of Shaanxi combined with two climate change scenarios of CanESM2 were assessed. Temperature increased 0.22-3.34 °C and precipitation increased 10-60 mm for RCP4.5 and RCP8.5. Elevated temperature and precipitation had positive effects on yield in all regions. The yield risk of most regions with climate change decreased 3.8%-25.1%. The GPC risk of all regions with climate change decreased 7.3%-27.2%. Irrigation decreased yield risk greatly in all regions, while had totally different effects for the three climatic regions. Yield risk with irrigation decreased 37.7%-52.1% in different climate. In contrast to previous studies, GPC risk with irrigation increased greatly 25.8%-28.9% in humid region, 3.9%-8.8% in sub-humid region, and decreased 37.7%-52.1% in semi-arid region. Climate change decreased yield risk and GPC risk together. While irrigation decreased yield risk greatly in all regions, had totally different effects for the three climatic regions.

Temperature is the abiotic factors that strongly influence the biology and behavior of insects. Thus, we assessed the development of the egg parasitoids, Ooencyrtus submetallicus and Telenomus podisi, parasitizing eggs of the brown stinkbug, Euschistus heros, at different temperatures, in addition to estimating the average number of generations using temperature records for seven representative soybean producing regions in Brazil. The comparative biology study conducted evidenced that O. submetallicus and T. podisi had similar percentages of parasitism and emergence, life cycle duration (egg-adult), and longevity. The sex ratio and the number of adult parasitoid emerged per parasitized egg was superior for O. submetallicus. The study of thermal requirements evaluated temperatures of 16, 19, 22, 25, 28, 31, and 33 °C. Ooencyrtus submetallicus and T. podisi developed at temperatures between 16 and 31°C. The lower temperature (Tb) threshold for O. submetallicus and T. podisi was 9.3° and 6.7°C, while the thermal constant (K) was 336.9 and 272.7 degree days, respectively. The estimated average number of generations per year for both parasitoids was greater than the pest E. heros. Ooencyrtus submetallicus and T. podisi exhibited the same biological capacity to parasitize and to develop parasitizing E. heros eggs under laboratory studied conditions. The temperature range between 16° and 33 °C was favourable for the development of both parasitoids parasitizing E. heros eggs.

A key concern in an ageing society is citizens’ mobility. As populations age, disability impairments can affect active ageing, health-related wellbeing and quality of life. In this paper, we present the on-going research project SeriousGiggle—Game-based learning for triggering active ageing. Its goal is to assess the potential of game-based learning for active ageing and contribute to a sense of wellbeing and quality of life. It also seeks to improve the mobility of older adults by creating a set of journey plans with route guidance that are rated in terms of safety, community support, environment and age-friendliness. Drawn on our field work with 33 co-designers, 40 end users and 10 semi-structured interviews with Subject Matter Experts, we identify a set of necessary design requirements to an Age-friendly Playable City. This study recommends the use of gamification and playful techniques to engage the end-users to provide information about local traffic signs, pavement conditions, wayfinding and, therefore, help to create route guidance and walking assistance that are personalized to older adults’ context in terms of location, travel fitness, mobility impairments and motivations.

Green peppers (Capsicum annuum L.) is a fruit vegetable with great culinary versatility, and present important nutritional properties for human health. Water deficit negatively affects nutritional quality of green peppers fruits. This study aimed to investigate the influence of zinc oxide nanoparticles (ZnONPs), associated with plant growth-promoting bacteria (PGPB), on the post-harvest quality nutritional of green peppers subjected to water deficit. In an open-field ex-periment, two irrigation levels (50 and 100% of crop evapotranspiration - Etc), four treatments composed by combination of ZnONPs, zinc sulfate (ZnSO4) and PGPB (T1 = ZnSO4, T2 = foliar ZnONPs, T3 = foliar ZnONPs + PGPB, T4 = ZnSO4 via soil + PGPB via soil) and a control treatment (Check) were tested. Water deficit or water deficit mitigation treatments did not interfere with the physical-chemical parameters (except vitamin C content) and physical color parameters (except L*) of green pepper. The water deficit reduced the levels of Ca, Mg, P, K, Mn, Fe, B, and Zn in the fruits. The foliar application of ZnONPs in association with PGPB provided better nutritional quality of green pepper in terms of vitamin C and mineral nutrients. Under water deficit, ZnONPs or ZnSO4 via foliar, associated or not with PGPB, increased the levels of Ca, P, Mg, and Fe in the fruits. Applying Zn via foliar as ZnONPs or ZnSO4 mitigated the negative effects of water deficit on the quality of pepper fruits that was enhanced by the Bacillus subtilis and B. amyloliquefaciens inoculation.

With growing global concern of food and water insecurity, an efficient method to monitor irrigation projects is essential, especially in the developing world, where irrigation performance is often suboptimal. In Nepal, the irrigated area has not been objectively recorded, although their assessment has substantial implications on national policy, project’s annual budgets, and donor fund-ing. Here we present the application of Landsat images to measure irrigated areas in Nepal for the past 17 years to contribute to the assessment of the irrigation performance. Landsat 5 TM (2006-2011) and Landsat 8 OLI (2013-2022) images were used to develop a machine-learning model which classifies irrigated and non-irrigated areas in study areas. The random forest classification achieved overall accuracy of 82.2% and kappa statistics of 0.72. For the class of irrigation areas, the producer’s accuracy and the consumer’s accuracy were 79% and 96%, respectively. Our regionally trained machine-learning model outperforms the existing global cropland map, highlighting the need of such models for local irrigation project evaluations. We assess irrigation project performance and its drivers by combining long-term changes in satellite-derived irrigated area with local data related to irrigation performance, such as annual budget, irrigation service fee, crop yield, precipitation, and main canal discharge.

This study explores the effect of seasonality on soil carbon efflux and pasture growth based on field and lysimeter experiments during summer-fall and winter-spring in two years. Focus is also pointed on irrigation strategies to alleviate the crop response to seasonal fluctuations in precipitation and surface temperatures. Soil respiration, soil and air temperature, leaf photosynthesis, plant dry weight and leaf area index were quantified and analyzed. It has been found significant differences in the CO₂ efflux between the two growing season. Emission of soil CO₂ allowed to characterize and to prioritize the temperature and rain influence in seasonal brachiaria response. During the seasons, the transient variation of CO₂ efflux was highly correlated with rainfall (r = 0.87, P < 0.05), and poorly correlated with soil temperatures (r = 0.5, P < 0.05). The CO₂ efflux and plant response to different level of reposition of evapotranspiration demonstrated that irrigation during fall mitigates the reduction of growth conditioned by drying soil and the lower temperatures. The lower temperatures are limiting only when the soil moisture is below 32% of the field capacity. Thus, we propose to keep the soil moisture around 50% during the fall as a key practices for mitigating the effect of seasonality and its intensification with the climate change, even more if added to management routine practices the soil and water conservation.

Oilfield produced water (OPW) is used to boost freshwater sources for crop irrigation in California's agriculturally important Central Valley. OPW is known to contain salts, metals, hydrocarbons, alkylphenols, naturally radioactive materials, biocides, and other compounds from drilling and production processes. Less is known about the potential uptake and accumulation of these compounds in crops and soil irrigated with OPW. In this study 23 potted mandarin orange plants were irrigated 2-3 times weekly (depending on season) with water containing three different concentrations of the known OPW heavy metals barium, chromium, lead, and silver. Seven sets of samples of soil and leaves and all fruits were collected and processed using microwave-assisted digestion (EPA Method 3051A). Processed samples were analyzed using ICP-OES. ANOVA, ANCOVA, and Tukey’s honest significant difference test were used to examine the effects of metal concentrations in the irrigation water, sample number, and number of watering days on the metal concentrations in the soil, leaf, and fruit samples. Accumulation of barium in soil and leaves was strongly positively associated with sample and number of watering days, increasing nearly 2,000-fold. Lead also showed an upward trend, increasing up to 560-fold over baseline level. Chromium showed an increase in the soil that tapered off, but less consistent results in the leaves and fruit. The silver results were more volatile, but also indicated at least some level of accumulation in the tested media. The smallest absolute accumulation was observed for chromium. Concentrations in the fruit were highest in the peel, followed by pith and juice. Accumulation of all heavy metals was generally highest in the soil and plants that received the highest irrigation water concentration. Considering the potential for adverse human health effects associated with ingesting soluble barium contained in food and drinking water, and to a lesser extent chromium and lead, the study signals that it is important to conduct further research into whether OPW contaminants can enter the food chain and pose risks to consumers.

Cities have witnessed a booming interest in cycling in recent years. However, the implementation of cycling networks rarely followed a strategy to make them more accessible to specific social groups, such as women. To address this gap in knowledge, this paper provides a holistic perspective of women’s cycling mobility. Specifically, the study’s goal is to look into the social and environmental implications of cycling for women in Turin, Italy. Hence, this paper provides insights for ensuring a woman-friendly cycling system in a city and ensuring women’s participation as vital stakeholders in promoting a bike-friendly identity. We clarify women’s perceptions of cycling and outline the requirements for more inclusive bike infrastructures. Thus, we identify strategies to improve women’s access to bike lanes and investigate the environmental benefits of reduced air pollution by such a bike system. All this information is intended to define a more socially inclusive and environmental-friendly transport system in Turin for everyone, as depicted in Goal 11 of Sustainable Development Goals (SDGs).

Vitamin C is an essential enzyme cofactor and antioxidant with pleiotropic roles in human physiology. Circulating vitamin C concentrations are lower in people with diabetes mellitus suggesting a higher dietary requirement for the vitamin. We interrogated the NHANES 2017/2018 and EPIC-Norfolk datasets to compare vitamin C requirements between those with and without diabetes mellitus using dose-concentration relationships fitted with sigmoidal (four parameter logistic) curves. The NHANES cohort (n=2828 non-supplementing adults) comprised 488 (17%) participants with diabetes (self-reported or HbA1c ≥6.5%). The participants with diabetes had a lower vitamin C status (median [IQR]) than those without (38 [17, 52] µmol/L vs 44 [25, 61] µmol/L, p&lt;0.0001), despite comparable dietary intakes between the two groups (51 [26, 93] mg/d vs 53 [24, 104] mg/d, p=0.5). Dose-concentration relationships indicated that the group without diabetes reached adequate vitamin C concentrations (50 µmol/L) with an intake of 81 (72, 93) mg/d, whilst the those with diabetes required an intake of 166 (126, NA) mg/d. In the EPIC-Norfolk cohort, comprising 20692 non-supplementing adults, 475 (2.3%) had self-reported diabetes at baseline. The EPIC cohort had lower a BMI than the NHANES cohort (26 [24, 28] kg/m2 vs 29 [25, 34] kg/m2, respectively). Correspondingly, the EPIC participants without diabetes required a lower vitamin C intake of 64 (63, 65) mg/d while those with diabetes required 129 (104, NA) mg/d to reach adequate circulating vitamin C status. C-reactive protein concentrations were strongly correlated with body weight and BMI and provided a surrogate biomarker for vitamin C requirements. In conclusion, people with diabetes have 1.4- to 1.6-fold higher requirements for vitamin C than those without diabetes. This corresponds to additional daily vitamin C intake requirements of ~30-40 mg for people with diabetes.

The Industrial Internet of Things (IIoT) paradigm is a key research area derived from the Internet of Things (IoT). The emergence of IIoT has enabled a revolution in manufacturing and production, through the employment of various embedded sensing devices connected with each other by an IoT network, along with a collection of enabling technologies such as artificial intelligence (AI) and edge/fog computing. One of the unrivaled characteristics of IIoT is the inter-connectivity provided to industries; however, this characteristic might open the door for cyber-criminals to launch various attacks. In fact, one of the major challenges hindering the prevalent adoption of the IIoT paradigm is IoT security. Inevitably, an increasing number of research proposals have been introduced over the last decade to overcome these security concerns. To obtain an overview of this research area, conducting a literature survey of the published research is necessary, eliciting the various security requirements and their considerations. This paper provides a literature survey of IIoT security, focused on the period from 2017 to 2023. We identify IIoT security threats and classify them into three categories, based on the IIoT layer they exploit to launch these attacks. Additionally, we characterize the security requirements that these attacks violate. Finally, we highlight how emerging technologies, such as AI and edge/fog computing, can be adopted to address security concerns and enhance IIoT security.

Sport nutrition knowledge has been shown to influence dietary habits of athletes. The purpose of the current study was to examine relationships between sport nutrition knowledge and body composition and examine potential predictors of body weight goal in collegiate athletes. Participants included National Collegiate Athletic Association Division III women (n=42, height: 169.9 ± 6.9 cm; body mass: 67.1 ± 8.6 kg; fat-free mass: 51.3 ± 6.6 kg; body fat %: 24.2 ± 5.3%) and men (n=25, height: 180.8 ± 7.2 cm; body mass: 89.2 ± 20.5 kg; fat-free mass: 75.9 ± 12.2 kg; body fat %: 13.5 ± 8.9%) athletes. Body composition was assessed via air displacement plethysmography. Athletes completed a validated questionnaire designed to assess sport nutrition knowledge and were asked questions about their perceived dietary energy and macronutrient requirements, as well as their body weight goal (i.e. lose, maintain, gain weight). Athletes answered 47.98 ± 11.29 % of questions correctly on the nutrition questionnaire with no differences observed between sexes (men: 49.52 ± 11.76% vs. women: 47.03 ± 11.04%; p=0.40). An inverse relationship between sport nutrition knowledge scores and body fat percentage (r = -0.330; p=0.008), and fat mass (r = -.268; p=0.032) was observed for all athletes. Fat mass (β = 0.224), BF % (β = 0.217), and BMI (β = 0.421) were all significant (p&lt;0.05) predictors of body weight goal in women. All athletes significantly (p&lt;0.001) underestimated daily energy (-1,360 ± 610.2 kcal/d), carbohydrate (-301.6 ± 149.2 g/d), and fat (-41.4 ± 34.5 g/d) requirements. Division III collegiate athletes have a low level of sport nutrition knowledge, which was associated with a higher BF %. Women athletes with a higher body weight, BF % and BMI were more likely to select weight loss as a body weight goal. Athletes also significantly underestimated their energy and carbohydrate requirements based upon the demands of their sport, independent of sex.

The current design of irrigation and fertilization systems often depends on a single index, leading to irrational systems due to the lack of a comprehensive multi-index evaluation. The aim of this study is to optimize the irrigation and nitrogen fertilizer application levels of spring maize using the TOPSIS method, in order to achieve a balance between growth characteristics, yield, water use efficiency, and soil nitrogen content. In the typical semi-arid region of Northeast China, through precise control experiments from 2022 to 2023, we adopted a two factor quadratic saturation D-optimal design method to study the effects of different irrigation amounts (145.40, 271.70, 348.20, 436.20 mm) and nitrogen fertilizer application amounts (34.80, 185.90, 277.40, 382.80 kg·hm-2) on spring maize. Results indicate that increasing both irrigation and nitrogen application rates can enhance dry matter accumulation by 15.17% to 32.70%. The impact of irrigation and fertilization on the net photosynthetic rate (Pn) of spring maize was found to be greater for irrigation amount than nitrogen application, particularly at 9:00 am and 13:00 pm, and slightly less so at 11:00 am and 15:00 pm. Concurrently, there was a significant increase in total nitrogen (TN1 by 20.85% for 0-20 cm soil layer, TN2 by 33.33% for 20-40 cm soil layer) and alkali-hydrolyzed nitrogen (AHN1 by 14.65% for 0-20 cm, AHN2 by 28.86% for 20-40 cm). Yields improved by 12.02% to 44.09%, and water use efficiency (WUE) saw an increase ranging from 20.08% to 140.07%. The optimal water and fertilizer management mode for spring maize SBDI (shallow buried drip irrigation) in semi-arid areas was determined through comprehensive analysis using TOPSIS entropy weight method. When the irrigation amount is 436.20 mm and the nitrogen fertilizer application amount is 277.40 kg·hm-2, it can significantly promote the accumulation of dry matter, yield, water use efficiency, photosynthetic characteristics, and soil nitrogen content of spring maize. This study provides a theoretical basis for the practical application of SBDI water fertilizer coupling technology for spring maize.

Due to the impacts from climate change, the allocation of water resources must urgently be optimized worldwide to ensure that the needs of both water managers and farmers are balanced. In this study, manager-oriented and farmer-oriented assessment models were developed for irrigation water optimization and allocation. The distance from water sources and hydraulic head were the main factors in the manager-oriented assessment model; crop value, water demand of crops, and soil type were additional factors in the farmer-oriented assessment model. The developed assessment models were used to assess irrigation water allocation in five villages in Neimen District. Cadasters at high elevation were discovered to not be suitable for cultivation of crops because of the difficulties in constructing irrigation facilities and the loss of irrigation water during transportation. The result obtained from the manager-oriented assessment system was related to the costs involved in the construction and maintenance of irrigation facilities, which indicated that cadasters located at long distances from water sources and at high elevation are unsuitable for cultivation. By contrast, the result obtained from the farmer-oriented assessment system was related to the profits of farmers and revealed that more cadasters would be suitable for cultivation if suitable crops were chosen.

Interpretation of increased gain in the bovine animals is difficult to be validated due to inherent genetic variation to meet their requirement for energy and protein, and those may relate to the bull species, e.g., Thai swamp buffalo. Therein, the study aimed at investigating and predicting protein requirement systems, with providing abundant energy intake 2.20 Mcal/kg DM for maintenance and growth of Thai swamp buffaloes using the comparative prolonged feeding trial for 90 days. Sixteen bull Thai swamp buffaloes at the initial (Age: 18-24 months; BW: 233 ± 25.0 kg) were as-signed into four treatment groups, four buffaloes each, fed 5.42, 6.96, 8.94, and 10.71% DM crude protein (CP). CP intake, BW, and physiological fluid were determined. The net CP requirements for maintenance and growth of Thai swamp buffaloes were 5.41 g CP/kg W0.75 and 0.46 g CP/g average daily gain (ADG), respectively. Our results indicated that CP requirement increases, when BW increases. An increased dietary CP resulted in an increased number at blood urine nitrogen (N), N absorption, total volatile fatty acid, urinary purine derivative, and the microbial N. Notably, the net CP requirement for growth of Thai swamp buffalo was higher than it reported in NRC, but the maintenance was lower.

To accurately and non-destructively monitor the growth of spring wheat in the Hetao Irrigation Area, UAV remote sensing was employed during various fertility stages. Digital image indices from diverse fertilization treatments were calculated and compared with physiological indices to identify the most sensitive digital image indices corresponding to these indices. The study underscored the critical importance of the flowering stage in the growth of spring wheat, thus highlighting the necessity of focusing on this stage. This finding reiterated that the flowering stage was pivotal for spring wheat development in the Hetao Irrigation Area. Several digital image indices, such as GLA, R, G, INT, g, GRVI, MGRVI, RGBVI, EXG, and GRRI, exhibited a high frequency of significant correlations with physiological indices during the four primary reproductive stages of wheat. Consequently, these sensitive digital image indices during the flowering stage can more effectively characterize the physiological indices of spring wheat.

Greenhouse gas (GHG) emissions from agriculture are significant contributors to global change. We experimentally manipulated biogeochemical control points of irrigation and nitrogen (N) to examine management strategies that could impact GHG flux, i.e., carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) and soil physiochemical changes over a growing season in an arid New Mexico sorghum (Sorghum bicolor (L.) Moench) cropping system. Sorghum is water and N efficient and amenable to environmental stress. Interrogating how crop systems perform in intense heat, aridity and ultraviolet stress of the southwestern US climate can inform future management in areas that produce more food currently, but that will undergo these stresses in the near future. Water was applied at regionally typical rates, or at ~30% below those rates. Timing N to plant needs may reduce N loss and N2O emissions, and we tested this hypothesis by adding equal amounts of fertilizer to all plots, with half receiving all fertilizer at planting versus plots fertilized at 50:50 planting and 30 days post-planting. Gas flux from soil was analyzed via FTIR. More biomass was harvested from the fully irrigated plots; N timing did not significantly affect biomass. Soil pH fluctuated throughout the season in response to both treatments. Carbon dioxide emissions significantly increased in fully irrigated plots through time. Methane uptake was depressed by full irrigation. Nitrous oxide flux was lower in split N plots, but N2O emissions were not impacted by reduced irrigation. These results suggest that arid adapted crops can maintain economically feasible yield, and biogeochemical monitoring within a growing season can help manage for GHG flux.

Among the different applicable irrigants for root canal disinfection, sodium hypochlorite 5.25% is one of the most attractive ones. The quality of root canal disinfection is dependent on some factors such as the employed approach, type of flow rate of irrigant and the size of needle. The majority of studies in the field of root canal disinfection are experimentally carried out. In the current article, Computation Fluid Dynamic (CFD) is used for modeling the antimicrobial liquid flow in the root canal and evaluate the effects of needle size and flow rate. Two needles, G28 and G30, are used for irrigation in three volumetric rates of flow including 0.10 mL⁄s , 0.20 mL⁄s and 0.30 mL⁄s. The results of numerical simulations revealed the improved quality of root canal disinfection by augmentation in the rate of flow and decrease in the inner diameter of the needle. According to the outcomes of the modeling, the highest average wall shear stress obtained in the case of using G28 needle and 30 mL⁄s flow rate, which was approximately 10.21 Pa.

Buildings’ thermal energy needs are inherently linked to climate conditions. Consequently, it is crucial to evaluate how climate changes affect these energy demands. Despite extensive analysis, a comprehensive assessment involving a diverse range of building types has not been consistently conducted. The primary objective of this research is to perform a coherent evaluation of the influence of climate changes, construction element properties, and the Heating, Ventilation, and Air Conditioning (HVAC) system type of control on the energy requirements of six buildings (residential, services, and commercial). The buildings are considered located in a temperate Mediterranean climate. Our focus is on the year 2070, considering three distinct climatic scenarios: (i) maintaining the current climate without further changes, (ii) moderate climate changes, and (iii) extreme climate changes. The buildings are distributed across three different locations, each characterized by unique climatic conditions. Buildings’ envelope features a traditional External Thermal Insulation Composite System (ETICS) and expanded polystyrene (EPS) serves as thermal insulation material. Two critical design factors are explored: EPS thickness ranging from 0 (no insulation) to 12 cm; and horizontal external fixed shading elements varying lengths from 0 (absence) to 150 cm. Six alternative setpoint ranges are assessed for the HVAC system control: three based on the Predicted Mean Vote (PMV) and three based on indoor air temperature (Tair). Results were obtained with a validated in-home software tool. They show that, even under extreme climate conditions, the application of thermal insulation remains energetically favorable, however its relative importance diminishes as climate severity increases. Then, proper insulation design remains important for energy efficiency. The use of external shading elements for glazing (e.g., overhangs, louvers) proves beneficial in specific cases. As climate changes intensify, the significance of shading elements grows. Thus, strategic placement and design are necessary for good results. The HVAC system’s energy consumption depends on the level of thermal comfort requirements, on the climate characteristics and on the building’s type of use. As climate change severity intensifies, energy demands for cooling increase, while energy needs for heating decrease. However, it’s essential to recognize that the impact of climate changes on HVAC system energy consumption significantly depends on the type of building.

Tanzania with 945 million hectares of land area and annual rainfall of 300 mm on 67% of its territorial land is considered as a semi-dry region in the world. Rice production in Tanzania needs to be increased to feed a growing population, whereas water for irrigation is getting scarce. One way to decrease water consumption in paddy fields is to change the irrigation regime for rice production and to replace continuous flooding with alternate wetting and drying. In order to investigate the effect of different regimes of irrigation and nitrogen fertilizer on yield and water productivity of hybrid rice, two greenhouse pot experiments comprising soils from upland and lowland production ecologies were conducted at Sokoine University of Agriculture, Tanzania during crop seasons of 2019. The experiment was arranged in split plots based on randomized completely block design with 3 replications. Water regimes were the main factor comparing continuous flooding (CF) and alternate wetting and drying (AWD) with nitrogen fertilizer levels as the sub-factor including absolute control , 0, 60, 90, 120 and 150 kg/ha. Alternate wetting and drying (AWD) improved water productivity in both upland and lowland production ecologies compared to CF. AWD increased yield under lowland production by 13.3% while in upland there was 18.5% decrease in yield. The average water use varied from 31.5 to 84 L pot-1 under upland trials, while in lowland trials it was 36 to 82.3 L. Higher yield and lower water application led to an increase in WP varying from 1.2 to 1.8 kg cm-3 under upland trials, and 0.6 to 1.5 kg cm-3 under lowland trials. The variation in water productivity among treatments was mainly due to the differences in the yield, water and nitrogen levels used in the production process. Both sets of trials recorded water saving up to 34.3% and 17.3% under lowland and upland trials, respectively. Under upland trials, the yield varied from 39.9 to 124.1 g pot-1 and in lowland trials yield ranged from 20.6 to 118.2 g pot-1 representing paddy rice. The measurements showed that less water can be used to produce more crops under alternative rice growing practices. The results are important for water-scarce areas, providing useful information to policy makers, farmers, agricultural departments, and water management boards in devising future climate-smart adaptation and mitigation strategies.

Veterinary antibiotics (VAs) have been indiscriminately used in animal feed for the past five decades to increase and ensure profits with negligible environmental considerations. The VAs amoxicillin (AMX), chlortetracycline (CTC), and oxytetracycline (OTC), which can be unintentionally introduced by irrigation water during rice cultivation, were evaluated for their phytotoxic effects, absorption-translocation into plants, and soil residues using a randomized complete block design. It was found that exposure to VAs can severely affect the photosynthetic pathway of rice plants. The uptake and translocation of VAs by rice plants varied significantly. CTC and OTC translocated more easily than AMX, a member of the β-lactam class, which accumulated at the lowest concentration compared to CTC and OTC across all treatments. Rice yield was about 4.3 - 5.7% lower in the experimental plots that received fifty-fold the background levels of VAs compared to the control. The findings indicate that these widely used veterinary antibiotics can hamper crop production, leave residues in the soil, and constitute a risk to human health if introduced into the agro-ecosystem unintentionally.

As indicated by standards of modern dentistry, the essence of endodontic treatment is chemo-mechanical disinfection of root canal system. The infected tooth should be eradicated of microorganisms and sealed tight with biocompatible materials. Vital element of this process is irrigation of canals with designated solutions as well as their activation. This review article summarizes various techniques of activation of irrigants for endodontic treatment and compares them. Results were gathered using PubMed and Google Scholar databases by searching keywords: irrigation, activation, sodium hypochlorite, irrigation protocols.

Innovative practices in irrigation systems can bring improvements in terms of economic efficiency and in the same time can reduce environmental impact. Investment in high tech technologies frequently involves additional costs, but an efficient water management can increase the lifetime of the equipment. The main objective of this article is to reduce the energy consumption by one thousand cubic meters pumped and automatically to increase the economic efficiency of the pumping groups. This paper develops a new operating algorithm that ensures the operation of the pumping group at safe operating intervals and in the same time identifies the equivalent pump operating points for the entire flow range and pumping height of the pumping group. This methodology is based on the principles of an Expert System to perform the optimization process of the energy consumption in pumping groups. The resulting methodology avoids the combinatorial explosion of the solutions to be analyzed and determines the point of maximum efficiency without violation of any of the system constraints under any operating condition. The proposed methodology is tested on an irrigation system that includes a pumping group with 5 pumps, showing its effectiveness in obtaining the optimal solution with a relatively low computational burden.

Irrigation is a useful crop enhancement procedure up to the point where free surface water appears. Thereafter, water can begin to flow into waterways, leaching nutrients and giving rise to environmental damage, as well as being a waste of a precious resource. The current work addresses the problem of measuring free water on the surface of agricultural soils by a real-time acoustic remote sensing method. Directional acoustic transmitter and receiver arrays are used to define a ‘footprint’ on the ground from which changes in reflectance are sensed. These arrays are mounted on a moving irrigator. Chirp signals are used to provide along-path resolution and to ensure robustness against unwanted acoustic background noise from farm machinery and the irrigator. Field measurements have been conducted above a well-defined ‘quadrat’ with controlled and measured water content, and also with the instrument mounted on an operational irrigator. A structured light camera mounted above the footprint is used to validate surface water fraction. It is found that the areal fraction of free water on the soil surface can be reliably estimated from changes in the amplitude of the reflected sound waves. The mechanism giving rise to the observed acoustic reflectivity changes is discussed and a model is developed which agrees with normalized intensity observations with a coefficient of determination R2 between 0.65 and 0.83. The rms error between model predictions and observations is comparable to the rms variation of the measurements, indicating that there is insignificant error due to the choice of model.

A remote sensing-based evapotranspiration (ET) study was conducted over the Central Arizona Irrigation and Drainage District (CAIDD), an Arizona agricultural region. ET was assessed means for 137 wheat plots, 183 cotton plots, and 225 alfalfa plots. The remote sensing ET models were the Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC), the Two Source Energy Balance (TSEB), and Vegetation Index ET for the US Southwest (VISW). Remote sensing data were principally Landsat 5, supplemented by Landsat 7, MODIS Terra, MODIS Aqua, and ASTER. The models produced similar daily ET for wheat, with 6–8 mm/d mid-season. For cotton and alfalfa daily ET showed greater differences, where TSEB produced largest daily ET, METRIC the least, and VISW in the midrange. Modeled cotton ET at mid-season ranged from 9.5 mm/d (TSEB), to 8 mm/d (VISW), and 6 mm/d (METRIC). For alfalfa ET, values at peak cover ranged from 8 mm/d (TSEB), 6 mm/d (VISW), and 5 mm/d (METRIC). Model bias ranged −10% to +18%. Relative to potential ET, FAO-56 ET, and USDA-SW gravimetric-ET, model variability ranged from negligible to 35% of annual crop water use. Model averaging was found a useful way to consider and reconcile all ET estimates.

This study was to determine the effects of different irrigation, fertigation and mulching applications on the yield and quality parameters of strawberries. The study was conducted at the Bursa Uludağ University Yenişehir Ibrahim Orhan Vocational School Agricultural Research Field in 2019-2020. In the research, four different irrigation topics, three different fertigation and three different mulching topics were selected The maximum and minimum yield values of the study years were calculated as 5.05-18.70 t ha-1 and 1.20-8.7 t ha-1, respectively, from I100F100M1 and I25F50M0 treatments. As a result, a three-factor study determined that irrigation, fertigation, and mulching had a significant effect on the yield and quality characteristics of strawberries. However, when the reductions in yield and quality losses are evaluated together, despite the reductions in irrigation water and fertigation levels, I75 and F75 topics can be recommended. Also, in mulching treatments, black mulch material (M1) should be chosen over clear mulch material (M1) and no mulch (M0).

Japanese plums form part of a multi-billion rand deciduous fruit industry in South Africa. Despite this, there is a paucity of knowledge on the seasonal water requirements of plum orchards. In a time of changing climatic conditions and diminishing water resources, this gap in literature poses an imminent threat to the long-term sustainability and global competitiveness of the South African plum industry. Therefore, this paper aimed to provide a review of the available literature on the crop water requirements of full-bearing well-irrigated Japanese plum orchards for improved agricultural water management. Full-year water requirements for well-watered full-bearing Japanese orchards ranged between 921 and 1 211 mm a-1 with a mean value of 1 084 ±140 mm a-1. Canopy growth and pruning appeared to be the most common causes of differences in water requirement estimates. Growing season length also plays a role with late-season maturing orchards having higher water requirements than their early and mid-maturing counterparts. The knowledge review provided benchmark figures for the annual water requirements of Japanese plums. However further research is required to determine the water requirement of plums from planting to full-bearing age and the response of plum trees to water stress, in a South African context.

Noise in requirements has been known to be a defect in software requirements specifications (SRS). Detecting defects at an early stage is crucial in the process of software development. Noise can be in the form of irrelevant requirements that are included within a SRS. A previous study had attempted to detect noise in SRS, in which noise was considered as an outlier. However, the resulting method only demonstrated a moderate reliability due to the overshadowing of unique actor words by unique action words in the topic-word distribution. In this study, we propose a framework to identify irrelevant requirements based on the MultiPhiLDA method. The proposed framework distinguishes the topic-word distribution of actor words and action words as two separate topic-word distributions with two multinomial probability functions. Weights are used to maintain a proportional contribution of actor and action words. We also explore the use of two outlier detection methods, namely Percentile-based Outlier Detection (PBOD) and Angle-based Outlier Detection (ABOD), to distinguish irrelevant requirements from relevant requirements. The experimental results show that the proposed framework was able to exhibit better performance than previous methods. Furthermore, the use of the combination of ABOD as the outlier detection method and topic coherence as the estimation approach to determine the optimal number of topics and iterations in the proposed framework outperformed the other combinations and obtained sensitivity, specificity, F1-score, and G-mean values of 0.59, 0.65, 0.62, and 0.62, respectively.

Medically underserved communities have limited access to effective disease management resources in the U.S. Mobile health applications (mHealth apps) offer patients a cost-effective way to monitor and self-manage their condition and to communicate with providers; however, current diabetes self-management apps have rarely included end-users from underserved communities in the design process. This research documents key stakeholder-driven design requirements for a diabetes self-management app for medically underserved patients. Semi-structured survey-interviews were carried out with 97 patients with diabetes and 11 healthcare providers from medically underserved counties in South Texas to elicit perspectives and preferences regarding a diabetes self-management app, and their beliefs regarding such an app’s usage and utility. Patients emphasized the need for accessible educational content and for quick access to guidance on regulating blood sugar, diet, and exercise and physical activity using multimedia rather than textual form. Healthcare providers indicated that glucose monitoring, educational content, and graphical visualization of diabetes data were among the top-rated app features. These findings suggest that specific design requirements for the underserved may improve the adoption, usability, and sustainability of such interventions. Designers should consider health literacy and numeracy, linguistic barriers, data visualization, data entry complexity, and information exchange capabilities.

The main goal of the study was to obtain objective measures of the impact of a complete pre-season of amateur football on the players’ physical activity parameters. 17 amateur football players (24.47 ± 4.53 years) enrolled in the same team agreed to participate. They were asked to wear one accelerometer on the right hip during each training practice. Moderate-to-Vigorous physical activity (MVPA), steps and metabolic-equivalent (METs), among other parameters, were obtained and analyzed. Results showed that players spent an average 54.86% of each training session in MVPA levels, for a total of 100.33 minutes every week (3 training sessions per week). Therefore, this group of amateur football players did not meet the requirements to obtain health benefits from their physical activity program: amateur football. This finding could be considered noteworthy because there are thousands of adults worldwide who play amateur football to maintain a healthy lifestyle. They should be aware that their weekly training load is not enough to help them meet the necessary requirements. They either need more practice time (90-minute sessions) or more sessions. The pre-season training program was rated as “somewhat hard” by the participants, and quadriceps and hamstrings were mentioned as the most exerted muscles

In order to formulate a reasonable water input model for cotton fields in southern Xinjiang, scientific and rational fertilization, reduce soil carbon leaching, and improve soil carbon sequestration capacity, the undisturbed soil column leaching test was used to simulate the current field management method in the study area. Two methods of drip irrigation and flood irrigation were set up, and three irrigation and three nitrogen fertilizer levels were used to carry out the undisturbed soil column leaching test. The results showed that the amount and mode of water and nitrogen input affected the distribution and leaching loss of organic carbon and inorganic carbon in soil. When the nitrogen application rate increased from 270 kg·hm-2 to 450 kg·hm-2, the leaching loss of soluble organic carbon and soluble inorganic carbon increased significantly. When the water input increased from 6000 m3·hm-2 to 9000 m3·hm-2, the leaching loss of DOC and DIC increased significantly. The carbon leaching loss under drip irrigation was higher than that under flood irrigation. The leaching rates of DOC and DIC were the fastest under the conditions of high water ( 9000 m3·hm-2 ) and high fertilizer ( 450 kg·hm-2 ). It shows that water and nitrogen input and irrigation methods are important factors affecting soil carbon leaching. In the case of excessive water input, long-term high-frequency irrigation is the main factor affecting carbon leaching.

Increasing demand for food production with limited available water resources pose the threat to agricultural activities. The conjunctive allocation of water resources maximizes the net benefit of farmers efficiently. In this study, a novel hybrid optimization model was developed based on a genetic algorithm (GA), bacterial foraging optimization (BFO) and ant colony optimization (ACO) to maximize the net benefit of water deficit Sathanur reservoir command. The GA-based opti-mization model considered crop-related physical and economical parameters to derive optimal cropping patterns for three different conjunctive use policies and further allocation of surface and groundwater for different crops are enhanced with the BFO. The allocation of surface and groundwater for the head, middle and tail reach obtained from BFO is considered as input to ACO as a guiding mechanism to attain an optimal cropping pattern. Comparing the average produc-tivity values Policy 3 (3.665 Rs/m3) has better values relating to Policy 1 (3.662 Rs/m3) and Policy 2 (3.440 Rs/m3). Thus, the developed novel hybrid optimization model (GA-BFO-ACO) is very promising to enhance the farmer's net income as well as for the command area water conservation and can be replicated in other irrigated regions of the globe to overcome chronic land and water problems.

Water management and soil pollution are hot issues in modern agriculture. While more production of food is required, this quantity has to be obtained using the minimum level of resources and the maximum amortization of assets. Moreover, studies show that in Europe, 30% of water consumption is for agriculture, which has a strong influence not only on the amount of water consumed, but also on its quality due to pollution caused by chemical fertilizers, pesticides and other pollutants used in this sector. Therefore, a major concern is related to the use of water, its efficient consumption, which requires optimal irrigation systems both in terms of water transport and distribution. In this context, but also in the context of a turbid environmental debate, the most important facts in agriculture are the protection of the environment on a long range of time. In order to obtain good results for an irrigation system, in this paper we will present a model for calculating the best suited material for a given geographical area and the optimal water content for irrigation

Flow and transport processes in soil and rock play a critical role in agricultural non-point source pollution (ANSP) loads. In this study, we investigated the ANPS load discharged into rivers from an irrigation district in the Tibetan Plateau, and simulated ANPS load using a distributed model involving detailed descriptions of flow and ANPS transport and transformation processes in the soil and rock. Experiments were conducted for two years to measure soil water content and nitrogen concentrations and the quality and quantity of lateral flow in the rock and at the drainage canal outlet during the highland barley growing period. A distributed model, in which the subsurface lateral flow was described using a step-wise method, was developed to simulate flow and ammonium nitrogen and nitrate nitrogen transport. Sobol’s method was used to evaluate the sensitivity of simulated flow and transport processes to model inputs. The results showed that, with a 21.2% increase of rainfall and irrigation in the highland barley growing period, the average NH4+-N and NO3--N concentrations in the soil layer decreased by 10.8% and 14.3%, respectively, due to increased deep seepage. Deep seepage of rainfall water accounted for 0–52.4% of total rainfall, whereas deep seepage of irrigation water accounted for 36.6–45.3% of total irrigation. NH4+-N and NO3--N discharged into the drainage channel represented 19.9–30.4% and 19.4–26.7% of the deep seepage, respectively. The mean Nash-Sutcliffe coefficients, root mean square errors, and cumulative deviations between the measured and simulated flow rates and NH4+-N and NO3--N concentrations at the surface drainage canal outlet were 0.694, 0.081, and 0.242, respectively, indicating that the proposed method can effectively describe the hydrological and ANPS pollution migration in the plateau irrigation zone. The Sobol’ sensitivity analysis results demonstrated that subsurface lateral flow had the most important first order and total effect on the simulated flow and NH4+-N and NO3--N concentrations at the surface drainage outlet.

The Inefficient water use, varying and low productivity in Kenya public irrigation schemes is a major concern. It is therefore necessary to periodically monitor and evaluate the performance of public irrigation schemes. The performance of public irrigation in western Kenya was assessed by combining benchmarking methodology and principal component analysis. The aim was to quantify and rank the performance of pumped public irrigation schemes in Kenya. Eleven benchmarking indicators were computed for the period from 2012 to 2016 and compared to global benchmark values. The indicators used fall under agricultural productivity, water supply and financial performance categories. The computed agricultural productivity was 36%–51% in Ahero, 23%–42% in West Kano and 26%–50% Bunyala irrigation scheme. Water supply performance in Ahero, West Kano and Bunyala irrigation schemes varied from 24% to 58%, 3% to 49% and 19% to 43% respectively. Financial performance varied from 46% to 54% in Ahero, 25% to 32% in West Kano and 54%–56% in Bunyala irrigation scheme. An average overall performance efficiency of 46%, 39% and 31% was obtained in Ahero, Bunyala and West Kano irrigation schemes respectively. The performance of the irrigation schemes is very poor and measures on improving performance are needed.

The cruise ship industry has become a well-implemented industry in the Baltic Sea area, and each year, the number of cruise ship passengers rises steadily. Efficient waste management in cruising ports around the Baltic Sea is a crucial element in minimizing environmental impacts. This research involves the four selected ports of Copenhagen, Helsinki, Stockholm and Tallinn. The study applies statistics and interview data to the analysis of waste management systems for cruise ship-generated waste. The interview data involves 14 executives and professionals responsible for environmental issues and decision making in their respective ports. The interviews highlighted the need for standardized environmental legislation and related procedures, which would result in coherent measurement systems. These systems would enable transparent environmental monitoring, thus maintaining the ports’ competitiveness. A common environmental legislation would support the emerging waste management system for the whole Baltic Sea area. We suggest that ports should focus on handling specific types of wastes and collaborate as a spatial network. Specialization to allow discharge of certain fractions of waste is essential. The paper concludes by addressing demands for future research, particularly vessel- and customer behavior focused studies.

Global concerns of decreasing water availability and food security are being exacerbated by the increasing demand for food, water quality degradation, and climate change impacts. Rice is an essential crop for food security with paddy rice cultivation largely practiced using traditional flooding conditions. Water regimes and components of the soil water balance in paddy rice catchments affect uptake of water by rice roots. Paddy management, especially puddling, significantly affects soil hydrological properties, such as hydraulic conductivity, percolation, and seepage in the paddy rhizosphere. Improved knowledge of these interactions is vital for developing sustainable water management strategies in paddy fields. Alternate Wetting and Drying (AWD) practice contributes to sustainable water management, but little is known about effects of soil wetting and drying duration on gravitational soil water flow, moisture dynamics, and hydraulic conductivity in the rice paddy rhizosphere. These factors depend on soil type, rice variety, rooting conditions, and environmental factors, such as solar radiation and temperature. Here we examine the paddy cultivation environment and soil properties under traditional continuous flooding and AWD practice. Some studies indicate that the duration and frequency of wetting and soil drying under AWD practice in paddy fields affects soil hydraulic conductivity that alters the next cycle of water application. During droughts and short rainy seasons, rainfall, soil properties, and atmospheric conditions control soil moisture dynamics in paddy fields. Limitations and challenges of applying AWD in paddy soils, including the variation of hydrological properties and the need for frequent field manual monitoring of water level (WL) head, are discussed. Implications of AWD practice on yield, water use efficiency, soil hydrology, and reduction of methane emissions, are outlined along with opportunities to improve AWD integration within government policies, irrigation schemes, and adoption by farmers. Coupled crop models can provide important insights for sustainable agricultural water management.

Irrigation and fertilizer are two essential factors affecting rice root traits and yield. In this respect, a pot experiment was performed at the boro (dry season irrigated) season of 2021-22 in the Department of Agronomy of Bangladesh Agricultural University, Mymensingh, Bangladesh. The variety Binadhan-10 was planted at two irrigation conditions i.e. saturation (S) and continuous flooding (CF); two potassium (K) dose e.g. 65 kg ha-1 (K65) and 98 kg ha-1 (K98) and two nitrogen (N) dose i.e. 140 kg ha-1 (N140) and 210 kg ha-1 (N210). The experiment was laid in split plot design and replicated thrice. The findings confirmed the effectiveness of irrigation, K and N on root number (RN), root length (RL), root volume (RV), leaf area index (LAI), total dry matter (TDM), yield attributes and yield. Considering interaction among irrigation, K and N, S condition with K65 and N140 showed best performance. At 80 DAT, the highest RN (373.00), RL (1700.00 cm), RV (8.90 cm3 hill-1), LAI (4.94) and TDM (25.83 g plant-1) was obtained from this combination. Grain yield (GY) and root traits except root porosity, showed a significant positive association. Grain yield (GY) was highest (27.12 g pot-1) in S condition with K65 and N140.

Senegal central regions face rainfall deficit combined with scarcity of surface water and poor quality. Populations use groundwater for drinking and irrigation. A Groundwater assessment studiebased on 42 samples aims to determine their quality. Several parameters (TDS, TH, WQI, SAR, RSC, %N, PI, KR and MR) and ion chemistry determined . Samples exhibit an alcaline pH (7.6) and hydrochemical facies mainly Ca-Na-Mg-HCO3. The ionic dominance is Na+ ˃ Ca2+ ˃ Mg2+ ˃ K+ and HCO3- ˃ Cl- ˃ SO42- ˃ NO3- ˃ F-. From TDS and WQI index most of the groundwaters are suitable for drinking. Irrigation quality (based on SAR, %N, RSC, KR, MR) varie from excellent to good water type. Plotting chemical data (USSL salinity & Wilcox diagrams) reveals that the majority of the groundwater samples belong to the fields excellent to good and from good to permissible . Natural processes (rock weathering, mineral dissolution, evaporation and ion exchange) control groundwater quality. As the region faced a persistent rainfall deficit combine with fragile groundwater quality should alert the stakeholders. A sustainable development of the region can be faced if strong measures are planned to follow water quality evolution and quantity available for human purposes

This review deals with the adaptive mechanisms that plants can implement to cope with the challenge of salt stress. Plants tolerant to NaCl implement a series of adaptations to acclimate to salinity, including morphological, physiological and biochemical changes. These changes include increases in the root/canopy ratio and in the chlorophyll content in addition to changes in the leaf anatomy that ultimately lead to preventing leaf ion toxicity, thus maintaining the water status in order to limit water loss and protect the photosynthesis process. Furthermore, we deal with the effect of salt stress on photosynthesis and chlorophyll fluorescence and some of the mechanisms thought to protect the photosynthetic machinery, including the xanthophyll cycle, photorespiration pathway and water-water cycle. Finally, we also provide an updated discussion on salt-induced oxidative stress at the subcellular level and its effect on the antioxidant machinery in both salt-tolerant and salt-sensitive plants. The aim is to extend our understanding of how salinity may affect the physiological characteristics of plants.

This study aimed to evaluate the effects of plasma treated metal contaminated water, used for irrigation, on plant growth. Zinc (Zn) is one of the more commonly used metals. It can enter the environment from industrial processes as particles released into the atmosphere or as wastewater discharged into waterways or the ground. Exposure to large amounts of zinc, even for a short time, can seriously affect human health. In this experiment, three different DBD O2 plasma treated zinc contaminated water and a control (tap water) were used, with Arabidopsis thaliana as the model plant. The treatments were: i) Control, ii) Zn water, iii) Zn+O3(30 min), and iv) Zn+O3(60 min). Arabidopsis plant exhibited maximum growth in the Zn+O3(30 min) treatment. All growth parameters, except leaf area, followed this trend: Zn+O3(30 min) > Control > Zn water > Zn+O3(60 min). Gene expression analysis revealed that reduced metal ion stress and controlled oxidation due to active oxygen species contributed to favorable/improved growth of Arabidopsis in the Zn+O3(30 min) treatment. Therefore, 30 minutes of DBD O2 plasma treated zinc contaminated water [Zn+O3(30 min)] can mitigate the adverse effects of excess zinc ions and promote the growth of Arabidopsis plants.

Regulated deficit irrigation (RDI) strategies aim to improve water usage without reducing yield. Generally, irrigation strategy effectiveness is measured as fruit yield, with little consideration of fruit quality. As water deficit and increased plant cell sclerification often associated, this study explores the effect of RDI on pear fruit stone cells, a crucial factor affecting flesh texture. The presence, distribution and development of pear fruit stone cells under RDI and full irrigation are compared using Pyrus communis L. cv. Barlett trees, employing recently developed microscope image analysis technology. The Control treatment was maintained under non-stress conditions, while the RDI treatment received an average of 15% of Control water during the latter part of Stage I fruit development. Observations at the end of Stage I and at harvest showed no effect on stone cell presence under the tested RDI strategy. Relative area of stone cells within the flesh was greater at Stage I than at harvest, as stone cell expansion occurred early in development while the (unsclerified) parenchyma cells, dominant component of the fruit flesh, expanded until harvest. Stone cell cluster density was higher near the fruit core than in the cortex center and exterior. These initial results suggest that well-planned RDI strategies will generally not affect pear fruit stone cell content and thus, textural quality. Microscope image analysis supported results from previously used analytical techniques, principally chemical, while providing a tool for better understanding the process and timing of stone cell differentiation and thus testing the factors involved.

The gradual decline of the Aral Sea’s water level over the past five decades poses a huge environmental problem in the heart of Central Asia. Uzbekistan, once blessed with abundant water resources, now struggles with water scarcity in certain regions. While global environmental issues contribute to some degree to this dilemma, irrigation mismanagement plays a central role. This study focuses on the widespread problem of irrigation mismanagement in Uzbekistan and specifically addresses the rural district of Oltinsoy in the southern province of Surkhandarya. The main objective of this research project is to propose effective measures that can alleviate the pressing problem of water scarcity in the region while emphasizing the urgent need for well-executed projects and careful maintenance to ensure long-term sustainability at the local level. It is important to note that this study does not include an examination of the impact of global warming on the decline of water resources in Uzbekistan.

This article identifies irrigated row-crop farmer factors associated with the adoption of water conserving practices. The analysis is performed on data from a survey of irrigators in Mississippi. Regression results show that the number of irrigated acres, years of education, perception of a groundwater problem, and participation in conservation programs are positively associated with practice adoption; while number of years farming, growing rice, and pumping cost are negatively associated with adoption. However, not all factors are statistically significant for all practices. Survey results indicate that only a third of growers are aware of groundwater problems at the farm or state level; and this lack of awareness is related to whether farmers noticed a change in the depth-to-water distance in their irrigation wells. This evidence is consistent with a report to Congress from the Government Accountability Office (GAO) that recommends policies promoting the use of: (1) more efficient irrigation technology and practices and (2) precision agriculture technologies, such as soil moisture sensors and irrigation automation.