Average Mean Value Research Articles

Black Carbon Radiative Impacts on Surface Atmospheric Oxidants in China with WRF-Chem Simulation

Black carbon (BC) changes the radiative flux in the atmosphere by absorbing solar radiation, influencing photochemistry in the troposphere. To evaluate the seasonal direct radiative effects (DREs) of BC and its influence on surface atmospheric oxidants in China, the WRF-Chem model was utilized in this study. The simulation results suggested that the average annual mean values of the clear-sky DREs of BC at the top of the atmosphere, in the atmosphere and at the surface over China are +2.61, +6.27 and −3.66 W m−2, respectively. Corresponding to the seasonal variations of BC concentrations, the relative changes of the mean surface photolysis rates (J[O1D], J[NO2] and J[HCHO]) in the four seasons range between −3.47% and −6.18% after turning off the BC absorption, which further leads to relative changes from −4.27% to −6.82%, −2.14% to −4.40% and −0.47% to −2.73% in hydroxyl (OH) radicals, hydroperoxyl (HO2) radicals and ozone (O3), respectively. However, different from the relative changes, the absolute changes in OH and HO2 radicals and O3 after turning off BC absorption show discrepancies among the different seasons. In the North China Plain (NCP) region, O3 concentration decreases by 1.79 ppb in the summer, which is higher than the magnitudes of 0.24–0.88 ppb in the other seasons. In southern China, the concentrations of OH and HO2 radicals reach the maximum decreases in the spring and autumn, followed by those in the summer and winter, which is due to the enhancement of solar radiation and the summer monsoon. Thus, BC inhibits the formation of atmospheric oxidants, which further weakens the atmospheric oxidative capacity.

Effect of Age on the Body Parameters, Scrotum, Seminal Characteristics, Spermatozoa Integrity and Sexual Behavior in Ganjam Buck

Background: Bucks raised for breeding purposes receive no special attention and are raised at random without following the correct selection process. Besides, the fertility of the male bucks is never addressed. Goat breeders have noted a sharp decline in the reproductive efficiency of Ganjam goats, associated with a lengthened kidding interval, fewer successful pregnancies and decreased twining frequency. The measurement of male fertility parameters is crucial for estimating the breeding performance of any species since males contribute 50% of the reproductive efficiency. Methods: Six Ganjam bucks of age 1.5(B1), 2(B2), 2.5(B3), 3.5(B4), 4(B5) and 4.5(B6) years procured after selection on the basis of their body morphometry and sexual behaviour were maintained under optimum management practice. They were trained for semen collection. For study on effect age on the semen qualities, they were classified into two age groups and ejaculates were collected during two phases. Bucks of age below 3 were considered under Age group I and bucks older than 3 years were assigned Age group II. Twelve ejaculates from each buck were collected, six ejaculates between the month of July to August and six ejaculates during October to November. The total of 72 ejaculates was evaluated for the study of seminal characteristics. Besides, the external body parameters, scrotal parameters and sexual behaviour of the bucks were also recorded before each semen collection. Result: The bucks of age group II were found to be having significantly higher (p less than 0.01) body weight, mean body length and average value of chest girth than age group I bucks in the present study. The mean value of scrotal width of the bucks of age group II was significantly higher (p less than 0.01) as compared to that of age group I. The mean value of abnormalities of the bucks of age group II was significantly higher (p less than 0.01) than that of age group I. The mean value of acrosome integrity and MBRT of the bucks of age group II was significantly higher (p less than 0.05) to that of age group I. The present study will be helpful in the selection of Ganjam buck at appropriate age for breeding purpose basing on their body parameters, scrotum, seminal characteristics, spermatozoa integrity and sexual behavior.

Economic Causes of Rural-to-Urban Migration in Karnali Province of Nepal

Migration is a natural phenomenon that is influenced by social, economic, political, geographic, and environmental factors. The main objective of this study is to identify the economic causes of rural-to-urban migration in Karnali province of Nepal. The study was conducted to test the research instrument so it took 40 respondents from the total 400 samples to test the readability and understandability of the survey questionnaire. The study was based on the quantitative data. It was a cross-sectional study. The simple random sampling technique was used to select the respondent. Crosstab, Chi-square test, and t-test were done to analyze the quantitative data. The result shows that the average mean value of each statement was a minimum of 3.7 to a maximum of 4.1 which is close to the ‘Agree’. Overall, the total mean score for economic causes of rural-to-urban migration is 3.9625, indicating that respondents perceive these factors as moderately to highly significant drivers of migration. This underscores the complex interplay of economic factors in shaping migration decisions, highlighting the importance of addressing economic disparities between rural and urban areas to mitigate migration pressures and promote balanced regional development. Further study can be done to identify the economic changes in migrant family in between before and after rural-to-urban migration.

ESTIMATION OF LENGTH AND SLOPE FACTOR (LS) AS EROSION PREDICTION IN WAY PUBIAN SUB WATERSHED

Erosion caused by land degradation is one of the main challenges to soil quality around the world. One way to prevent erosion is by analyzing the length and slope factor (LS). The increasing length of the slope causes the higher amount of cumulative runoff and also the increasing steepness of the soil slope causes the higher speed of runoff that contributes to erosion. The purpose of this research is to analyze the LS factor value of Moore and Burch (1986) method and LS Nomograph for Way Pubian Subwatershed. The research method was done with Moore and Burch method and Nomograf LS. Moore and Burch (1986) method used the flow accumulation on DEM data as the main input in ArcGIS and the result shows the LS value ranged from 0 to 765.625 with an average (mean) value of 4.427. Meanwhile, the nomograph LS method resulted in high LS values and is not compatible with the Jakarta RTL-RLKT Preparation Implementation Guidelines (1986). Slope conditions affect the amount of surface flow so that it also affects the contribution of erosion and LS value. Way Pubian sub-watershed also classified as steep, resulting in a high LS value. Based on the method of Moore and Burch (1986) using ArcGIS software, we can conclude that the results of LS factor values can be used in Indonesia compared to the nomograph LS method which the results are less suitable due to topographic factors. As a result, the LS value from Moore and Burch (1986) method can be used in erosion prediction calculation.

Automated Surface Crack Detection in Historical Constructions with Various Materials Using Deep Learning-Based YOLO Network

ABSTRACT Cultural heritage (CH) constructions involve the use of diverse masonry materials. Under natural and human influences, masonry materials can undergo various types of damages, with crack damages being most prevalent. Developing a robust model using deep learning (DL) capable of detecting cracks in various CH materials is crucial. In this study, we compared the performance of the DL method You Only Look Once (YOLO) object detection network based on images in different masonry materials (stone, brick, cob, and tile) with that in a modern material (concrete). The dataset used in the study comprised 1213 bricks, 1116 concretes, 955 cobs, 882 stones, and 208 tile images. YOLOv5 architecture, transfer learning, and object detection models were utilized for detecting cracks and compare their performance in different materials. This study represents the first comparison of this kind using an original dataset. The DL model achieved mean average precision values of 94.4%, 93.9%, 92.7%, 87.2%, 83.4%, 81.6%, and 70.3% for concrete; concrete and cob, cob; stone; stone and brick; brick; and tile, respectively. The findings of this study indicate considerable potential for the widespread use of DL techniques in identifying cracks from images across various CH materials and assisting inspection professionals in damage surveys.

Diversity in the population of Fusarium oxysporum f. sp. ricini causing castor wilt disease in India

A major constraint on castor crop productivity is wilt disease caused by Fusarium oxysporum f. sp. ricini (For). In this study, morphological, pathogenic and molecular features of F. oxysporum f. sp. ricini isolates collected from different castor-growing agro-climatic zones of India were analyzed. Eighty-nine F. oxysporum f. sp. ricini isolates were obtained from wilt-infected root samples of castor plants from different states of India viz., Andhra Pradesh, Bihar, Gujarat, Haryana, Karnataka, Madhya Pradesh, Maharashtra, Odisha, Rajasthan, Telangana and Tamil Nadu. Morphology assessment in terms of variability in radial growth, size of micro and macroconidia and number of septa, grouped the isolates into 5 clusters in biplot analysis. A genotype-by-isolates bi-plot analysis ranked genotypes for pathogen susceptibility, with JI-35 being closest to the ideal and most susceptible, and DCS-9, and Kranthi being less susceptible. In contrast, 48–1 and JI-258 showed resistance, while Haritha and VP-1 exhibited intermediate susceptibility levels. K-means cluster analysis found 5 groups for pathogenic variability studies of isolates. The six isolates viz., For-12-5 (61.8 %), For-12-10 (50.5 %), For-13-47 (51.4 %), For-113 (86.1 %), For-13-33 (77.9 %) and For-53 (62.2 %) recorded as highly virulent isolates against wilt disease which showed >50 % average wilt incidence mean value across cultivars. Out of 40 ISSR primers tested, 11 primers gave polymorphic bands and analysis of genetic variability among 36 F. oxysporum f. sp. ricini isolates showed polymorphism with PIC, heterogygosity index, effective multiplex ratio, Marker index, and Shannon Diversity Index which ranged (in %) between 0.22 and 0.69, 0.19–0.44, 11.09–27.9, 6.14–8.98, and 0.78–3.04 respectively. The dendrogram analysis revealed 53 % similarity among the isolates studied and these isolates grouped broadly into 6 clusters. Variability among isolates was significant in morphological, pathogenic and molecular characters of F. oxysporum f. sp. ricini.

Mindful self-care among oncology nurses in China: a latent profile analysis

BackgroundOncology nurses are considered the group with the highest risk for moral distress, compassion fatigue and burnout. Mindful self-care may help oncology nurses improve their well-being and solve psychological problems. However, the investigation and in-depth analysis of mindful self-care among oncology nurses in China is lacking.ObjectivesTo identify heterogeneity groups of oncology nurses on mindful self-care ability and examine the sociodemographic correlation to these profiles.DesignCross-sectional descriptive study.ParticipantsThe study was carried out among oncology nurses in two affiliated comprehensive hospitals and one affiliated oncology hospital. A total of 839 oncology nurses were enrolled in this survey.MethodsFrom January to May 2023, a cross-sectional study was carried out among oncology nurses using convenient sampling. The subjects were given the brief Mindful Self-Care Scale (B-MSCS) and the General Demographic Information Questionnaire. Latent profile analysis using the Mplus 7.4 program was used to separate oncology nurses’ mindful self-care into a variety of subgroups. The SPSS 25.0 statistical program was used to analyze the data. One-way ANOVA and the chi-square test were performed to compare the score of B-MSCS in each class and the difference in sociodemographic characteristics among the subgroups. Multinomial logistic regression was used to examine the influence of the sociodemographic variables on each class.ResultsThe total score of the B-MSCS was 76.40 ± 13.19. The support structure dimension had the highest score, with an average mean value of 3.60, and physical care had the lowest score at 2.57. The findings of the latent profile analysis showed that respondents were divided into three classes, moderate mindful self-care(51.2%), low-low mindful relaxation(14.8%), and high-high mindfulness self-awareness(34.0%). Across scale scores and dimensions, three groups demonstrated statistically significant differences (p < 0.05). Univariate analysis revealed significant differences between the three profiles in terms of professional title, position, concern about self-care, interest in mindfulness, and experience with meditation (p < 0.05). Profile membership was predicted by 3 factors, namely, self-care status, interest in mindfulness, and experience with meditation.ConclusionThe mindful self-care among oncology nurses can be categorized into three latent profiles: moderate mindful self-care, low-low mindful relaxation, and high-high mindfulness self-awareness. Multinomial logistic regression results indicated that whether oncology nurses concern about self-care, interest in mindfulness and have experience with meditation influenced different latent profiles. Nursing manager should develop targeted intervention based on the typological characteristics of the oncology nurses to improve their mindful self-care ability and mental health.

Using Data Augmentation to Improve the Generalization Capability of an Object Detector on Remote-Sensed Insect Trap Images.

Traditionally, monitoring insect populations involved the use of externally placed sticky paper traps, which were periodically inspected by a human operator. To automate this process, a specialized sensing device and an accurate model for detecting and counting insect pests are essential. Despite considerable progress in insect pest detector models, their practical application is hindered by the shortage of insect trap images. To attenuate the "lack of data" issue, the literature proposes data augmentation. However, our knowledge about data augmentation is still quite limited, especially in the field of insect pest detection. The aim of this experimental study was to investigate the effect of several widely used augmentation techniques and their combinations on remote-sensed trap images with the YOLOv5 (small) object detector model. This study was carried out systematically on two different datasets starting from the single geometric and photometric transformation toward their combinations. Our results show that the model's mean average precision value (mAP50) could be increased from 0.844 to 0.992 and from 0.421 to 0.727 on the two datasets using the appropriate augmentation methods combination. In addition, this study also points out that the integration of photometric image transformations into the mosaic augmentation can be more efficient than the native combination of augmentation techniques because this approach further improved the model's mAP50 values to 0.999 and 0.756 on the two test sets, respectively.

A search for Galactic post-asymptotic giant branch stars in Gaia DR3

When low- and intermediate-mass stars leave the asymptotic giant branch (AGB) phase, and before they reach the planetary nebula stage, they enter a very brief and rather puzzling stellar evolutionary stage called post-AGB stage. The post-AGB phase lasts very briefly, about a few thousand years at most. The number of objects that are confirmed in this phase therefore is really small, and our understanding of this elusive stellar evolutionary stage is accordingly very limited. We provide a reliable catalogue of Galactic post-AGB stars together with their physical and evolutionary properties obtained through Gaia DR3 astrometry and photometry. As an added product, we provide information for a sample of other types of stellar objects, whose observational properties mimic those of post-AGB stars. Post-AGB stars are characterised by their infrared excesses and high luminosities. The publication of precise parallaxes in Gaia DR3 made it possible to calculate accurate distances and to revise the derivation of luminosities for post-AGB candidates, so that objects outside the expected luminosity range can be discarded. We started by identifying post-AGB stars or possible candidates from the bibliography, and we then searched for their Gaia DR3 counterpart sources. Using the available photometry, interstellar extinction, spectroscopically derived temperatures or spectral types and parallax-derived distances from the literature, we fitted their spectral energy distributions and estimated their luminosities and circumstellar extinctions. By a comparison to models, the luminosity values allowed us to determine which objects are likely post-AGB stars from other target types. Their position in the Hertzsprung-Russell diagram allows a direct comparison with updated post-AGB evolutionary tracks and an estimation of their masses and evolutionary ages. We obtained a sample of 69 reliable post-AGB candidates that meet our classification criteria, which provide their coordinates, distances, effective temperature, interstellar and circumstellar extinction, luminosity, mass, and evolutionary age. In addition, we provide similar data for other stellar objects in our initial compilation, such as supergiant stars and young stellar objects. Our identifications and parameters are compared with others found in the recent literature for the subject. We selected the data with the best precision in parallax and distance to obtain more accurate luminosities, which allowed us to confidently classify the objects of the sample in different stellar phases. In turn, this allowed us to provide a small but reliable sample of post-AGB objects. The derived mean evolutionary time and average mass values agree with theoretical expectations and with the mean mass value obtained in a previous work for the subsequent evolutionary stage, the planetary nebula stage.

Effect of blood and artificial saliva contamination on marginal adaptation and sealing ability of different retrograde filling materials: A comparative analysis.

The objective of this study was to evaluate the effect of blood and artificial salivary contamination of different root-end filling materials on microleakage using a confocal laser scanning microscope and on marginal adaptation using a scanning electron microscope. Eighty noncarious single-rooted teeth with mature apices were taken. After retro-cavity preparation, they were randomly assigned into two major groups (n = 40). They were contaminated with blood and artificial saliva, respectively. Each major group was divided randomly into four subgroups (n = 10) and filled as follows: Subgroup A, Biodentine; Subgroup B, bioactive bone cement; Subgroup C, Cention N; and Subgroup D, Bio-C Repair. The samples were sectioned transversely at 1 and 2 mm from the root apex and checked under a confocal laser scanning microscope for microleakage and under an scanning electron microscope for marginal adaptation. The average mean values were calculated. Independent samples t-tests, paired t-tests, and one-way analysis of variance with Tukey's post hoc tests were done to analyze the data. All the tested materials showed marginal gaps and dye leakage. The Bio-C Repair group showed the least mean marginal gap and dye leakage values, followed by bioactive bone cement, Biodentine, and Cention N, respectively, in both blood and artificial saliva contamination. However, the mean marginal gaps and dye leakage between the major groups were statistically insignificant. In an overall comparison, Bio-C Repair was found to be superior in terms of marginal adaptation and sealing ability under the test conditions.

Personality Profile of Elite Male Badminton Players of Bangladesh

Background: Psychology is the scientific study of human mind and its functions, especially those which affect behaviour in a given context. A personality profile is a comprehensive assessment that provides insights into an individual's psychological characteristics, behavioural tendencies, and emotional attributes. Objective: The purpose of the present study was to analyze and understand the personality profile of elite male badminton players of Bangladesh. Methods: The personality profile was measured for the present study. For assessing personality profile, 16 PF questionnaire which was developed by Cattell was considered. Personality factors were assessed using Bengali version of Raymond Cattell’s 16 PF questionnaires (Form-C) developed and standardized by Bose and Chatterjee (1984), Department of Applied Psychology, University of Calcutta. The 16 PF questionnaire was included total sixteen factors i.e. Factor A-reserved v/s outgoing; Factor B- less intelligent v/s more intelligent; Factor C-economically less stable v/s economically stable; Factor E- humble v/s assertive; Factor F-sober v/s enthusiastic; Factor G-expedient v/s conscientious; Factor H- shy v/s venturesome; Factor I-tough minded v/s tender minded; Factor L- trusting v/s suspicious; Factor M-practical v/s imaginative; Factor N-forthright v/s shrewd; Factor O- self assured vs. apprehensive; Factor Q1- conservative vs. liberal; Factor Q2- Group oriented v/s self sufficient; Factor Q3- undisciplined self-conflict v/s following self image; Factor Q4- relaxed v/s tensed. Total hundred male badminton players (n=100) who were attending National Coaching Camp of Bangladesh were selected as subjects. The collected data of personality factors were analyzed using descriptive and analytical statistics. Results: The results of the present study revealed that the subjects were lower average in Factor H. On the other hand they were also found to be higher than the normal in Factors G and Q3. In respect of other factors they had found average standard mean values. Conclusions: National level male badminton players of Bangladesh appear to be average in personality traits including; A (warmth), C (emotional stability), E (dominance), F (liveliness), I (sensitivity), L (vigilance), N (privateness, O (apprehension), Q1 (openness to change), and Q4 (tensions ). It is also found that they have less reasoning ability (Factor B), be shy, sensitive, and hesitant (Factor H) in respect of social boldness. National level male badminton players of Bangladesh appear to be dutiful and rule bound (Factor G), imaginative (Factor M), self reliant (Factor Q2) and socially precise (Factor Q3)) conscientious in respect of rule consciousness.

Perbandingan Kinerja Keuangan Perbankan Berbasis Syariah Dan Konvensional (2017-2020)

This research aims to determine whether there is a comparison between the financial performance of conventional commercial banks and sharia commercial banks. To see this comparison, this research uses four ratios, where the capital ratio uses the CAR (Capital Adequacy Ratio) ratio, the liquidity ratio uses the LDR (Loan to Deposit Ratio) ratio, the profitability ratio uses the ROA (Return On Asset) ratio, and the turnover ratio activities using the TATO (Total Asset Turnover) ratio. The method used in this research is to use the Independent Sample T-test to compare the performance of conventional commercial banks with sharia commercial banks. The type of research used in this research is comparative research. The research results show that there is a comparison between the financial performance of conventional commercial banks and sharia commercial banks when viewed from the CAR, LDR and ROA ratios, while in terms of the TATO ratio there is no comparison between the performance of the two banks. If viewed from the mean (average) value of the CAR, LDR and ROA ratios, the financial performance of conventional commercial banks is better than sharia commercial banks. Meanwhile, if you look at the TATO ratio, you don't see a much different comparison. Overall, it can be seen that conventional commercial banks are superior to sharia commercial banks. This is because conventional commercial banks have been operating much longer than sharia commercial banks which are relatively new. So sharia commercial banks need to increase public trust so that the existence of sharia banks in the eyes of the public can be greatly increased.

An Enhanced Multiple Unmanned Aerial Vehicle Swarm Formation Control Using a Novel Fractional Swarming Strategy Approach

This paper addresses the enhancement of multiple Unmanned Aerial Vehicle (UAV) swarm formation control in challenging terrains through the novel fractional memetic computing approach known as fractional-order velocity-pausing particle swarm optimization (FO-VPPSO). Existing particle swarm optimization (PSO) algorithms often suffer from premature convergence and an imbalanced exploration–exploitation trade-off, which limits their effectiveness in complex optimization problems such as UAV swarm control in rugged terrains. To overcome these limitations, FO-VPPSO introduces an adaptive fractional order β and a velocity pausing mechanism, which collectively enhance the algorithm’s adaptability and robustness. This study leverages the advantages of a meta-heuristic computing approach; specifically, fractional-order velocity-pausing particle swarm optimization is utilized to optimize the flying path length, mitigate the mountain terrain costs, and prevent collisions within the UAV swarm. Leveraging fractional-order dynamics, the proposed hybrid algorithm exhibits accelerated convergence rates and improved solution optimality compared to traditional PSO methods. The methodology involves integrating terrain considerations and diverse UAV control parameters. Simulations under varying conditions, including complex terrains and dynamic threats, substantiate the effectiveness of the approach, resulting in superior fitness functions for multi-UAV swarms. To validate the performance and efficiency of the proposed optimizer, it was also applied to 13 benchmark functions, including uni- and multimodal functions in terms of the mean average fitness value over 100 independent trials, and furthermore, an improvement at percentages of 29.05% and 2.26% is also obtained against PSO and VPPSO in the case of the minimum flight length, as well as 16.46% and 1.60% in mountain terrain costs and 55.88% and 31.63% in collision avoidance. This study contributes valuable insights to the optimization challenges in UAV swarm-formation control, particularly in demanding terrains. The FO-VPPSO algorithm showcases potential advancements in swarm intelligence for real-world applications.

Real-time detection of plastic part surface defects using deep learning- based object detection model

In this study, it was aimed to detect defects in plastic parts produced in a company operating in the automotive sub-industry using the YOLOv8 object detection model. The defect types seen in plastic parts were evaluated with the help of Pareto analysis, and scratches, stains and shine were selected as the most common defect types, and data on the three defect types were collected. YOLOv8 models were trained using faulty part images. As a result of the training, the highest mean average precision value of 0.990 was obtained in the YOLOv8s model, and the shortest training time was obtained in the YOLOv8n model. In the YOLOv8s model, which gave the highest mAP value, hyperparameter adjustment was made according to the batch size and learning rate values. The testing phase was carried out with the hyperparameter values that gave the best results and the mAP value was obtained as 0.902.

Assessment of the accuracy of 3D printed medical models through reverse engineering

The dimensional accuracy of additively manufactured (3D printed) medical models can be affected by various parameters. Although different methods are used to evaluate the accuracy of additively manufactured models, this study focused on the investigation of the dimensional accuracy of the medical model based the combination of reverse engineering (RE) and additive manufacturing (AM) technologies. Human femur bone was constructed from CT images and manufactured, using Fortus 450mc Industrial material extrusion 3D Printer. The additive manufactured femur bone was subsequently 3D scanned using three distinct non-contact 3D scanners. MeshLab was used for mesh analysis, while VX Elements was used for post-processing of the point cloud. A combination of the VX Inspect environment and MeshLab was used to evaluate the scanning performance. The deviation of the 3D scanned 3D models from the reference mesh was determined using relative metrics and absolute measurements. The scanners reported deviations ranging from −0.375 mm to 0.388 mm, resulting in a total range of approximately 0.763 mm with average root mean square (RMS) deviation of 0.22 mm. The results indicate that the additively manufactured model, as measured by 3D scanning, has a mean deviation with an average range of approximately 0.46 mm and an average mean value of around 0.16 mm.

Gravimetric characteristics and friction parameters of common bean (Phaseolus vulgaris L.)

When designing appropriate machinery systems, equipment, and infrastructures for interacting with, cultivating, gathering, and agriculture-related processing, it is required to have an understanding of the engineering characteristics of agricultural products. This unpredictability makes it difficult to design or develop machines that can efficiently and effectively manage a wide range of product characteristics. Experimental analysis was used to accomplish the study's objective, which was to investigate the implications of variation on the gravimetric characteristics and frictional parameters of common bean (Phaseolus vulgaris L.) concerning the design of the threshing machine. The mean average values of gravimetric parameters were determined by analysing the experimental data: arithmetic mean diameter (7.042 ± 0.473 mm), geometric diameter (6.737 ± 0.463 mm), bulk density (781.20 ± 25.34 kg m-3), true density (1347.03 ± 143.0 kg m-3), porosity (41.385 ± 7.05%), width (6.316 ± 0.502 mm), thickness (4.962 ± 0.50 mm), projected area (49.194 ± 6.715 mm2), and volume of the seed (161.689 ± 3.778 mm3). The average moisture content values were found to be 11.214±1.185% on a dry basis, the static coefficient of friction varied between 0.276 and 0.386 on the surface of iron sheets, 0.294 to 0.435 on stainless steel, 0.317 to 0.434 on galvanized iron, 0.321 to 0.451 on medium density fiberboard, 0.319 to 0.480 on aluminum, 0.310 to 0.470 on painted sheets, 0.320 to 0.440 on glass, 0.333 to 0.447 on plastic, and 0.374 to 0.575 on rubber. Perforated sheet surfaces showed the highest static coefficients of friction, followed by rubber, plastic, plywood, glass, aluminum, galvanized iron, painted sheet, stainless steel, and iron sheet surfaces. These data are not only required for predicting loads in agricultural storage structures but are also needed to establish useful sources for the development of machinery for handling, cleaning, storing, transporting and drying, among other things.

Constructing and applying a neural-network-based architectural landscape evaluation model

With the continuous improvement of living standards, people go outdoors and spend more and more time in scenic spots. The landscape architecture design that serves people in urban scenic spots attracts more and more public attention, which puts forward higher requirements for landscape architecture design that serves people in scenic spots. How to integrate better the design of all kinds of landscape architecture into nature, to serve the public better, is an urgent problem to be solved at this stage. This paper selects the evaluation indexes of urban architectural landscapes, uses the analytic hierarchy process to determine the weight of each index and quantifies six evaluation indexes to build an evaluation model of architectural landscape design. In terms of the improvement of the you only look once version 4 (YOLOv4) model, MobileNetV3 was selected as the backbone feature extraction network, and the convolution in the feature enhancement extraction network was replaced by the depth-separable volume, and an architectural landscape recognition system based on the improved YOLOv4 model was constructed. In terms of algorithm performance verification, the improved algorithm was compared with the single-shot detector, MobileNetV3, ShuffleNetV2, YOLOv3, YOLOv4 and YOLOv5s algorithms under multiple evaluation indexes. The experimental results show that the size of the model is 51.4 MB, which does not cause a large burden. The mean average precision value of the improved YOLOv4 algorithm is 93.5%, and the frames per second (FPS) value is 30 FPS, which has higher recognition accuracy and detection speed and has obvious advantages.

The Contribution of Subsistence Agriculture to the Livelihoods of the Smallholder Farmers in South Kivu Province, Democratic Republic of Congo

Subsistence agriculture in most cases is practiced on a limited space of land by farmers and it is dependent on human power that results into production of food consumed at household level, with little or no surplus for sale. A study about the contribution of subsistence agriculture to the livelihoods of the smallholder farmers in south Kivu province was carried out. A total of 303 smallholder farmers were randomly selected and interviewed to examine the effects of practicing subsistence agriculture on the socioeconomic status of the farmers. How the socioeconomic status attained by the farmers influenced their sustainable livelihoods was also assessed. Data were analyzed using means, frequency distribution, correlation, ANOVA and Chi square test. Thematic analysis was done on qualitative data. The farmers practiced shifting and primitive agriculture, intensive subsistence, and nomadic herding. There was a significant difference in the yields of crops grown (F = 0.0088; P = 0.05), while education and income status significantly affected the farmer’s socioeconomic status by practicing subsistence agriculture (P &lt; 0.05). An average mean value of 3.12 for the components of the farmers’ livelihoods were exhibited, with the human capital mean value being highest (4.16) compared to natural, social and financial capital. The socioeconomic status attained had a significant effect on sustainable livelihoods of the farmers at P &lt; 0.05. This implies that the smallholder farmers need to be trained by the agricultural extension workers better and sustainable methods of farming for higher crop yields. In addition, the Central Government though the Ministry of Agriculture, should avail improved seed inputs to the local community for better crop yields. All these will enable the smallholder farmers improve their socioeconomic status and at the same time attain higher sustainable livelihoods.

Deep machine learning approaches for battery health monitoring

The performance and longevity of batteries can be measured through battery parameters, like: state of charge, state of health, and remaining useful life. Therefore, accurate estimation of these parameters is essential for developing efficient battery management systems. Recently, a variety of smart model-based and data-driven artificial neural network methods have been evolved for efficient management of battery systems. However, there is space for improving the accuracy and reliability of the proposed methods, particularly in the prediction and forecasting of battery performance metrics. In this study, a deep machine learning based forecasting technique has been proposed to better estimate the battery performance parameters. Available open source data-sets on three distinct battery technologies comprising of battery cycling profiles and environmental conditions, are adopted to construct and validate models for predicting battery health. A common data-set is developed by combining the open source data, which is later supplied as input to the predictive analytics and time series analysis. Encoders and decoders are employed for the extraction of important and relevant feature information from the data-set. Three different network model-based architectures are developed to predict the state of charge of Lithium-ion batteries. The proposed models are observed to accurately capture the dynamic behavior of the battery during discharge, which is confirmed by the analysis of the recorded voltage, current, and temperature data, and the precise estimation of state of charge. Furthermore, a novel forecasting method based on the time-series analysis is also introduced. Mean average error values less than 0.2% are observed when neural basis expansion analysis for interpretable time series architecture in conjunction with encoder–decoder-based feature extraction is employed.

Assessing the Influence of Cost on the Implementation of Tetfund Projects in Selected Public Universities in Nigeria

The Academic Staff Union of University (ASUU) strike in Nigeria has highlighted the need for improved funding options for public institutions, particularly in the education sector. A study on TETFUND projects in the Southeast found that project success and cost performance are crucial for building projects. The study used a census technique, questionnaires, and content validity to confirm the instrument's inclusivity and relevance to strategic leadership and project management sustainability. The study reveals that the cost of every project is important, regardless of funding, and that cost availability has hindered any TETfund project at State universities in South-East, Nigeria.The majority of respondents strongly agreed that cost determines whether a TETfund project is completed with or without a supervisor, and that cost gives contractors more chance of income during the TETfund project period. Furthermore, the cost of the project motivates workers to complete it within an appropriate period. The analysis shows that TETfund cost has a positive effect on the completion rate of projects across State Universities in SouthEast, Nigeria, with an average mean value of 3.65&gt;3.00. The test of hypotheses showed that TETfund cost has no significant positive effect on the completion rate of projects across State Universities in South-East, Nigeria. The results showed that the model in this table has a statistically significant positive effect, and the alternate conclusion is accepted, as TETfund cost had a significant positive effect on the completion rate of the project across State Universities.