Nitrogen oxides (NOx), pivotal atmospheric pollutants, significantly imperil the environment and human health. The CH4-SCR process, leveraging the abundant and accessible methane (CH4), emerges as a promising avenue for NOx abatement. Previous studies have demonstrated that zeolite support with twelve-membered ring (12-MR) and five-membered ring (5-MR) structures are susceptible to framework collapse in the presence of water, leading to catalyst deactivation. Consequently, there is a necessity to explore novel zeolites with enhanced hydrothermal stability for application in CH4-SCR processes. This research introduced for the first time an investigation into a novel In-H-SSZ-39 catalyst, synthesized via ion exchange and meticulously optimized for preparation conditions, including calcination temperature, In ions concentraiton, and reaction conditions, including CH4/NO ratio, O2 concentration, water vapor content, and Gas hourly space velocity (GHSV). Notably, the In/H-SSZ-39 catalyst achieved superior performance of 42.1% at 605 °C, with a calcination temperature of 500 °C and an In ion concentration of 0.066 M, under CH4/NO ratio of 1.5:1, 10% O2, and GHSV of 21000 h-1. In addition, the catalytic performance was influenced by the concentration of water vapor and the GHSV value.

Farmers are continually looking for new reliable, objective and non-invasive methods for evaluation of ewe condition. Live weight (LW) and body condition score (BCS) are used by farmers as a basis to determine the condition of the animal. Body composition is an important aspect of monitoring animal condition. The body composition is the amount of fat, lean and bone; knowing the amount of each is important because the information can be used for better strategic management interventions. Experiments were conducted to establish the relationship between body composition and body parameters, at key life’s stages (weaning and pre-mating), using measurements automatically determined by an image processing ap-plication at Lincoln University sheep farm for 88 Coopworth ewes. Computerized Tomography technology was used to develop relationship with body parameters and a subset was used to validate the predicted model. Multivariate linear regression (MLR), artificial neural network (ANNs) and regression tree (RT) statistical analysis methods were evaluated to determine their efficacy to predict body fat, lean and bone. The results showed a correlation between fat, lean and bone determined by CT and the fat, lean, bone weight estimated by live weight and body parameters calculated using the image processing application with R2 values of 0.90 for fat, 0.72 for lean and 0.50 for bone using ANNs statistical model. From these results, farmers can utilize accurate measures of fat which will enhance nutritional and management practices.

Grinding processes’ stochastic nature poses a challenge in predicting the quality of the resulting surfaces. Post-production measurements for form, surface roughness, and circumferential waviness are commonly performed due to infeasibility in measuring all quality parameters during the grinding operation. Therefore, it is challenging to diagnose the root cause of quality deviations in real-time resulting from variations in the machine’s operating condition. This paper introduces a novel approach to predicting the overall quality of the individual parts. The grinder is equipped with sensors to implement condition-based maintenance and is induced with five frequently occurring failure conditions for the experimental test runs. The crucial quality parameters are measured for the produced parts. Fuzzy c-means (FCM) and Hotelling’s T-squared (T2) have been evaluated to generate quality labels from the multi-variate quality data. Benchmarked random forest regression models are trained using fault diagnosis feature set and quality labels. Quality labels from the T2 statistic of quality parameters are preferred over FCM approach for their repeatability. The model, trained from T2 labels achieves more than 94% accuracy when compared to the measured ring disposition. The predicted overall quality using the sensors’ feature set is compared against the threshold to reach a trustworthy maintenance decision.

Slimy sculpin (Uranidea cognata) inhabitat coldwater streams in southeastern Minnesota, USA, many of which were subjected to probable 2000-year flood events in August 2007. Floods scoured streambeds, created new stream channels, and greatly reduced benthic invertebrate communities that serve as the primary food resource for sculpin. Diets and Fulton condition of sculpin in Gilmore Creek (with moderate flooding) and Garvin Brook (with very severe flooding) had been examined just prior to flooding, and were re-examined two weeks after flooding to assess possible diet and condition changes. Diets, condition, and reproductive fitness of sculpin were examined seven months post-flood in these same two streams, plus nearby Trout run (also experienced very severe flooding). Sculpin condition declined significantly post-flood in Garvin Brook, but improved in Gilmore Creek. Prior to spring spawning, condition of Garvin Brook sculpin had improved, but Gilmore Creek fish condition worsened. Sculpin diets were more diverse before and after flooding in Gilmore Creek than in Garvin Brook, although diets of fish from both streams were dominated (>55%) by midge (Diptera: Chironomidae) larvae. Diets differed significantly before versus after flooding in the more severely flooded Garvin Brook, but not in Gilmore Creek. Prey number per sculpin stomach declined post-flood in Gilmore Creek, but not in Garvin Brook, although dry mass of prey/fish wet mass declined post-flood in both streams. Pre-spawn sculpin displayed no patterns in reproductive fitness (gonadosomatic index, hepatosomatic index, oocyte number) among the three streams that may have been related to flooding severity the previous summer. Although sculpin diets and condition were altered significantly immediately post-flood in the more severely flooded stream, food resource recovery apparently was rapid enough to prevent longer-term impacts on sculpin condition and reproductive fitness.

Condition monitoring of industrial robots has the potential to decrease downtimes in highly automated production systems. We suggest a new health indicator based on vibration data measurements and compare its performance with state-of-the-art health indicators regarding different criteria. This evaluation is based on different data sets from robot test rigs. We find that the proposed health indicator can detect different faults, has low temperature sensitivity and works in instationary velocity regimes. A discussion of the validity of the results rounds up our contribution.

New York State Department of Transportation designates potentially hazardous conditions on bridges as flags. From 1982 until 2006 the flags issued for the bridges owned by New York City underwent all phases typical of crises, including a gradual increase, an exponential expansion, an extended peak, a gradual decline, and a convergence to a higher but manageable level. The attempts to forecast the flag pattern as it was developing are reviewed for possible relevance to management of the transportation infrastructure and in general.

This paper introduces a new adaptation of the enthalpic lattice Boltzmann method (LBM) tailored to simulate the intricate dynamics of conjugate heat transfer in non-homogeneous media, showcasing considerable potential for enhancing heat exchanger designs. This approach expands upon the conventional enthalpic LBM framework by seamlessly incorporating tailored source terms, enabling precise representation of local and transient variations in the medium’s thermophysical properties. The resulting LBM model comprehensively captures the energy conservation equation, with exceptions made only for flow compressibility and viscous dissipation, thereby significantly augmenting its versatility in analyzing heat exchanger configurations. Verification tests, comprising assessments of both transient and steady-state solutions, unequivocally validate the accuracy of the proposed model, further bolstering its reliability for analyzing heat exchange processes within intricate heat exchanger geometries. To demonstrate its efficacy, simulations of conjugate heat transfer processes involving fluid natural convection within structured cavities were performed, imposing both Dirichlet and Neumann boundary conditions to emulate scenarios encountered in practical heat exchanger operations. The findings yield compelling insights, particularly in the transient regime, revealing the beneficial impact of structured cavities on enhancing heat transfer processes. These insights inform potential design optimizations for heat exchangers. The results emphasize the potential of the modified enthalpic LBM approach to elucidate complex heat transfer phenomena in non-homogeneous media and structured geometries, offering valuable implications for heat exchanger engineering and optimization.

The objective of this study is to determine the relationship between body condition score (BCS) and body carcass fat (BCF) of ewes at two key stages of the production cycle. Data were collected from 88 Coopworth ewes including BCS measurements manually evaluated by the farm manager, BCF measurements determined by computerized tomography (CT) and body parameter measurements automatically determined by an image processing application. Artificial neural network (ANN) statistical analysis method was used to develop a relationship between the body parameters and the BCF with an r2=0.96 and RMSE=1.01 using all top and side variables.

Multiple indicators have been used to assess the degree of exposure of fish to anthropogenic chemicals in their stream habitats. We hypothesized that brook stickleback (Culaea inconstans) in a headwater stream receiving urban and agricultural runoff (South Fork Whitewater River, SFWR) would exhibit poorer condition, reduced reproductive fitness, and greater left side-to-right side morphological asymmetry (i.e., fluctuating asymmetry or FA) than would fish from a nearby headwater stream with a forested drainage basin (Garvin Brook). Male and female fish were collected from both streams just prior to spawning in 2013-2015. In 2013 and 2014, fish were assessed for overall condition (Fulton’s K) and internal measures of condition (hepatosomatic index, HSI) and reproductive fitness (gonadosomatic index [GSI], total egg count, egg weight). In 2015, measurements of head length, jaw length, eye diameter, pectoral fin length, and pelvic fin length were made on both sides of each fish for use in assessing degree of FA. We observed declining condition with fish size, increased liver size, and reduced egg counts and egg size in female brook stickleback in SFWR relative to those from Garvin Brook. SFWR females had significantly higher FA than Garvin females for all structures assessed except pelvic fin length. FA also was slightly higher for all structures in SFWR males compared to Garvin males, but differences were not significant. A composite FA index combining all measurements from an individual fish into a single value displayed highly significant differences for female fish (SFWR FA >> Garvin FA), but not for male fish (SFWR FA = Garvin FA). Exposure of brook stickleback to reduced water quality in SFWR during early development appears to increase morphological asymmetry in female (but not male) fish, and continuing exposure to compromised water quality throughout life impacts both general condition and reproductive fitness of stickleback, especially older female fish, in SFWR.

The process industry is confronted with rising demands for flexibility and efficiency. One way to achieve this are modular process plants that consist of pre-manufactured modules with their own decentralized intelligence. Plants are then composed of these modules as unchangeable building blocks and can be easily re-configured for different products. Condition monitoring of such plants is necessary, but available solutions are not applicable. The authors suggest an approach in which model-based symptoms are derived from few measurements and observers that are based on manufacturer knowledge. The comparisons of redundant observers lead to residuals that are classified to obtain symptoms. These symptoms can be communicated to the plant control and are inputs to an easily adaptable diagnosis. The implementation and validation at a modular mixing plant showcases the feasibility and the potential of this approach.

Condition monitoring of bearings is an open issue. The use of the stator current to monitor induction motors has been validated as a very advantageous and practical way to detect several types of faults. Nevertheless, for bearing faults the use of vibrations or sound generally offers better results in the accuracy of the detection although with some disadvantages related to the sensors used for monitoring. To improve the performance of bearing monitoring, it is proposed to take advantage of more information available in the current spectra, beyond the usually employed, incorporating the amplitude of a significant number of sidebands around the first eleven harmonics, growing exponentially the number of fault signatures. This is especially interesting for inverter-fed motors. But, in turn, this leads to the problem of overfitting when applying a classifier to perform the fault diagnosis. To overcome this problem, and still exploit all the useful information available in the spectra, it is proposed to use shrinkage methods, which have been lately proposed in machine learning to solve the overfitting issue when the problem has much more variables than examples to classify. A case study with a motor is shown to prove the validity of the proposal

Запропонована методика спрямована на визначення та прогнозування технічного стану елементів мосту, що може слугувати прогресивним інженерним інструментом для оцінки надійності та довговічності. Розроблено на основі фундаментальних досліджень, які узагальнюють досвід вивчення фізико-механічних і фізико-хімічних властивостей матеріалів мостових конструкцій, що експлуатуються в реальних умовах. Теоретичною основою методики є модель надійності та прогнозування залишкового ресурсу елементів мосту на основі теорії Маркова. Розроблена методика призначена для оцінки технічного стану окремих елементів мосту з подальшою комплексною оцінкою всієї конструкції. За показник технічного стану прийнята надійність при експлуатації. Цей кількісний показник надійності в моделі є критерієм для: оцінки рівня безпеки елементів мосту; ранжування елементів мосту за необхідністю для конкретних видів ремонту, реконструкції або заміни; стратегічне планування витрат на ремонт або реконструкцію в умовах обмеженого фінансування; прогнозування залишкового ресурсу елементів. Для застосування розробленої методики запропоновано алгоритм оцінки та прогнозування технічного стану мостів. Було проведено математичний експеримент розробленої методики, який підтвердив адекватність висунутої гіпотези, тобто використання моделі надійності та прогнозування залишкового ресурсу елементів мосту. По-перше, запропоновано триступеневий механізм уточнення технічного стану мосту, суттєво підвищення точності розрахунків. Тому розроблена методика має практичне значення і може слугувати основою для інформаційно-аналітичних систем управління станом мостів.

Textbooks tell us that the coupling constant of Dirac magnetic monopoles is 34.259. Here I show by using the quark hypothesis that at zero temperature the coupling constant is as high as 308.331. Moreover I show that it is a running coupling constant and is smaller than 0.5 at the Planck temperature.

Despite decades of conflict and political instability, drought has also severely affected Afghanistan’s rural population and agriculture sector. Agriculture is a primary livelihood source in the country, and recurring droughts have put cascading pressures on the agricultural economy and food security. Therefore, understanding the key drivers of all risk components through a comprehensive risk assessment is critical for proactive drought risk management. This study assesses agricultural drought risk from an integrated perspective, emphasizing the combined effects of drought hazard, exposure, and vulnerability. Multi-methods research was conducted, employing remote-sensing-based analysis covering a period from 2001 to 2023. We used the Vegetation Condition Index (VCI) to analyze the drought hazard for the agricultural system during the winter growing season. The vulnerability indicators and exposure levels of the agricultural system were determined through spatial analysis combined with a literature review and expert consultation. An adapted conceptual framework of the Intergovernmental Panel on Climate Change (IPCC) was utilized, which incorporates hazard, exposure, and vulnerability to define risk. The results revealed that droughts occur frequently and in changing patterns throughout the country. All provinces have experienced drought over the past two decades, albeit with varying severities and frequencies. Severe droughts were observed in 2001-2002, 2008, 2011, 2018, and consecutively in 2021-2022-2023. Drought vulnerability and exposure vary widely across the country, particularly in the northern, western, and southern provinces. The final calculated risk shows that most provinces in southern and southwestern zones face a high risk of recurrent droughts, further exacerbating agricultural challenges in these regions. Such analyses under a multimethod approach provide information on spatial patterns of drought risk and drivers of agricultural systems that can support decision-makers and policymakers in formulating strategies to reduce drought risk and adapt to the agricultural sector.

In cutting processes tool condition affects the quality of the manufactured parts. As such an essential component to prevent unplanned downtime and to assure machining quality is having information about the state of the cutting tool. The primary function of it is to alert the operator that the tool has reached or is reaching a level of ware beyond which behavior is unreliable. In this paper the tool condition is being monitored by analyzing the electric current on the main spindle via an artificial intelligence model utilizing a LSTM neural network. In the current study the tool is monitored while working on a cylindrical raw piece made of AA6013 aluminum alloy with a custom polycrystalline diamond tool, for the purposes of monitoring the ware of these tools. Spindle current characteristics were obtained using external measuring equipment to not influence the operation of the machine included in a larger production line. As a novel approach, an artificial intelligence model based on a LSTM neural network is utilized for the analysis of the spindle current obtained during a manufacturing cycle and assessing the tool ware range in real time. The neural network was designed and trained to notice significant char-acteristics of the captured current signal. The conducted research serves as a proof of concept for use of a LSTM neural network-based model as a method of monitoring the condition of cutting tools.

Numerous elements, such as the composition and characteristics of carbon nanomaterials, the composition and characteristics of the matrix material, moisture levels, temperature, and loading circumstances, influence the piezoresistive behaviour of self-sensing cementitious composites. While some past research has explored the impact of some of these factors on the performance of self-sensing cementitious composites, additional investigations need to be conducted to delve into how loading conditions affect the sensitivity of self-sensing cement-stabilized composites. Therefore, this study explores the influences of various loading conditions (i.e., location of loading regarding the location of recording electrodes, and loading level) on the electromechanical performance of self-sensing cement-stabilized sand. To this end, firstly, the evaluation of the percolation threshold based on 10% cement-stabilized sand specimens containing various multiwall carbon nanotubes (MWCNTs) and graphene nanoplatelets (GNPs) was performed. Then, 10% cement-stabilized sand containing 4% MWCNTs/GNPs was tested under various cyclic compressive stresses. The results suggested that the distance between the loading area and the electrode location used for recording the electrical resistance significantly impacts the sensitivity of cement-stabilized sand. Optimal sensitivity was achieved when the electrodes were positioned directly beneath the loading area. Moreover, the study yielded that the stress sensitivity of self-sensing cement-stabilized sand increases proportionally with the stress level. Examination through scanning electron microscopy (SEM) demonstrated that the loading condition influences the bridging characteristics of carbon nanomaterials in cement-stabilized sand, leading to diverse electromechanical behaviors based on the loading condition. This study underscores the importance of considering specific parameters when designing the application of self-sensing cement-stabilized sand for practical field use.

This study investigated gender differences in exposure to hazardous factors, health problems, and work environment satisfaction and identified the effects of such differences on farmworkers in Korea. Through the 6th Korean Working Conditions Survey (KWCS) conducted by the Occupational Safety and Health Research Institute (OSHRI), the raw data on 2,347 farm-workers were analyzed to present descriptive statistics for demographic characteristics, exposure to hazardous factors, health problems, and work environment satisfaction. We compared be-tween genders using chi-squared tests and investigated the effects of gender-specific working conditions on work-related health problems and work environment satisfaction using multi-variate logistic analysis. The results were presented as an odds ratio (OR) with 95% confidence interval. Job position predicted work-related health problems in male farmworkers and work environment satisfaction in female farmworkers. Furthermore, female farmworkers perceived themselves to have more health problems than male farmworkers. Nevertheless, female farm-workers received less health and safety information and had lower work environment satisfac-tion compared to male farmworkers. The findings may improve the occupational welfare of farmworkers through continuous enhancement of the agricultural labor environment. Keywords: work-related; working condition; farmworker; agriculture; musculoskeletal; KWCS

With enhanced life expectancy and ageing global populations, the prevalence of multimorbidity continues to increase. However, there is a dearth of evidence linked to multimorbidity diagnoses, treatments and health outcomes which remains a concern for future proofing optimal healthcare services. Generating evidence is critical to managing multimorbidity, promoting public health and minimizing health inequalities via effective healthcare policies that improve quality of life for vulnerable populations. This study assessed meta-epidemiology of multimorbidity to report the gaps in scientific knowledge and clinical practice. A systematic methodology was designed and published in PROSPERO (CRD42022347308) to report meta-epidemiology analyses using databases including PubMed, Web of Science, ScienceDirect, EMBASE, The Cochrane Gynaecology and Fertility Group Specialised Register of Controlled Trials and MEDLINE for studies published between the 1^st of January 1980 - 31^st December 2022. A random-effects model was used to estimate the pooled proportion of multimorbidity in adults. Forest plots, pooled odds ratios and statistical heterogeneity metrics were used to assess the association between multimorbidity and investigated factors. Funnel plots and Egger’s regression were used to detect and correct for publication bias. Our findings identified women to be 0.32 times more likely to have multimorbidity in comparison to males. In regard to ethnicity, white people were 0.47 times less likely to develop comorbidities than black people. People who identified as a drinker or unmarried were more likely to develop comorbidities than those who are non-drinkers or married, respectively. Regardless of smoking status, people were equally likely to have comorbidity. In terms of environmental influences, people in rural areas were found to be 0.2 times less likely to have comorbidity in comparison to those living in urban areas. Interestingly, people with a higher education level were 0.57 times more likely to develop comorbidities than those with only a high school education. It is evident that multimorbidity has a significant burden globally and impacts the provision of care necessitated across populations given its association with several social determinants of health. Robust research and healthcare policies are required to better manage multimorbidity in patients. An example of such intervention includes employing prevention programs to reduce risk and incidence of multimorbidity within at-risk populations.

In a series of recent works the relevance of gravitational boundary degrees of freedom and their dynamics in gravity quantization and black hole information has been explored. In this work we further the progress by keenly focusing on the boundary degrees of freedom as the origin of black hole entropy. Wald's entropy formula is scrutinized, and the reason that the Wald's formula correctly captures the entropy of a black hole is examined. Afterwards, limitations of the Wald's method are discussed; a coherent view of entropy based on boundary dynamics is presented. The discrepancy observed in the literature between holographic and Wald's entropies is addressed. We generalize the entropy definition so as to handle a time-dependent black hole. Large gauge symmetry plays a pivotal role. Non-Dirichlet boundary conditions and gravitational analogues of Coleman-De Luccia bounce solutions are central in identifying the microstates and differentiating the origins of entropies associated with different classes of solutions. The result in the present work leads to a view that black hole entropy is entanglement entropy in a thermodynamic setup.

Concrete condition assessing penetrometers need to be able to distinguish between making contact with a hard (concrete) surface as opposed to a semi-solid (corroded concrete) surface. If a hard surface is mistaken for a soft surface, concrete corrosion may be over-estimated, with the potential for triggering unnecessary remediation works. Unfortunately, the variably-angled surface of a concrete pipe can cause the tip of a force-sensing tactile penetrometer to slip and thus to make this mistake. We investigated whether different shaped tips of a cylindrical penetrometer were better than others at maintaining contact with concrete and not slipping. We designed a range of simple symmetric tip shapes, controlled by a single superellipse parameter. We performed a finite element analysis of these parametric models in SolidWorks before machining in stainless steel. We tested our penetrometer tips on a concrete paver cut to four angles at 20∘ increments. The results indicate that penetrometers with a squircle-shaped steel tip (a=b=1,n=4) have the least slip, in the context of concrete condition assessment.

Understanding the effect of horseshoe-surface combinations on hoof kinematics at gallop is relevant for optimising performance and minimising injury in racehorse-jockey dyads. This intervention study assessed hoof breakover duration in Thoroughbred ex-racehorses from the British Racing School galloping on turf and artificial tracks in four shoeing conditions: barefoot, aluminium-rubber composite (GluShu), aluminium and steel. Shoe-surface combinations were tested in a randomized order and horse-rider pairings (n=14) remained constant. High-speed video cameras (Sony DSC-RX100M5) filmed the hoof-ground interactions at 1000 frames per second. The time taken for a hoof marker wand fixed to the lateral hoof wall to rotate through an angle of 90 degrees during 384 breakover events was quantified using Tracker software. Data were collected for leading and non-leading front and hind limbs, at gallop speeds ranging from 23–56 km h-1. Linear mixed-models assessed whether speed, surface, shoeing condition or any interaction between these parameters (fixed factors) significantly affected breakover duration. Day and horse-rider pair were included as random factors and speed was included as a covariate. The significance threshold was set at p<0.05. For all limbs, breakover times decreased as gallop speed increased (p<0.0005), although a greater relative reduction in breakover duration for hindlimbs was apparent beyond approximately 45 km h-1. Breakover duration was longer on turf compared to the artificial surface (p≤0.04). In the non-leading hindlimb only, breakover duration was affected by shoeing condition (p=0.025) and an interaction between shoeing condition and speed (p=0.023). Future work seeks to relate these results to hoof accelerometer data.

In this paper, we introduce a simple yet powerful and working version of the molecular dynamics code using the Python 3.9 language. The code contents are published in the link given in the appendix 1. The structure and components of the program is given in detail using flowcharts and code snippets. The program consists of major features like velocity verlet integrator, thermostats, COM removal, input and output modules, virial, pressure, and other thermodynamic quantities estimation etc. The author believes that this program will be helpful to graduate students who perform research in molecular dynamics simulations who intend to write their own code instead of the sophisticated open source packages.

India’s energy needs are in demand with the increase in energy and other electric uses which is highest among the world. There are sectors which heavily rely on energy generated by fossil fuels but there is also seen a paradigm shift towards renewable energy sources. If India continues to rely on the former then they end up blocking development in energy system meeting uncertainties and face difficulties in supply of fossil fuels. To meet the fast-growing economy, India needs to supply the energy 3-4 times more of what they are supplying now. Government of India has become aware of the situation and has started facilitating polices in action towards a sustainable energy. As of 2019, India’s on grid renewable energy capacity is 85.9 GW. Government is working to establish 500 GW of renewable energy source by 2030.

Automatic ancient Roman coin analysis only recently emerged as a topic of computer science research. Nevertheless, owing to its ever-increasing popularity, the field is already reaching a certain degree of maturity, as witnessed by a substantial publication output in the last decade. At the same time, it is becoming evident that research progress is being limited by a somewhat veering direction of effort and the lack of a coherent framework which facilitates the acquisition and dissemination of robust, repeatable, and rigorous evidence. Thus, in the present article, we seek to address several associated challenges. To start with, (i) we provide a first overview and discussion of different challenges in the field, some of which have been scarcely investigated to date, and others which have hitherto been unrecognized and unaddressed. Secondly, (ii) we introduce the first data set, carefully curated and collected for the purpose of facilitating methodological evaluation of algorithms and, specifically, the effects of coin preservation grades on the performance of automatic methods. Indeed, until now, only one published work at all recognized the need for this kind of analysis, which, to any numismatist, would be a trivially obvious fact. We also discuss a wide range of considerations which had to be taken into account in collecting this corpus, explain our decisions, and describe its content in detail. Briefly, the data set comprises 100 different coin issues, all with multiple examples in Fine, Very Fine, and Extremely Fine conditions, giving a total of over 650 different specimens. These correspond to 44 issuing authorities and span the time period of approximately 300 years (from 27 BC until 244 AD). In summary, the present article should be an invaluable resource to researchers in the field, and we encourage the community to adopt the collected corpus, freely available for research purposes, as a standard evaluation benchmark.

The paper presents a case study and a model for condition monitoring of Diesel engines’ oil of urban buses, through the accompaniment of the evolution of its degradation, with the objective to implement a predictive maintenance policy. Along time, because the usage, there is some decay in the lubricant properties. However, in normal functioning conditions, the lubricants properties, at the time the manufacturers recommend its changing, regardless of they are within the safety limits. Then, based on the accompaniment of the lubricants’ oil condition, the intervals of oil replacement can be enlarged what implies the availability increasing and the corresponding production increasing of the equipment. The model presented in this paper shows its potential to be spread to other types of equipment and organisations that want can implement similar maintenance policies, to achieve the best availability based on the real equipment health conditioning conditions

In order to serve the online monitoring of the aging state of high-voltage (HV) cables, a novel method based on impedance spectroscopy has been proposed. Firstly, the method uses the Debye model to restore the dielectric response of the insulating material from the mechanism. Secondly, based on the transmission line equation, a calculation model for the impedance spectroscopy of HV cables was established. Then, the characteristics of the impedance spectroscopy under different overall aging and local deterioration states were analyzed. Further, the simulation analysis of the influence of factors such as load factor, length, metal resistivity, and geometric parameters on the impedance spectroscopy was carried out. Finally, diagnostic criteria were proposed, which are divided into two major categories: known cable parameters and unknown cable parameters. If the geometric parameters are known, the aging state can be determined using the resonance peak values and the resonance peak frequencies. If the geometric parameters are unknown, it is necessary to test the impedance spectroscopy in the healthy condition of the cable as the baseline curve. By comparing the impedance spectroscopy of the cable under test with the baseline curve, the aging development trend of the cable can also be determined.

Greenhouse gas (GHG) emission-induced climate change, particularly occurring since the mid-20th century has been considerably affecting short-term weather conditions, such as increasing weather variability, and incidence of extreme weather-related events. Milk production is sensitive to such changes. In this study, we use spatial panel econometric models, Spatial Error Model (SEM) and Spatial Durbin Model (SDM), with a panel dataset at the state-level varying over seasons, to estimate the relationship between weather indicators and milk productivity, in an effort to reduce the bias of omitted climatic variables that can be time-varying and spatially correlated and cannot be directly captured by conventional panel data models. We find an inverse U-shaped effect of summer heat stress on milk production per cow (MPC), indicating that milk production reacts positively to a low-level increase in summer heat stress, and then MPC declines as heat stress continues increasing beyond a threshold value of 72. Additionally, fall precipitation exhibits an inverse U-shaped effect on MPC, showing that milk yield increases at a decreasing rate until fall precipitation rises to 14 inches and then over that threshold milk yield declines at an increasing rate. We also find that, relative to conventional panel data models, spatial panel econometric models could improve prediction performance by leading to smaller in-sample and out-sample root mean squared errors. Our study contributes to the literature by exploring the feasibility of promising spatial panel models and resulting in estimating weather influences on milk productivity with high model predicting performance.

. (1) Background: The aim of this study was to verify the differences in body composition, power and lower limb muscle strength in futsal players from three competitive levels. (2) Methods: The study involved 68 players (24.26 ± 4.63 years), divided into three competitive levels: elite, sub-elite and amateur. To assess physical condition, we used the Inbody270, the ChronoJump and the Isokinetic Dynamometer and used the non-parametric Kruskal-Wallis test to compare the three independent variables. (3) Results: There were no differences in body composition between the players at the three competitive levels. With regard to lower limb muscle power and strength, the elite group showed, on average, better performance and capabilities, especially when compared to the amateur group, and all groups showed an H/Q ratio > 60%. (4) Conclusions: We conclude that there are not many differences in physical condition between the three competitive levels of futsal, despite the unequal sporting demands between them. However, the elite group has greater power and muscle strength, and both groups showed a high probability of lower limb muscle injury at this stage of the sporting season, alerting sports training professionals to the importance of individualized analysis of physical condition and performance capacity.

This comparative research paper examines the difference between sugarcane harvesting practices in Thailand and India. The objective of research was to investigate and compare current pattern of sugar harvesting and its impact of these approaches on labor health conditions in the sugarcane industry of both the countries as India and Thailand. Data were collected from target population groups (40 each) from study areas, namely Kanchanaburi and Saraburi provinces in Thailand and Beed District of Maharashtra state from India. The data showed that comparatively in Thailand, adoption of mechanized harvesting was seen more. However in India, mechanized harvesting was seen increasing but still people were largely involved in sugarcane harvesting and it had ill effects on the overall health of female workers specially. The result clearly indicated that there was drastic difference in harvesting system and labor health conditions in Thailand and India. The findings underscore the multifaceted impact of their occupation on various aspects of health including general well-being of female sugarcane cutters. The strenuous nature of sugarcane cutting, coupled with inadequate protective measures and limited access to healthcare has contributed to a range of health care challenges among female sugarcane cutters in India.

In this study, we had investigated the use of high-pressure homogenization (HPH) as a novel pretreatment method for the extraction of sodium alginate (SA) from Laminaria japonica. Our results demonstrated that under optimal condition of 100 MPa HPH pressure, 4 cycles, pH 6.0, and 0.5% ethylene diaminetetra acetic acid tetrasodium salt (EDTA) concentration (w/v) for 3.0 hours, the extraction yield of HPH reached 34%, which was higher than that achieved by previous methods such as complex enzyme hydrolysis (CE), ultrasonic-assisted extraction (UAE), and complex enzyme-ultrasonic combined method (CE-UC). To further characterize the extracted SA, we conducted a comprehensive analysis using SEM, FTIR, MRS, NMR, XRD, TGA, and T-AOC assay. Our findings revealed the following: 1) HPH treatment significantly disrupted the structure of L. japonica cells and reduced their crystallinity to 76.27%, the lowest among all pretreatment samples; 2) the extracted SA had a greater tendency to form elastic gels; and 3) the antioxidant activity of HPH-extracted SA reached 0.02942 mgVceq∙mg-1, which was the highest among all tested methods. Based on these results, we concluded that HPH extraction not only increased the yield of SA but also enhanced its antioxidant activity.

The objective was to evaluate the metabolic and performance differences in primiparous Nellore cows, which became pregnant at 14 or 24 months. Thirty-eight primiparous Nellore cows, which were bred for first pregnancy at 14 or 24-mo of age, with 202 ± 5 days of gestation were divided into two treatments according to breeding age: 14 or 24 months in the breeding season. Cows were evaluated for weight, body condition score (BCS), carcass characteristics, milk yield and blood parameters: glucose, total proteins, albumin, urea, beta-hydroxybutyrate (β-OHB), non-esterified fatty acids (NEFA), progesterone, and for insulin-like growth factor-1 (IGF-1). There was an in-teraction (p < 0.01) between age at first pregnancy and time for body weight, where 24-mo cows were heavier, but both categories loss weight at parturition which concomitant decrease in the BCS (p < 0,01). However, BW of 24-mo remained stable (p > 0.05) after parturition. Milk yield was higher in 24-mo cows (p < 0.01). Interaction between sampling time and breading age was observed for Longissimus muscle area (p < 0.01) and tended for Longissimus muscle depth, where 24-mo cows had greater values in prepartum. Calves born from the cows bred at 24-mo, where was higher (p < 0.01) from 160 days of age until weaning at 240 days. Glucose, urea, total proteins, albumin and NEFA were affected (p < 0.01) by the parturition data-point. There was an interaction (p < 0.01) between breading age and sampling period for β-OHB. Progesterone concentration was greater in 24-mo cows (p < 0.01). It was concluded that primiparous cows that became pregnant at 14 or 24 months of age have different nutritional and metabolic responses.

Condition monitoring is today a crucial and unavoidable practice related to the use of widespread mechanical components as rolling element bearings. Despite the diffusion of new data-driven techniques to analyze vibrational signals and detect the state of health of the system, mathematical tools, deriving more or less directly from the Fourier transform and defined either on the time or the frequency domain or on a combination of them (i.e. signal-based techniques), remain still essential and irreplaceable in the light of the deep and detailed information they provide. In this paper the diagnostic efficacy of an ample spectrum of these methods is investigated, applying them to study faults occurrence in the first bearing dataset released by NASA IMS Center. Thanks to their analytical definition, which is formulated taking into consideration to some extent the mathematical nature of the signal, faults signatures can be clearly identified and their temporal development can be fully traced, confirming their ability as powerful means to constantly monitor the state of a system even in real-time applications.

In the present paper, we introduced a quadratically convergent Newton’s like normal S2 iteration method free from the second derivative for the solution of nonlinear equations permitting 3 f'(x) = 0 at some points in the neighborhood of the root. Our proposed method works well 4 when the Newton method fails. Numerically it has been verified that the Newton’s like normal 5 S-iteration method converges faster than Fang et al. method [A cubically convergent Newton-type 6 method under weak conditions, J. Compute. and Appl. Math., 220 (2008), 409-412]. We studied 7 different aspects of normal S-iteration method. Lastly, fractal patterns support the numerical 8 results and explain the convergence, divergence, and stability of method.

In the field of industrial process monitoring, more and more interest is being shown in specific process categories. These include time-varying processes, that is, those processes whereby the response one receives as output from the system depends on when the input signal is sent into it. There are many reasons for this process variability and such contexts are not always analyzed with this operational characteristic at their core. At the same time, interest in certain categories of techniques is also becoming more prominent, to meet certain application needs. Among these, clustering and unsupervised techniques in general are gaining ground. This is largely due to the difficulty of finding fault data with which to train, for example, supervised models. On the other hand, the clustering technique, on which this contribution focuses, also makes it possible to compensate for the lack of complete knowledge of the structure of the process itself. With these two considerations in mind, this contribution proposes a literature review on the topic of clustering applied in time-varying contexts, in the maintenance field. The aim is to present an overview of the main fields of study, the role of clustering in this context and the main clustering techniques used.

In 1895, Korteweg and de Vries (KdV), derived their celebrated equation describing the motion of waves of long wavelength in shallow water. In doing so they made a number of quite reasonable assumptions, incompressibility of the water and irrotational fluid. The resulting equation, the celebrated KdV equation, has been shown to be a very reasonable description of real water waves. However there are other phenomena which have an impact on the shape of the wave, that of vorticity and viscosity. This paper examines how a constant vorticity affects the shape of waves in electrohydrodynamics. For constant vorticity, the vertical component of the velocity obeys a Laplace equation and also has the usual lower boundary condition. In making the vertical component of the velocity take central stage, the Burns condition can be thus bypassed.

This study describes the first successful rearing of capelin from hatch to adulthood in a laboratory setting using intensive culture methods. Over the span of about two years the capelin were reared in aquaculture tanks under a constant temperature of 7°C. The capelin demonstrated a robust linear growth during their first year of life, with a mean length increment of 0.36 mm per day. Due to their accelerated growth, some of the capelin became sexually mature as early as one-year post hatch. The first year was characterized by a rapid increase in condition factor (CF) while the second year showed a plateau. The von Bertalanffy growth equation effectively described the two-year growth of the cultivated capelin, predicting an asymptotic length (L∞) of 18.4 cm, similar to the 18.6 cm median L∞ of wild Icelandic capelin (1981–2018 cohorts). The cultivated capelin were projected to reach this length in 2.6 years, compared to about 6 years for wild capelin. This study provides new insights into the growth dynamics of capelin, and although the species is sensitive to handling, it demonstrates that intensive culture methods can be used to investigate biological aspects of this important forage species.

The implementation of systems condition monitoring is of utmost importance in ensuring effective maintenance, optimal efficiency, and adherence to established design criteria. This research presents a novel and economically efficient Digital Twin Model (DTM) designed for the purpose of Live Condition Monitoring (LCM) and Predictive Lifecycles. This study discusses the enhancement of DTs modelling by decreasing the number of dimensions from five to three: Physical, Digital, and Connection entities. The resulting model exhibits a notable improvement in accuracy and efficiency. The suggested Dynamic Time Model (DTM) enhances the empirical pre-set average load for both simulation and experimentation by 35.7%, which is equivalent to a 1.6-fold increase. The DTM demonstrates a significant enhancement in the empirical predefined average lifecycles of the system, as shown in the real-life case study. Specifically, the DTM achieves a 12-fold improvement compared to the simulated results and a nine-fold improvement compared to the experimental findings. The suggested Dynamic Time Management (DTM) demonstrates a significant enhancement in the average lifecycles of the system, with a 19.7% improvement (equivalent to 1.2 times greater) compared to the wireless DTM results obtained through the application of the real system load. The suggested Dynamic Time Warping (DTW) method's accuracy and efficiency are shown via a practical case study using the suspension system of a Peugeot 3008 vehicle.

This study investigates the application of frequency augmentation techniques to improve condition-based maintenance (CBM+) systems for airborne weapon systems, focusing on the predictive accuracy of velocity and acceleration parameters. Utilizing MIL-STD-1553B MUX data from 138 sorties of fixed-wing aircraft, we explored six augmentation methods, including traditional interpolations (linear, quadratic, cubic spline) and advanced machine learning models (K-Nearest Neighbor, LSTM, and Bi-LSTM). Our findings indicate that while traditional methods like linear interpolation are more effective for velocity parameters, advanced ML techniques, particularly Bi-LSTM, provide better results for acceleration parameters, which exhibit more complex and rapid variability. The study underscores the necessity of tailoring augmentation approaches to specific parameter characteristics and highlights the potential of ML models in capturing intricate patterns within time-series data. These insights are critical for advancing CBM+ systems in military avionics, enhancing reliability and operational efficiency through improved data processing strategies. Future work should explore more performant augmentation techniques, such as by integrating multi-dimensional datasets, and explore ways to develop automated tools for a more powerful predictive maintenance framework.

Negative thermal quenching (NTQ) denotes that the integral emission spectral intensity of a given phosphor increases continuously with increasing temperature up to a certain elevated temperature. NTQ has been a subject of intensive investigation in recent years and a large number of phosphors were reportedly to have exhibited NTQ. In this paper, a collection of results in the archival literature about NTQ of specific phosphors are discussed from a macroscopic viewpoint in the following three aspects:1) Could NTQ of a given phosphor be reproducible?2) Could the associated data for a given phosphor exhibited NTQ be in line with the law of conservation of energy?3) Could NTQ of a given phosphor be demonstrated in a prototype WLED device? By analyzing typical cases based on common sense, it is hoped to increase the awareness for the issues with the papers reporting NTQ of the specific phosphors based on spectral intensity along with for the importance of keeping stability and consistency of the measurement conditions in temperature-dependent spectral intensity measurement which is a prerequisite for the validity of the measurement results.

Condition monitoring of rotating shafts is essential for ensuring the reliability and optimal performance of machinery in diverse industries. In this context, as industrial systems become increasingly complex, the need for efficient data processing techniques is paramount. Deep learning has emerged as a dominant approach due to its capacity to capture intricate data patterns and relationships. However, a prevalent challenge lies in the black-box nature of many deep learning algorithms, which often operate without adhering to the underlying physical characteristics intrinsic to the studied phenomena. To address this limitation and enhance the fusion of data-driven methodologies with the fundamental physics of the system under study, this paper leverages physics-informed neural networks (PINNs). Specifically, a simple but realistic numerical case study of an extended Jeffcott rotor model, encompassing damping effects and anisotropic supports for a more comprehensive modelling, is considered. PINNs are used for the estimation of five parameters that characterize the health state of the system. These parameters encompass the radial and angular position of the static unbalance due to the disk installed on the shaft, the stiffness along the principal axes of elasticity, and the non-rotating damping coefficient. The estimation is conducted solely by exploiting the displacement signals from the centre of the disk and, to showcase the efficacy and precision provided by this novel methodology, various scenarios involving different constant rotational speeds are examined. Additionally, the impact of noisy input data is also taken into account within the analysis.

Rice cultivation serves as a significant anthropogenic source of methane (CH4), a primary greenhouse gas, and nitrous oxide (N2O), a secondary greenhouse gas. Although N2O emissions remain relatively small compared to CH4 emissions, they are remarkably affected by nitrogen-fertilized soil conditions during rice cultivation. To date, investigations are very limited concerning N2O emissions from rice cultivation in relation to environmental factors such as temperature, rainfall, and soil properties. In this case study, we investigated the characteristics of N2O emissions in the central region of South Korea, where a single rice cropping cycle occurs annually over a span of three years, from May 2020 to May 2023. We investigated the impact of variations in temperature and soil moisture on N2O emissions during rice cultivation. In this context, we attempted to discover the complex dynamics of N2O emissions by comparing longer fallow periods with the rice cultivation periods and extended non-dry periods with irrigated periods. We observed that extremely high N₂O flux events encountered during the fallow period appeared to have a substantial impact on the yearly cumulative N₂O emissions, surpassing the impact of cumulative N₂O emissions during the rice cultivation period. We found that high N₂O flux events arose not only from artificial nitrogen inputs but also due to temperature and soil moisture variations influenced by weather conditions. We concluded that assessing N₂O emissions solely based on the rice cultivation period would underestimate annual emissions. To prevent underestimation of N₂O emissions, continual gas collection throughout a year covering both rice cultivation and fallow phases is required in align with monitoring of varying temperature and soil moisture conditions. Based on our findings, we recommend that at least a three whole year evaluation period is needed to ensure estimation accuracy of N₂O emissions under varying nitrogen fertilization rates. Also, the findings from this study would help prepare a further revision or refinement of N2O emission factor from rice cultivation in the national greenhouse gas inventories defined by the inter-governmental panel on climate change (IPCC).

Condition monitoring is a primary principle for predictive maintenance. In this regard, several techniques have been employed, each of which has its strengths and weaknesses. In this work, the relationships between the electrical (ε', ε", tan δ) and chemical (Fe, Pb, Cu, Cr, Al, Si, Zn) properties of engine lubricants were investigated using soft computing models. The models' performance was evaluated considering some criteria, including RMSE and MAPE. The RBF model was determined as the best one to predict chemical properties (Fe, Pb, Cu, Cr, Al, Si, Zn) through electrical indexes (ε', ε", tan δ). The RBF’s parameters (e.g., hidden size and training algorithm) were then optimized for optimum performance. For instance, in the RBF model implemented to predict Al, the best hidden size and training algorithmic were 15 and 'trainlm', respectively. Finally, by sensitivity analysis it was observed that eliminating any of the inputs does not improve the performance of the RBF model performance. In general, the results of the current research showed that the electrical properties (e.g., ε', ε", tan δ) have the capability of estimating the contaminants in the lubricants which can improve the existing monitoring methods.

Background and objectives: Suicide is a topical issue in Lithuania and all around the world. It is the second most common cause of death among young people. There is a lack of research studies on the psychosocial aspects of adolescent suicide in Lithuania. This study aimed to evaluate demographics, life circumstances, and health conditions as factors related to suicide attempt in adolescents. Materials and Methods: A retrospective study was performed at the Children’s Hospital (Vilnius University Santara Clinics). Medical documentation of adolescents treated in this hospital after suicide attempts from January 2011 to April 2018 were analyzed. Results: There were 117 cases of hospitalization due to suicide attempts during this period, and 102 cases were included in the study. There were 83.8% female and 16.2% male patients with an average age of 15.02 ± 1.9 years (p = 0.405); 40.6% of patients lived in divorced families, 17.7% lived in orphanages, 4.2% lived in foster care, 36.4% suffered from parental alcoholism, and 17.2% experienced suicide in their close surroundings. They had comorbidities (girls 72.5%, boys 68.8%), mostly depression (31.3%), were suffering from bullying (54.1%) or violence (26.0%), and 85.4% showed signs of other types of self-harm (girls more often (p < 0.001)). Most of them chose to cut for suicide attempts (86.0% girls, 56.2% boys). In 52.8% of cases, the attempt was spontaneous and 34% relapsed. Thirty-four events occurred in March (18.9%) (p = 0.688). Conclusions: Our study revealed the possible circumstances related to adolescent suicide attempt. Female gender was more common overall and males were more likely to carry out more potentially lethal methods. Other common associated factors were a lack of prosperity in family life, experience of bullying, violence, comorbidities, and the early spring period. Signs of any self-harm could be an indicator of later suicide attempt.

Ultrasonic nondestructive testing is widely used not only in the laboratory, but also in industry. The tests use various types of ultrasonic waves, diverse measurement techniques and different apparatus. One of the problems encountered is the high susceptibility of the surface wave to interference. Some of the interference is random in nature and can be minimized (e.g., contamination of the surface, resting a finger on the surface under study). Some of the interference is permanent in nature, such as variable surface roughness. The aim of the conducted research was to evaluate the influence of roughness on ultrasonic wave propagation. The study used samples with surface roughness Sa from 0.28 to 219µm, and ultrasonic surface wave probes with frequencies from 1.41 to 8.02 MHz. It was observed that roughness significantly affects the attenuation of the ultrasonic wave, and the differences in signal amplification reached more than 15dB. Similarly, the effect of the ultrasonic wave's transit time through surfaces of different roughness was noted. It was found that the difference in the ultrasonic wave transition time was more than 50 µs. The results of the study can be helpful, for ultrasonic testing of materials with different surface conditions.

There are some limitations in using the Body Condition Score (BCS) for client education to prevent obesity, hyperlipidemia-related diseases, and orthopedic diseases in dogs because it is hard to quantify in detail. In small dogs, patellar luxation is a common orthopedic disease that is related to obesity and hind leg muscle. In this study, we evaluated the thorax/thigh circumference ratio as a prognostic evaluation index, along with the BCS, for assessing patellar dislocation and other hyperlipidemia-related diseases. I analyzed eleven client-owned dogs that visited Bundang New York Animal Hospital, South Korea. According to the results, triglycerides (TGs) showed a negative correlation with thorax/thigh value (R = -0.585, p-value = 0.059) and a strong positive correlation with thigh circumference (R = 0.749, p-value = 0.008). Total cholesterol (TC) showed a strong positive correlation with thigh circumference (R = 0.776, p-value = 0.005), whereas the thorax/thigh value showed a negative correlation with the medial patella luxation (MPL) grade with low significance (R = -0.343, p-value = 0.302). These data indicate that thigh circumference can be an excellent negative indicator for hyperlipidemia and thorax/thigh value shows a low correlation with medial patella luxation, which encompasses other factors including varus and trochlear groove, despite the limitations of this study due to the small sample size.

Real time condition monitoring and precision health assessment system is a mandatory need for effective maintenance program in industrial sector. Rapid advancement in the information technology and other engineering technologies have invited more proactive attention from research and development in industrial sectors and particularly in condition monitoring of machines and related Industrial processes. In this work, drill bit condition monitoring techniques have been developed based on wavelet analysis and artificial neural networks (ANN) for automatic drill bit fault detection and classification. An experimental work has been conducted to capture the vibration signals for analysis. In this experiment, the CNC drill machine is used with high carbon steel drill bit and mild steel material as work piece. The cutting condition parameters are kept constant and the wear level is varied from 0.2 to 0.6 mm. The Data Acquisition system (DAQ) with Lab VIEW software is used to capture the vibration signals for drill bit with different wear conditions using accelerometer. The captured vibrations data are analyzed using continuous wavelet transform (CWT) with Morlet wavelet and Daubechies wavelet as a prime function. In general, the CWT coefficient is used to generate the inputs features to ANN for automatic tool condition classification, with two outputs (0, 1) for healthy and (1, 0) for faulty. The outcome of ANN showed 98% of accuracy in the wear prediction and this results show the effectiveness of the combed WT and ANN for automatic classification of tool wear conditions with high success rate.

Studies have demonstrated the critical role that safety plays in preserving favourable working conditions in the construction industry, which is necessary to accomplish goals. The aim of this research was to inform stakeholders in the construction industry in developing nations about the value of safety and possible strategies for influencing their opinions regarding safety protocols. The importance of safety to the construction sector, which is crucial to the advancement of the country, has also been emphasized. However, due to a lack of adequate safety understanding among stakeholders in the construction sector, the construction industry is characterized by a great deal of instability and hazard. To determine what factors, affect productive working conditions in the construction production, this study examined safety. A conceptual framework for safe working conditions in the construction sector was developed by considering several aspects, including the person dimension, environment factor, safety behaviour, organization features, technology, and incentives. It is now imperative to bring in these dimensions to improve the safe working conditions in the construction industry, particularly in the developing countries. By putting these safety factors into practice, the construction industry can reduce safety risks, lower the number of accidents and fatalities, cut expenses related to subpar safety performance, safeguard the reputation of construction companies, boost employee morale and satisfaction with their work, enhanced employee retention, reduced absenteeism, and enhanced sustainability goals. Additionally, the proposed conceptual framework is assured to be new and to be widely accepted in the developing nations. Based on this assumption the conceptual framework is designed.

This research focuses on the Hopf bifurcation occurring at a singular point with high-order degeneracy in a class of three-dimensional systems. Based on center manifold theorem, by expanding the technique for determining singular point values of degenerate singularities in two-dimensional systems, the formal series method to determine the singular point values at the high-order degenerate critical point is discussed. Furthermore, a class of specific three-dimensional differential systems with high-order degenerate singularities is explored. By computing the singular point values and determining center conditions, we proved that a minimum of 5 small-amplitude limit cycles in the vicinity of the origin. This research provides new perspectives on the center problem and limit cycle bifurcation in high-dimensional systems with high-order degenerate singular points.

In order to improve the yield of the therapeutic agent N-methylsansalvamide (SA), optimization of stationary liquid fermentation conditions was carried out on an endophytic strain, Fusarium sp. R1, at flask level. Using a One-Factor-At-a-Time approach, the fermentation conditions for SA production were determined as follows: fermentation time of 13 d, 24 oC, initial pH of 6.5, seed age of 24 h, inoculum size of 5.0% (v/v), loading volume of 50% (v/v), 20.0 g/L salinity, and sucrose, tryptone, and yeast extract were found to be the best sources of carbon and nitrogen. Using response surface methodology, the optimal medium compositions consist of 22.5 g/L sucrose, 16.5 g/L tryptone, and 0.024 g/L yeast extract. Verification tests suggested that the SA yield under these optimal conditions reached up to 536.77 ± 2.67 mg/L, which was increased by almost ten times the initial yield (54.05 ± 3.45 mg/L). The findings indicate that a high SA production yield can be achieved by static culture of strain R1 under the proper fermentation conditions using a low-cost medium. This study paves the way towards industrial-scale SA production by strain R1 for new drug development.

Disease detection and prediction have been one of the most frequent uses of machine learning models in the last decade, and Parkinson's disease is no exception. It is common for people in their old age to have one form of disease or another depending on the circumstances in which they find themselves. This study analyzed and evaluated various supervised machine learning models to detect the presence of Parkinson's disease in elderly individuals. An extreme gradient boosting model, along with other models, such as naïve Bayes, random forest, K-nearest neighbor, and support vector machine (SVM), was implemented to determine the best outcome. The extreme gradient boosting (XGBoost) model is a powerful boosting machine learning technique, particularly in terms of speed and accuracy. The results showed that the XGBoost model, which is an optimized version of the other boosting models, is capable of projecting the presence of Parkinson's disease with very high accuracy compared to other algorithms. The implemented classification algorithm extreme gradient boosting method achieved approximately 98 % accuracy, 97.5 % precision, 100.0 % recall, and 98.7% f1-score which are high compared with the results found in the existing literature.

Spatio-temporal variability and dynamics in Sahelian agro-pastoral zones make each local situation a special case. These specificities must be considered to guide the dissemination of agricultural options with a view to sustainable development. The territorial scale of municipalities is not sufficient for this necessary contextualization; the scale of the “village terroir” seems to be a better option. This is the hypothesis put forward by the Global Collaboration for Resilient Food Systems program. By analyzing the links between farm managers and their cultivated land, as well as the spatio-temporal dynamics of “village terroirs” in three regions of Niger (Maradi, Dosso and Tillabéri), this study provides evidence of the existence and functional usefulness of the village terroir for farmers, their land management and their activities. It demonstrates the usefulness of contextualizing agricultural options at this scale. It is based on data collected through participatory mapping and surveys. Their analysis elucidates the links between “terroirs village” and the specific functioning of the agro-socio-ecosystems acting on each of them, thus laying the systemic and geographical foundations for a model of the spatio-temporal dynamics of “village terroirs”. This initial work has opened up new perspectives in modeling and sustainable development.

The purpose of this study is to identify the different effects exerted by planting density and nitrogen fertilization on high-salinity Reclaimed Paddy Fields(RPF) and General Paddy Fields(GPF), and to find the amount of fertilization and the planting density suitable for the growth of forage rice in each paddy field. In both experimental locations, there was no significant change in the soil due to N treatment, but in the case of RPF, electrical conductivity(EC) decreased significantly from more than 5dS/m to up to 2.87dS/m during the yellow ripe stage due to the influence of floods and concentrated precipitation on the fields. In all soils, as both the amount of N treatment and the planting density increased, there was a proportional relationship in which the number of tillers and the dry weight also increased, with the occurrence of lodging also being increased. The dry weight, as expected, was 1.5 times higher at a planting distance of 10 cm, rather than 16 cm. In addition, in both locations N treatment led to an increase in the dry weight, but when N treatment reached 400kg/ha (2.0), the dry weight decreased instead. Moreover, although there was no clear difference in feed value according to N treatment, in RPF, neutral detergent fiber(NDF) was higher than 60%, RFV was less than 98, and TDN was also low, confirming that the quality of rice was higher in GPF.

The aim of the study was to assess the adaptability of Polish Red cows to extensive housing and feeding conditions. The study was carried out using 22 cows together with their offspring, kept on a certified organic family farm, from 15 October 2017 – the start of the winter feeding season – to 30 October 2018 – the end of the pasture season. The animals were kept as beef suckler cows, which stayed in the pasture during the summer and were kept in buildings with permanent access to a large cattle run in winter. The following were determined in the study: changes in the cows’ weight and body condition, daily gains in calves, 210-day standardized body weight, and estimated milk yield of suckler cows. To evaluate the cows’ health, blood was collected from them twice and analysed for haematological and biochemical parameters. The results indicated that during the study period the Polish Red cows had adequate body condition, normal vital parameters, and a normal course and development of pregnancy despite the sparse winter diet. This definitively confirms the breed’s high capacity to adapt to an extensive system.

Bacteria a Microscopic organisms are the most inexhaustible and flexible of microorganisms and constitute a huge division of the whole living earthly biomass, certain microorganisms were found to amass metallic components at a high limit Was Known as Bacterial Bio-sorption Due to their little size, capacity to become under controlled conditions, and their Accommodation to an extensive variety of ecological situations; Potent metal bio-sorbents among microorganisms, at low pH esteems, cell divider ligands are protonated and contend essentially with metals for official. With expanding pH, more ligands, such as amino and carboxyl groups, could be exposed, leading to attraction between these negative charges and the metals, and consequently increment bio-sorption onto the cell surface. Starting with Isolation and identification of heavy metal-resistant bacteria from rock Ore. Studying Factors Affecting Uranium Bio-sorption, Optimization of bacterial growth conditions and optimum for metal uptake by free and immobilized bacterial cells and Desorption ratio of uranium ions adsorbed by Coli. /alginate, All this evidence suggest that functions groups Represented in our study are responsible for metal uptake in our bacterial biomass beside change in peaks position which assigned for it's groups confirm bio-sorption of metal ions from waste due to ions charge interaction comparing with immobilized we found increase in no of binding sites indicate that immobilized bacterial have high efficiency for metal up take which also change in peaks position which assigned for its groups confirm bio-sorption of metal ions from waste due to ions charge interaction, Where the high bio-sorption yield obtained by bacteria, the Uranium & heavy metal bioremediation process expects microorganisms to be joined to a strong surface.

For the solution existence condition of the Navier-Stokes equation, we propose a conjecture as follows: "\emph{The Navier-Stokes equation has a solution if and only if the determinant of flow velocity gradient is not zero, namely $\det (\bm \nabla \bm v)\neq 0$.}"

The material derivative is important in continuum physics. This Letter shows that the expression $\frac{d }{dt}=\frac{\partial }{\partial t}+(\bm v\cdot \bm \nabla)$, used in most literature and textbooks, is incorrect. The correct expression $ \frac{d (:)}{dt}=\frac{\partial }{\partial t}(:)+\bm v\cdot [\bm \nabla (:)]$ is formulated. The solution existence condition of Navier-Stokes equation has been proposed from its form-solution, the conclusion is that "\emph{The Navier-Stokes equation has a solution if and only if the determinant of flow velocity gradient is not zero, namely $\det (\bm \nabla \bm v)\neq 0$.}"

The growing shortage of human resources to manage landscape in the water environment has led to problems concerning preservation of the landscape in Japan. To cultivate people with awareness about preserving the landscape, it is important to understand the process of how a region’s residents develop an attachment toward the water environment in their area. This paper identified factors influencing the attachment of residents toward the Fukushima-gata lagoon, Niigata prefecture, Japan, as a case study while focusing on changes in the lifestyle of the regional residents. The findings are as follows: (1) The target households frequently used Fukushima-gata lagoon for “fishing and for picking edible wild plants” and “gathering reeds to make thatched roofs” before “the reclamation project was carried out by the government in Fukushima-gata lagoon.” They also frequented Fukushima-gata lagoon for “walking and cherry blossom viewing” after “the reclamation project” was implemented. (2) Even the fact that distance from the lagoon to their place of residence increased after the project did not lose their attachment to the lagoon. (3) It was suggested that the project contributed to increase the attachment of regional residents towards Fukushima-gata lagoon because of multiple factors such as “playing in the water,” “livelihood opportunity,” “recreational use,” and so on. (4) Changes in the attachment of regional residents toward Fukushima-gata lagoon before and after the reclamation project was carried out by the government corresponded to “changes in the lifestyle of the local residents” before and after the project.

Tree condition, pruning and orchard management practices within intensive horticultural tree crop systems can be determined via measurements of tree structure. Multi-spectral imagery acquired from an unmanned aerial system (UAS) has been demonstrated as an accurate and efficient platform for measuring various tree structural attributes, but research in complex horticultural environments has been limited. This research established a methodology for accurately estimating tree crown height, extent, plant projective cover (PPC) and condition of avocado tree crops, from a UAS platform. Individual tree crowns were delineated using object-based image analysis. In comparison to field measured canopy heights, an image-derived canopy height model provided a coefficient of determination (R2) of 0.65 and relative root mean squared error of 6%. Tree crown length perpendicular to the hedgerow was accurately mapped. PPC was measured using spectral and textural image information and produced an R2 value of 0.62 against field data. A random forest classifier was applied to assign tree condition into four categories in accordance with industry standards, producing out-of-bag accuracies >96%. Our results demonstrate the potential of UAS-based mapping for the provision of information to support the horticulture industry and facilitate orchard-based assessment and management.

For offshore wind power generation, accessibility is one of the main factors that has great impact on operation and maintenance due to constraints on weather conditions for marine transportation. This paper presents a framework to explore the accessibility of an offshore site. At first, several maintenance types are defined and taken into account. Next, a data visualization procedure is introduced to provide an insight into the distribution of access periods over time. Then, a rigorous mathematical method based on finite state Markov chain is proposed to assess the accessibility of an offshore site from the maintenance perspective. A five-year weather data of a marine site is used to demonstrate the applicability and the outcomes of the proposed method. The main findings show that the proposed framework is effective in investigating the accessibility for different time scales and is able to catch the patterns of the distribution of the access periods. Moreover, based on the developed Markov chain, the average waiting time for a certain access period can be estimated. With more information on the maintenance of an offshore wind farm, the expected production loss due to time delay can be calculated.

The good management of the building maintenance should be based on the regular maintenance work. Repair works are often not carried out or there are shifts in time, which causes degradation of the building. The article presents a method of planning renovation works of a MUBAR (maintenance Management in an Un-renovated Building with Abandoning Renovation) based on the consequences in case of their lack. No renovation work on individual elements in a building can cause a threat to the stability of the building structure, risking the lives of users, continuing damage to the building by damaging further elements, or even a building disaster. The method can also be applied to historic buildings. Often, due to abandonment of repairs caused most often by lack of the owner, improper manager or irresponsible users, degradation of these objects occurs. The consequences of neglected renovation of buildings lead to irreversible processes of their destruction. The aim of the article is to present the developed method of planning renovation works based on the consequences of lack of renovation works.

Heating stage accounts for the largest proportion of building carbon emissions. In order to figure out heating situations of rural residential building in Gansu Province, Gannan region was selected to carry out on-site survey on heating condition including heating mode, energy used for heating, heating fees, residents’ satisfaction on heating and thermal environment of typical building. The results showed that local rural residents burnt scattered coal for heating with primitive heating stove with high heating cost and low efficiency, causing low air temperature and high heating fees. Besides carbon emissions caused by heating reached 5743.28 kgCO2em-2, Several strategies for improving the heating effect and reducing carbon were proposed taking economic benefits into consideration limited by rural economic development. A parameter of reduced carbon emissions per investment input was proposed to evaluate the carbon reduction strategies. Results showed that Biomass was the most economical way to reduce the carbon emissions, followed by thermal insulation design, natural gas furnace and air source heat pumps for heating, and it was not reasonable to blindly increase building insulation to reduce building carbon emissions. The optimum energy efficiency was 55% in Gannan. The results provided reference for building low-carbon design in rural areas, helped achieving low carbon goal with low investment.

Amidst worsening climate change, drought stress imperils global agriculture, jeopardizing crop yields and food security, necessitating urgent exploration of sustainable methods like biopriming, harnessing beneficial microorganisms to bolster plant resilience. Recent research has revealed diverse biological compounds with versatile applications produced by Schizophyllum commune, rendering this fungus as a promising contender for biopriming applications. For the first time this study aimed to investigate,the potential of S. commune exo- (EPSH) and intra-PSH (IPSH), isolated from two strains: Italian (ITA) and Serbian (SRB) under submerged cultivation, in enhancing the resilience of Pisum sativum L. seeds through biopriming technique. Testing of seed quality of bioprimed, hydroprimed, and unprimed seeds was done using a germination test, under optimal and drought conditions. In optimal conditions, the IPSH SRB increased germination energy by 5.50% compared to the control, while the highest percentage of germination (94.70%) was shown after biopriming with PSH isolated from the ITA strain. Additionally, all assessed treatments resulted in a boost in seedling growth and biomass accumulation, where the ITA strain demonstrated greater effectiveness in optimal conditions, whereas the SRB strain showed superiority in drought conditions. The drought tolerance indices increased significantly in response to all examined treatments during the drought, with EPSH ITA (23.00%) and EPSH SRB (24.00%) demonstrating the greatest effects. Results demonstrate a significant positive influence of fungal PSH, indicating their potential as biopriming agents and offers insights into novel strategies for agricultural resilience in the face of changing environmental conditions.

This paper aims at achieving optimal rotor speed estimation for an induction motor using the Extended Kalman filter (EKF). Speed estimation is essential for fault diagnosis in Motor Current Signature Analysis (MCSA). The estimation accuracy is obtained by exploring the noise covariance matrices estimation of the EKF algorithm. The noise covariance matrices are determined using a modified subspace model identification approach. In order to reach this goal, this method compares an estimated model of a deterministic system, derived from available input-output datasets, with the discrete-time state-space representation used in the Kalman filter equations. This comparison leads to the determination of model uncertainties, which are subsequently represented as noise covariance matrices. Based on the 5th order nonlinear model of the induction motor, rotor speed is estimated with the optimized EKF algorithm and the used algorithm is tested experimentally.

Two-dimensional heat diffusion problem with heat source which is quasilinear parabolic problem is examined analytically and numerically. Periodic boundary conditions are used as a boundary conditions. Since the problem is not linear, Picard’s successive approximation theorem is used. Under certain conditions of natural regularity and consistency imposed on the input data, establish the existence, uniqueness and constant dependence of the solution on the data using the generalized Fourier method. As a numerical solution, implicit finite difference scheme is used. The results ob-tained from analytical and the numerical solutions are so close to each other.

The rapid growth of the aviation industry has progressively heightened the importance of airport maintenance management. There is an increasing need for a comprehensive system that considers the diverse requirements of various stakeholders. Existing research has primarily focused on the technological advancements of optimization models, with limited applicability in practice. In this study, we introduce the Condition Cases Optimization (CCO) to address these limitations while incorporating multi-facility and multi-year network optimization models. We developed condition index, Serviceability Level, integrated assessment indices and performance models for decision-making criteria. We categorize conditions into three classes and propose a practical decision-making strategy that can flexibly adapt to budget constraints. Sensitivity analysis highlights the impact of initial budget, maintenance methods, costs, and thresholds on decision outcomes. Through a case study we validate the effectiveness and practicality of the CCO method as an efficient decision-making tool.

Background: Post-Covid condition can reduce activity and quality of life, resulting in a significant socioeconomic and health burden. Understanding its impact on patients' health is important for the development of personalized rehabilitation interventions. An independent association between obesity and post-covid condition was found because of complications and comorbidities. Methods: Sixteen patients with obesity and post-COVID symptoms (i.e., dyspnea, pain, poor sleep quality, muscle fatigue) admitted to the Istituto Auxologico Italiano, Piancavallo (VB), Italy, were recruited for a four-week rehabilitation program including conventional exercise therapy, nutritional intervention, psychological support and whole-body cryostimulation (WBC). Results: All participants attended all sessions of the program. Anthropometric data showed statistically significant changes in weight, waist circumference and body mass index. Biochemical analyses showed significant reductions in lipid and inflammatory profiles. There was a significant improvement in physical performance, reduction in pain and improvement in psychological well-being. Conclusion: A multidisciplinary rehabilitation protocol including WBC designed for patients with obesity and post-covid condition is safe and feasible. The overall improvements demonstrate that multidisciplinary rehabilitation was effective on post COVID patients and suggest that the use of WBC is safe and could play a role as a booster in rehabilitation programs.

Third-generation biomass production utilizing microalgae exhibits sustainable and environmentally friendly attributes, along with significant potential as a source of physiologically active compounds. However, the process of screening and localizing strains that are capable of producing high-value-added substances necessitates a significant amount of effort. In the present study, we have successfully isolated the indigenous marine diatom Odontella aurita OAOSH22 from the East coast of Korea. Subsequently, we conducted a comprehensive analysis of its morphological, molecular, and biochemical characteristics. Afterwards, comprehensive analysis was conducted on the morphological, molecular, and biochemical characteristics, along with testing for optimal growth conditions. The morphological characteristics of the isolate were observed using optical and electron microscopes, and it exhibited typical features of O. aurita. Additionally, the molecular phylogenetic inference derived from the sequence of the small-subunit 18S rDNA confirmed the classification of the microalgal strain as O. aurita. This isolate has been confirmed to contain 7.1 mg g-1 dry cell weight (DCW) of fucoxanthin, a powerful antioxidant substance, in terms of dry cell weight (DCW). In addition, this isolate contains 11.1 mg g-1 DCW of eicosapentaenoic acid (EPA), which is one of the nutritionally essential polyunsaturated fatty acids. Therefore, this indigenous isolate exhibits a significant potential as a valuable source of bioactive substances for various bio-industrial applications.

Compared with ordinary asphalt pavement, the application of modified asphalt will improve the performance of asphalt pavement. However, using the same modification method for the whole road section can cause modification waste or result in some parts not meeting performance standards. This paper proposes a whole road section modification method. First of all, a finite element model was established using Abqus according to the actual road pavement material to analyze the mechanical response of each layer under different working conditions and establish the grading standard under comprehensive working conditions. Secondly, different modification methods were analyzed according to investigation and their modification effects were graded through clustering analysis method. Finally, different working conditions and different modification methods were reasonably matched and a section of highway was selected for verification. The results show that before modification, the calculated rutting is 6.23mm and after modification, it is reduced to 2.45mm which indicates that reasonable modification has a better effect and can achieve the purpose of rutting equalization control on the whole road section. It is of great significance to carry out reasonable modification on different conditions to control rutting equalization of asphalt pavements.

Organ transplantation remains the only treatment option for patients with end-stage organ dysfunction. However, there are numerous limitations that challenge its clinical application, including the shortage of organ donations, the quality of donated organs, injury during organ preservation and reperfusion, primary and chronic graft dysfunction, acute and chronic rejection, infection, and carcinogenesis in post-transplantation patients. Acute and chronic inflammation and cell death are two major underlying mechanisms for graft injury. Necroptosis is a type of programmed cell death involved in many diseases and has been studied in the setting of all major solid organ transplants, including the kidney, heart, liver, and lung. It is determined by the underlying donor organ conditions (e.g., age, alcohol consumption, fatty liver, hemorrhage shock, donation after circulatory death, etc.), preservation conditions and reperfusion, as well as allograft rejection. Specific molecular mechanisms of necroptosis have been uncovered in the organ transplantation setting, and potential targeting drugs have been identified. We hope this review article will promote more clinical research to determine the role of necroptosis and other types of programmed cell death in solid organ transplantation to alleviate the clinical burden of ischemia-reperfusion injury and graft rejection.

Electrical rotating machines like Induction Motors (IMs) are widely used in several industrial applications since their robust elements, provide high efficiency and give versatility in industrial applications. Nevertheless, the occurrence of faults in IMs is inherent to their operating conditions, hence, Inter-turn short-circuit (ITSC) is one of the most common failures that affect IMs and its appearance is due to electrical stresses leads to the degradation of the stator winding insulation. In this regard, this work proposes a diagnosis methodology for the assessment and detection of incipient ITSC in IMs, the proposed method is based on the processing of vibration, stator currents and magnetic stray-flux signals. Certainly, the novelty and contribution include the characterization of different physical magnitudes by estimating a set of statistical time domain features, as well as, their fusion and reduction through the Linear discriminant Analysis technique within a feature-level fusion approach. Furthermore, the fusion and reduction of information from different physical magnitudes leads to perform the automatic fault detection and identification by a simple Neural-Network (NN) structure. The proposed method is evaluated under a complete set of experimental data and the obtained results demonstrate that the fusion of information from different sources (physical magnitudes) allows to improve the accuracy during the detection of ITSC in IMs , the results make this proposal feasible to be incorporated as a part of condition-based maintenance programs in the industry.

Leptospirosis remains an important worldwide zoonotic disease caused by Leptospira spp affecting human and animals. This research aims to study the virulent-associated secreted proteins (protein secretome) of pathogenic Leptospira interrogans serovar Icterohemorrhagiae strain RGA (Leptospira RGA) transition from the environment to mammalian physiological osmolarity, temperature (37 °C) and carbon dioxide concentration (5% CO2) conditions for 24 h. Mass Spectrometry and bioinformatics approaches, we identified 69 potential secreted proteins from the culture supernatant of the Leptospira RGA isolate. We discovered transporters and porins such as phosphate porin, outer membrane efflux, ompA family protein, and polymer-forming cytoskeletal family protein under hyperosmotic condition. Under heat stress, degradation enzymes included zinc metallopeptidase, M23 family (LA3456, LA0709), Rhs family protein (LA1765), thermolysin metallopeptidase; / hydrolase family (LA1345, LA2501). Oxidative stress response proteins induced by osmolarity and temperature shifts included chaperon GrpE, DnaK (LA3705), antioxidants, i.e., thiol-specific redoxin, and peroxiredoxin (LA2809). In response to the in vivo transition, metabolic and other enzymes involved in energy production (COG:C), amino acid metabolism and transport (COG:E), and lipid metabolism and transport (COG:I), as well as moonlighting proteins functionally binding to plasminogen and fibronectin and regulating transcription, were also discovered. An overview of secreted proteins will supplement our understanding of Leptospira biology and pathogenesis during infection and also in response to environmental stimuli and their potential virulent determinants have the potential for developing leptospirosis vaccines and diagnosis.

Autonomic dysfunction is an increasingly recognised complication in chronic neurological conditions such as Parkinson’s disease , and other medical conditions, including diabetes mellitus, chronic fatigue syndrome, postural tachycardia syndrome with and without Ehlers-Danlos syndrome, fibromyalgia and recently Long Covid. Despite laboratory-based tests to evaluate normal and abnormal autonomic function, there are no home-based tests to record neuro-cardiovascular autonomic responses to common stimuli in daily life that are dependent on the normal functioning of the autonomic nervous system. We have developed an adapted blood pressure/heart Autonomic Profile (aAP) that can be used by an individual independently and repeatedly in a domiciliary setting to determine the physiological and symptomatic response to standing, food, and physical and mental (cognitive, emotional) activities. The aAP aids separating autonomic failure (often irreversible) from autonomic dysfunction. This helps the individual and attending healthcare professional understand the relationship between symptoms and common triggers in daily life and informs on self-management in debilitating conditions such as the postural tachycardia syndrome (PoTS) and Long Covid.

Worldwide, millions of kilometres of sewers are constructed from concrete pipes. Unfortunately, concrete sewers are susceptible to corrosion from biogenic hydrogen sulphide, and though they may pass visual inspection, their ability to hold together under load may be degraded. This paper presents the design of a teleoperated robot with a protractible probe, that allows an operator to apply a localised load to selected points within a concrete sewer pipe. We report findings from laboratory and field trials of our prototype, with initial results suggesting that this approach has the potential to contribute useful information to sewer maintenance planning.

Trigonella foenum is one of the oldest medicinal plants that grow in many parts of Iran with the diverse ecological situation. Employing this plant for treating diabetes and high cholesterol has a long history, because of some metabolites. Due to the habitat of fenugreek is a wide range of climatic conditions, it may have power to cope with climate variation. The main intention of this inquiry was to understand the effect of the environmental variables on this therapeutic plant features. It was also interesting for us to understand which environment variables are more impressive for enhancing of trigonelline and 4-hydroxy isoleucine content as the most important metabolites of this plant. For achieving this goal, environmental information and vegetal data were analyzed to discover the role of nature on the seed part of fenugreek life in 50 different regions of Iran. Canonical correspondence analysis (CCA) displayed that high content of metabolites and some morphological characteristics happened in high temperature and solar irradiation. Partial least squares regression (PLSR) and path analysis used to find the best predictors and direct and indirect effect of all variables on 4-hydroxy isoleucine and trigonelline. Ecological condition were the best predictors and had the highest direct and indirect impact on 4-hydroxy isoleucine. However, for trigonelline, the environment did not play a senior role. It seems that the reaction of components of fenugreek does not follow the same way. Studying on morphological, primary and secondary metabolites, and surrounding environment of fenugreek, helped us to have a more precise judgment about the life of this plant.

Background: Post COVID-19 syndrome or Long Covid (LC) is a novel fluctuating condition with a protracted course in some patients. Specialist LC services have been operational in the UK since 2020 and deal with a high caseload of patients. Aims: To evaluate long-term outcomes in patients discharged from a community-based LC specialist service. Methods: A service evaluation study that included patients who were well engaged in the services [completed the standard Patient Reported Outcome Measures (PROMs) and received intervention from clinician(s)] and had been discharged for at least 3 months from the service. They consented to the study and completed standard PROMs: COVID-19 Yorkshire Rehabilitation Scale (C19-YRS), EQ-5D-5L, and National Institute for Health and Care Excellence (NICE) criteria for Myalgia Encephalomyelitis/ Chronic Fatigue Syndrome (ME/CFS). Results: Out of 460 patients contacted, 112 (average of 37.6 months since infection and 9.8 months post-discharge) completed the PROMs. 90.2% patients continued to experience LC symptoms and disability and had not returned to their pre-COVID health status. The average EQ-5D-5L index score was 0.53 (SD 0.29) highlighting a significant disability and that LC had become a long-term condition (LTC) in majority of patients who responded to the survey. 43% patients met the criteria for suspected ME/CFS. Conclusion: A proportion of LC patients develop Persistent Long Covid (PLC) consistent with a LTC and had a significant overlap with ME/CFS.

The study´s goals were to determine the health status of a group of heart transplant recipients (HTR) and their level of physical activity and to compare the health status among them and with a group of healthy sedentary individuals. Fifty-four HTR and eighteen S were assigned to four groups, according to their level of physical activity (determined with The International Physical Activity Questionnaire); patients with a low, a moderate and a high level of physical activity (HTRL, HTRM and HTRH, respectively) and sedentary individuals (S). Participants underwent a basic blood analysis and several tests to assess their cardiovascular, neuromuscular, functional mobility condition and their quality of life. The S and HTRH were very similar in terms of BP, HR and blood analysis while HTRM and HTRL differed from both S and HTRH in these parameters. Regarding the cardiovascular, neuromuscular, functional mobility, and quality of life variables assessed in this study, HTRH showed the best results across all of them, followed by S, HTRM, and HTRL. It is suggested that the weekly level of physical activity of HTR should be high, what might help them to enhance their health and quality of life.

A class of semi-linear elliptic equations with critical Hardy-Sobolev exponent has been considered. This model is widely used in hydrodynamics and glaciology, gas combustion in thermodynamics, quantum field theory and statistical mechanics, as well as gravity balance problems in galaxies. The $\left( PS\right) _{c}$ sequence of energy functional has been investigated, and then the mountain pass lemma was used to prove the existence of at least one nontrivial solution. Also a multiplicity result has been obtained. Some known results have been generalized.

The fractional reduced differential transform method is a finite iterative method based on infinite fractional expansions. The result obtained is the approximation of the real value. There are few reports on the approximate error and the applicable condition. In this paper, according to the fractional expansions, we study the factors related to the approximate errors. Our research shows that the approximate errors relate not only to fractional order but also to time $t$ and increase rapidly with time $t$. This method can only be applied within a certain time range and the time range is relevant to fractional order and fractional expansions. Then, many obtained achievements may be incorrect if the applicable conditions are not satisfied. Some examples presented in this paper verify our analysis.

Permeability is a fundamental property of porous media, especially when studying resin transfer within a dense fibrous medium to manufacture composite materials. When the measurement of permeability tensor of composite reinforcements is difficult and time consuming, especially in thick, heterogeneous fibrous structures, a numerical approach based on the calculation of the tensor components on a 3D image of the material can be very advantageous. A new finite element based method proposed in this study allows solving very high-dimensional flow problems while limiting the biases associated with boundary conditions and the small size of the numerical samples addressed. The method is validated against academic test cases and against the results of a recent permeability benchmark exercise. The results emphasize that for heterogeneous and anisotropic microstructures, it is important to perform calculations on large samples using boundary conditions that do not suppress the transverse flows that occur when flow is forced out of the principal directions. Since these are not necessarily known in complex media, the permeability determination method must not introduce bias by generating non-physical flows.

In this paper, a class of semi-linear elliptic equations involving Hardy-Sobolev critical exponents has been investigated. This problem comes from the consideration of standing waves in the anisotropic Schr\"{o}dinger equation, also it is very important in the field of hydrodynamics, glaciology, quantum field theory and statistical mechanics. By a detailed estimation for the extremum function and using Mountain Pass Lemma with $\left( PS\right) _{c}$ conditions, the existence of positive solutions has been obtained. On the other hand, by establishing Pohozaev-type identity and using the properties of Bessel function, the nonexistence of positive solution also has been obtained. These results are extensions of E. Jannelli's research (\cite[Theorem 1.A-1.C]{EJ}).

This study presents a novel approach for automating the body condition scoring of dairy cows, leveraging advancements in 3D imaging technology and deep neural networks. The primary objective was to design and implement a system capable of accurately and efficiently assessing the body condition of dairy cows, a critical metric in livestock management for optimizing health and productivity. To achieve this, a 3D camera was employed to capture detailed point cloud data, reconstructing the three-dimensional morphology of individual cows. The obtained data were then fed into a deep neural network, specifically tailored for the task of ranking body condition. The neural network was trained on a diverse dataset of annotated body condition score representing varying degrees of body condition, ensuring robust performance across different physiological states. The results demonstrate the efficacy of the proposed system in automatically and objectively scoring the body condition of dairy cows. The automated process not only expedites the assessment but also reduces the subjectivity associated with manual scoring methods. This innovative approach holds promise for improving the efficiency of dairy farm management by providing timely and accurate body condition assessments. The integration of 3D imaging and deep learning techniques paves the way for future advancements in precision livestock farming, contributing to enhanced animal welfare, optimized production, and sustainable agriculture practices.

ccurate recording of growth indicators for aquaculture species at different stages is essential for evaluating aquaculture production effectiveness and the rationality of feed formulations. Due to their convenience and non-invasiveness, weight, length, and condition factor are commonly used to assess the growth of aquaculture species . However, fish growth indicators, can vary significantly with age structure and feed components(nutrition), and relying on a single indicator may lead to misjudgments . In this study we investigated the growth indicators of gibel carp (Carassius auratus gibelio var. CAS V) at different growth stages (juvenile and adult) and different feed formulations. Meanwhile, fish weight-length relationship () was used to assess the growth indicators. The results showed that the weight-length relationship of gibel carp varied significantly with age and feed formulations. Additionally, the condition factor calculated depended on the weight-length relationship was more consistent with weight/length changes than measured. Weight analysis indicated that both weight, length, height, back thickness, and carcass ration had higher weights in analyzing fish growth. Therefore, during aquaculture production, fish growth evaluating requires considering diverse indicators such as weight, length,body height, back thickness, carcass ration,as well as condition factor to avoid misjudging the actual growth situation. Meanwhile, the use of the condition factor should consider the sufficient amount of data and whether the assumptions (such as being in a isometric growth period) are met.

The accumulation of steel slag and other industrial solid wastes has caused serious environmental pollution and resource waste. The resource utilization of steel slag is imminent. In this paper, alkali activated materials ultra-high performance concrete (AAM-UHPC) was prepared by replacing GGBFS powder with different proportions of steel slag powder, and its workability, mechanical properties, curing condition, microstructure, and pore structure were investigated. The results illustrate that the incorporation of steel slag powder can significantly delay the setting time and improve the flowability of AAM-UHPC, making it possible for engineering applications. The mechanical properties of AAM-UHPC showed a tendency to increase and then decrease with the increase of steel slag dosing, and reached the best performance at 30% dosage of steel slag. The maximum compressive strength and flexural strength are 157.1 MPa and 16.32 MPa, respectively. High temperature steam or hot water curing at early age was beneficial to the strength development of AAM-UHPC, but continuous high temperature hot and humid curing would lead to strength inversion. When the dosage of steel slag is 30%, the average pore diameter of the matrix is only 8.43nm, and the appropriate steel slag dosage can reduce the heat of hydration and refine the pore size distribution, making the matrix more dense.

Acute SARS-CoV-2 infections in children and adolescents are usually mild. However, they can suffer from ongoing symptoms generally referred as long COVID. Sleep disorders are one of the most frequent complaints in long COVID although precise data are missing. We assessed the sleep behavior of children and adolescents who presented at our outpatient clinic between January 2021 and May 2022 with the Children's Sleep Habits Questionnaire (CSHQ-DE). We compared sleep behavior at three different time points: pre-COVID-19, post-COVID-19 at initial presentation and post-COVID-19 at re-presentation. Data from 45 patients were analyzed. Of those, 64% were female and the median age was 10 years (range 0-18 years). Asymptomatic or mild COVID-19 disease was experienced in 89% of patients, whilst 11% experienced moderate disease. Initial presentation occurred at a median of 20.4 weeks (6 weeks - 14 months) after infection. The CSHQ-DE score increased significantly from pre-COVID-19 (45.82+8.7 points) to post-COVID-19 (49.40+8.3 points; p=&lt;0.01). The score then normalized at re-presentation (46.98+7.8; p=0.1). The greatest changes were seen in the CSHQ-DE subscale score "daytime sleepiness". Our data show that children and adolescents with long COVID often suffer from sleep disturbance. For most children and adolescents these sleep disorders decreased over time without further medical intervention, aside from a basic sleep consultation.

In this paper after introducing a model of binary data matrix (BDM) for physical parameters of an evolving system (of particles), we develop a Hilbert space as an ambient space to derive induced metric tensor on embedded parametric manifold identified by associated joint probabilities of particles observables (parameters). Parameter manifold assumed as space-like hypersurface evolving along time axis, an approach that resembles 3+1 formalism of ADM and numerical relativity. We show the relation of endowed metric with related density matrix. Identification of system density matrix by this metric tensor, leads to the equivalence of quantum Liouville equation and metric compatibility condition while covariant derivative of metric tensor has been calculated respect to Wick rotated time or spatial coordinates. After deriving a formula for expected energy per particles, we prove the equality of this expected energy with local scalar curvature of related manifold. We show the compatibility of BDM model with Hamilton-Jacobi formalism and canonical forms. On the basis of the model, I derive the Ricci flow like dynamics as the governing dynamics and subsequently derive the action of BDM model and Einstein field equations. Given examples clarify the compatibility of the results with well-known principles such as equipartition energy principle and Landauer’s principle. This model provides a background for geometrization of quantum mechanics compatible with curved manifolds and information geometry. Finally, we conclude a “bit density principle” which predicts the Planck equation, De Broglie wave particle relation, , Beckenstein bound and Bremermann limit.

Corynebacterium pseudotuberculosis is a Gram-positive bacterium that causes caseous lymphadenitis, a disease that predominantly affects sheep, goat, cattle, buffalo, and horses, but has also been recognized in other animals. This bacterium generates a severe economic impact on countries producing meat. Gene expression studies using RNA-seq is one of the most commonly used techniques to perform transcriptional experiments. Computational analysis on such data through reverse-engineering algorithms leads to a better understanding of the genome-wide complexity of gene interactomes, enabling the identification of genes having the most significant functions inferred by the activated stress response pathways. In this study, we identified the influential or causal genes from four RNA-seq data-sets from different stress conditions (high iron, low iron, acid, osmosis, and PH) in C. pseudotuberculosis, using a consensus-based network inference algorithm called miRsig and identified the causal genes in the network using the miRinfluence tool, which is based on the influence diffusion model. We found that over 50\% of the genes identified as influential have some essential cellular functions in the genomes. In the strains analyzed, most of the causal genes have crucial roles or participate in processes associated with response to extracellular stresses, pathogenicity, membrane components, and essential genes. This research brings new insight into the understanding of virulence and infection by C. pseudotuberculosis.

Riparian restoration is an important objective for landscape managers seeking to redress the widespread degradation of riparian areas and the ecosystem services they provide. This study investigated the long-term outcomes of ‘one-off’ restoration activities undertaken in the Upper Murrumbidgee Catchment, NSW, Australia. The objective of the restoration was to protect and enhance riparian vegetation and control erosion, and consequently reduce sediment and nutrient delivery into the Murrumbidgee River. To evaluate the outcomes 10 years after restoration, rapid riparian vegetation and geomorphological assessments were undertaken at 29 sites spanning the four different restoration methods used (at least five replicates per treatment), as well as at nine comparable untreated sites. We also trialed the use of aerial imagery to compare width of riparian canopy vegetation and projective foliage cover prior to restoration with that observed after 10 years. Aerial imagery demonstrated the width of riparian canopy vegetation and projective foliage cover increased in all restored sites, especially those with native plantings. The rapid assessment process indicated that 10 years after riparian restoration, the riparian vegetation was in a better condition at treated sites compared to untreated sites. Width of riparian canopy vegetation, native mid-storey cover, native canopy cover and seedling recruitment were significantly greater in treated sites compared to untreated sites. Geomorphological condition of treated sites was significantly better than untreated sites, demonstrating the importance of livestock exclusion to improve bank and channel condition. Our findings illustrate the value of ‘one-off’ restoration activities in achieving long-term benefits for riparian health. We have demonstrated that rapid assessments of the vegetation and geomorphological condition can be undertaken post-hoc to determine the long-term outcomes, especially when supported with analysis of historical aerial imagery.

A real-time two-way direct load control (TWDLC) of central air-conditioning chillers in wide area is proposed to provide demand response. The proposed TWDLC scheme is designed to optimize the load shedding ratio of every customer under control to ensure the target load to be shed is met at every scheduling period. In order to overcome the load reduction uncertainties of TWDLC, an innovative solution is proposed by applying a certain degree of loosening on the constraint of the actual shed load. Fuzzy linear programming is utilized to solve the optimization problem with fuzzy constraints. The proposed fuzzy linear programming problem is solved by delicately transforming it into a regular liner programming problem. A selection scheme used to obtain the feasible candidates set for load shedding at every sampling interval of TWDLC is also designed along with the fuzzy linear programming.

In the industrial sector, intelligent sensors in fault diagnosis are becoming more critical in recent technological improvements. The prediction accuracy can be enhanced in fault diagnosis using 3-dimensional, sequential, real-time, and image data. Sensors that capture the vibration, sound, and image data are more critical in predicting unbalancing, tool wear, crack, misalignment, etc, in the rotating machinery to increase productivity and to provide an effective maintenance management system. Due to the fast development of industry 4.0 techniques, monitoring of mechanical machinery is experiencing explosive growth and getting more attention in the area of fault diagnosis (FD). Machine learning and Deep learning methods give promising results and accuracy in predicting faults in rotating machinery on shop floors. The success of AI-based models is due to the availability of comprehensive labeled data. Federated Learning (FL) is the machine learning subfield aiming to train an algorithm with a heterogeneous dataset. Data transmission from local facilities to a central server in AI models creates data privacy and security issues. Heterogeneous data analysis is a complicated process in predicting the machine fault in the central server because of millions of data during real-time condition monitoring. Decentralized data handling and analysis is mandatory in condition monitoring because of heterogeneity in data processing, data privacy, and security advantages. The application of federated learning in fault diagnosis has been getting more attention in recent days, and this study is a review of FL applications that address fault diagnosis in rotating machinery in the first phase. A comparison between the types of FL approaches in FD and the use of aggregation algorithms and their applications will also be discussed in Phase 1. In phase 2, a novel methodology has been proposed using Federated learning to diagnose rotating machinery faults. The proposed method, FLOACOS, addresses how prediction challenges are solved using federated learning approaches by optimizing and standardizing the data at local facilities. This work will be helpful for future condition monitoring researchers and gives an overview of a novel method of the FL technique used in predicting faults and the progress made in the maintenance management of rotating machinery.

This study investigates the influence of internal factors on growth dynamics in four Cyprinus carpio varieties: three Romanian strains (Frăsinet, Ineu, and Podu Iloaiei) and the ornamental Koi variety. The research aims to elucidate variety-specific growth patterns and their implications for aquaculture and ecological management. Fish were measured for total length, maximum height, and weight at four ontogenic stages: 7 days post-hatch, summer fingerlings (0⁺), one year and summer old (1⁺), and two years and summer old (2⁺). Weight Gain (WG), Specific Growth Rate (SGR), Relative Growth Rate (RGR), Fulton's condition factor, and profile index were calculated and analyzed. Results revealed significant intervariety differences in growth performance, body shape development, and physiological condition across life stages. The Romanian varieties showed superior growth performance at later stages, while Koi exhibited unique growth patterns. Fulton's condition factor and profile index varied markedly among varieties and life stages, indicating differential energy allocation and morphological adaptations. These findings highlight the complex interplay between genetic factors and developmental processes in carp varieties, providing valuable insights for selective breeding programs, aquaculture practices, and ecological management strategies. The study underscores the importance of variety-specific approaches in carp cultivation and conservation efforts.

Condition monitoring is a crucial process for ensuring industrial assets' reliability and operational efficiency. In the age of the digital industry, AI-based data-driven condition monitoring is proving extremely effective in detecting potential issues before they escalate into major problems, thereby reducing downtime, minimizing maintenance costs, and extending the lifespan of the equipment. The availability of tools that can enable the operationalization of these data-driven solutions is, therefore, critical. In this direction, this work proposes a comprehensive, modular, and scalable pipeline covering all the steps from the data acquisition to the AI model training and inference phases. The tool integrates the data acquisition and processing steps with a configurable feature extraction phase. Moreover, the system also integrates deep learning algorithms for diagnosis and prognosis, including a domain adaptation stage to permit transfer learning and increase generalizability. In addition, it features a communication system using MQTT, which allows for an online data stream to enable real-time monitoring and maintenance. The overall infrastructure was deployed in actual industrial settings and tested in a real-time experiment, demonstrating the proposed approach's validity.

This study focuses on a biogas dry reforming to produce H2. Biogas contains CO2 of 40 vol% approximately, indicating that the efficiency of the power generation is reduced because of the smaller heating value compared with a natural gas. The authors have proposed the combination system consisting of biogas dry reforming reactor and solid oxide fuel cell (SOFC). Since a biogas dry reforming is an endothermic reaction, this study adopts a membrane reactor which can be operated by causing the non-equilibrium state with H2 separation from the reaction space. This study aims to clarify the impact of thickness of Pd/Cu membrane on the characteristics of the biogas dry reforming membrane reactor using Pd/Cu membrane and Ni/Cr catalyst. The impact of the operation temperature, the molar ratio of CH4:CO2 and the differential pressure between the reaction chamber and the sweep chamber on the characteristics of the biogas dry reforming membrane reactor using Pd/Cu membrane and Ni/Cr catalyst is examined changing the thickness of Pd/Cu membrane. As a result, the highest concentration of H2 is 122711 ppmV which is obtained in case of CH4:CO2 =1:1 at the reaction temperature of 600 ℃ and the differential pressure of 0 MPa using the Pd/Cu membrane whose thickness of 40 m.

This study proposed the combination system consisting of biogas dry reforming reactor and solid oxide fuel cell (SOFC). Since a biogas dry reforming is an endothermic reaction, this study adopted a membrane reactor operated due to the non-equilibrium state with H2 separation from the reaction space. This study aimed to clarify the characteristics of Ni/Cr/Ru catalyst using for a biogas dry reforming membrane reactor. Additionally, this study also investigated the comparison of the characteristics of Ni/Cr/Ru catalyst with that of Ni/Cr catalyst. The impact of operation temperature, the molar ratio of CH4 : CO2, the differential pressure between the reaction chamber and the sweep chamber and the introduction of sweep gas on the characteristics of the biogas dry reforming membrane reactor using Pd/Cu membrane and Ni/Cr/Ru catalyst was examined. The concentration of H2 using Ni/Cr/Ru catalyst was larger than that using Ni/Cr catalyst by 2871 ppmV for the molar ratio of CH4 : CO2 = 1.5 : 1 at the reaction temperature of 600 ℃ and the differential pressure of 0 MPa without a sweep gas, especially. Under this condition, CH4 conversion, H2 yield and thermal efficiency were 67.4 %, 1.77×10-2 % and 0.241 %, respectively.

Lubricated tribosystems such as main-shaft bearings in gas turbines have been successfully diagnosed by oil sampling for many years. In practice, the interpretation of wear debris analysis results can pose a challenge due to the intricate structure of power transmission systems and the varying degrees of sensitivity among test methods. In this work, oil samples acquired from the fleet of M601T turboprop engines were tested with optical emission spectrometry. Two-way analysis of variance (ANOVA) with interaction analysis and post hoc tests were carried out on measurement data from the whole fleet of the M-601T turboprop engines to study the impact of aluminum and zinc concentration on iron concentration. Finally, the developed model was used to evaluate the oil testing results for two specific engines of this type. Thanks to ANOVA, the assessment of engine health is based on a statistically proven correlation between the values of the dependent variable and the classifying factors.

The COVID-19 pandemic has profoundly changed the world and human society in various ways. Infection-related figures are key indicators of the direct impact of the pandemic. In addition to the virus, many vulnerable populations (e.g. patients with complex health conditions) have suffered direct or indirect health impacts, such as delayed treatment or diagnosis of cancer and severe mental illness due to stay-at-home restrictions, or financial issues. Studying these kinds of impacts can provide the community with insights and understanding of how the pandemic has affected patients with health conditions and help inform decision-makers design policies that support various vulnerable populations. This paper surveys analytical work conducted on how COVID-19 has impacted patients with complex health conditions. We categorise vulnerable populations into three specific cohorts: patients with chronic disease, patients with cancer and patients with end-of-life care. We then investigate how each of these cohorts has been impacted by the pandemic. We also identify each cohort from an analytics perspective by leveraging various data collection strategies and analytical methods. The goals of this paper are to provide evidence on how to improve the quality of life for vulnerable populations and to inform a national response strategy for future pandemics, which seems inevitable in a highly globalised economy.

Technology-enabled maintenance via sensors and the internet of things (IoT) technologies is seen as the “new maintenance strategy”. The aim is to use these technologies to focus on unnecessary breakdowns and increase levels of asset availability where the existing of a dedicated team to maintain or data on asset condition is limited, useless or full of noise. This paper presents the integration of various sensing nodes within an automated laser cutting machine for monitoring of critical components’ conditions. The integrated system provides a centralised platform for data collection from various sensors and provides early failure detection mechanism based on specified set points with alert mechanisms utilising a custom dashboard for data visualisation. The research highlights the required stages for the identification of the appropriate components and sensory devices for the development of efficient monitoring environment.

This paper proposes a method for monitoring the structural health of concrete bridges in Iran. In this method, the bridge condition index (BCI) of bridges is determined by the analytical hierarchy process. BCI constitutes eight indices that are scored based on the experts' views, including structural, hydrology and climate, safety, load impact, geotechnical and seismicity, strategic importance, facilities, and traffic and pavement. Experts' views were analyzed by Expert Choice software, and the relative importance (weight) of indices were determined using the analytical hierarchy process (AHP). Then, the gave scores of experts were assigned to indices for various conditions. Bridge inspectors can examine the bridge, determine the scores of indices, and compute BCI. Higher values of BCI ​​indicate better conditions. Therefore, bridges with lower BCI take priority in maintenance activities. Five bridges in Iran, Semnan province, were selected as the case studies, and BCI calculation of these bridges was conducted.

In this research, a model is presented to detect and monitor the rotor bar’s condition of large motors. This proposed model uses two diagnostic methods MCSA and ZCT, to extract the fault components. The input of the proposed model is only the motor current at two levels of 80% and 100% of the nominal motor load, which by using the two methods MCSA and ZCT and making changes in how to use them can be the disadvantages of other methods such as incorrect detection of rotor bars in Large motors with variable load, the harmonical stator voltage (or the presence of the drives) and asymmetric conditions. The extracted components are classified using neural network, instead of experimental and human resources constraints. Using this classification algorithm is high accuracy and the ability to implement in conventional processors. The proposed model has been trained andtested using currents of real large motor data and 96.87% accuracy using neural network to detect faults and monitor the online status of the rotor bars. On the other hand, the proposed model has been implemented in the industrial environment (FiroozkoohCement Factory) and has been able to correctly determine the rotor bars of 4 large suspicious motors announced by the factory technicians.

The Soil Conservation Service - Curve Number (SCS-CN) method is extensively used to calculate the runoff from rainfall over a large catchment over the world. Slope is an important criterion for runoff but a very few attempts have been made to evaluate the effect of slope on the CN with runoff potential. The objective of this paper is to summarise the historical review on the effects of slope on CN and runoff potential in various regions by the hydrologists. This paper also depicts that how the various researchers proved the importance of consideration of slope for CN and runoff estimation. In addition, paper highlights the key features of research in future like to classify the watersheds on slope based CN, accurate Antecedent Moisture Condition (AMC) and proper initial abstraction in the various regions etc. Considering these parameters an accurate runoff estimation can be predicted and managed properly in the urban watersheds.

The Australian dairy sheep industry is small and mostly based on a natural grass grazing system which can limit productivity. The current study tested different plant oil-infused and rumen protected polyunsaturated fats and their interactions with sire breeds to improve lactation traits and body condition score (BCS) of ewes grazing low quality pastures. It was hypothesised that supplementing lactating ewe diets plant-derived polyunsaturated oils will improve milk production and composition without compromising BCS. Sixty ewes (n=10/treatment) in mid-lactation, balanced by sire breed, parity, milk yield, body condition score, and liveweight were supplemented with: 1) control: wheat-based pellets without oil inclusion; wheat-based pellets including 2) canola oil (CO); 3) rice bran oil (RBO); 4) flaxseed oil (FSO), 5); safflower oil (SFO) and 6) rumen protected fat containing eicosapentaenoic acid and docosahexaenoic acid (RPO). Except for the control group, all supplementary diets included the same level of 50 ml/kg DM of oil and all diets were isocaloric and isonitrogenous. Experimental animals were grazed in the same paddock with ad libitum access to pasture, hay and water during the 10-week study. RPO was the most effective diet that enhanced milk, fat and protein yields by approximately 30, 13, and 31% respectively (P<0.0001). Significant increase in milk production was also observed in CO, RBO, and SFO (P<0.0001). Breed significantly influenced animal performance with higher milk yield recorded for crossbred Awassi x East Friesian (AW x EF) (578 g/day) vs purebred Awassi (452 g/day) (P<0.0001). This study provides empirical evidence for the use of rumen-protected and plant-derived oil-infused pellets as supplements under low quality pasture grazing conditions, to improve production performance of purebred Awassi and crossbred AW x EF ewes.