Analysis and Prediction of Land Resources’ Carrying Capacity in 31 Provinces of China from 2008 to 2016

The rapid development of urbanization, population growth, and unreasonable use of land resources have made the contradiction between human beings and land increasingly prominent, and the carrying capacity of land resources has become an important factor affecting the sustainable development of a city or even a country. Based on the carrying capacity of agricultural land, construction land, and ecological land in 31 provinces of China, this paper analyzed the spatiotemporal characteristics of the carrying capacity of land resources in 31 provinces of China from 2008 to 2016 by using single-factor carrying capacity evaluation and comprehensive carrying capacity evaluation methods. The evaluation results were divided into six levels (No pressure, Lower pressure, Low pressure, Medium pressure, High pressure, Higher pressure), and the ArcGIS10.2 software was used for visualization, and the carrying capacity prediction model was constructed to predict the development trend of the comprehensive carrying capacity of land resources in 2020 and 2025. The results showed that China’s land carrying capacity is extremely unbalanced. Whether from the evaluation results of single-factor carrying capacity or comprehensive carrying capacity, we showed the characteristics of high pressure in the east and low pressure in the west, and that the regional land resources have a deteriorating trend. Through the prediction of the future land resource carrying capacity index, it was found that the comprehensive carrying capacity index of land resources in 31 provinces of China will still show an upward trend in the future.

Quality of life and the work capacity of professional nursing staff in the hospital environment

This study aimed to evaluate the association between life quality and the work capacity of nursing professionals in a public hospital of the public health system. A cross-sectional, quantitative study with the participation of 115 nursing professionals. The study method used the WHOQOL-brief questionnaire, the Work Capacity Index questionnaire, and a sociodemographic and occupational questionnaire. The data were analyzed through descriptive and inferential statistics, where values of p≤ 0.05 were considered significant. In terms of life quality, the domains that presented higher averages were for psychological 70.0±14.5 and social relations 70.8±19.8, with the physical domain reaching 64.4±11.9 and the environment at 57.7±13.6. The average score from the Work Capacity Index was 40.3±6.1; median: 42; IQR: 37.0-45.0), with a predominance of good and excellent for work capacity. Work capacity was increased for being male (β=3.99; p=0.016) and negatively associated to age (β=-0.31; p<0.001). In conclusion, it is verified that there is a positive correlation between the capacity for work and the evaluation of the quality of life, but it is emphasized that the sociodemographic and occupational characteristics lead the nursing professional to present a work capacity reduction, which generates alterations in the perception of life quality.

National Innovation Capacity and Economic Progress of Countries

The ability of the national economy to create and to valorize innovations on the market in order to produce economic goods represents its national innovation capacity, which is at the same time a key determinant of countries’ economic progress. Due to this fact, its relevance imposes the task of identifying, as accurately as possible, the key theoretical postulates on which this concept is based, as well as calculating the Innovation capacity index by which it is possible to predict progress in building innovation capacity of individual countries and mutual comparison with other countries according to innovation capabilities. After a brief explanation of the essence of learning, on which this concept is based, an attempt is made to calculate the Innovation capacity of the European Union and the Western Balkans, on the one hand, and to consider the interdependence of the obtained results and the achieved level of their economic development in 2020, on the other hand. The results of the research confirmed the strong connection between the Innovation capacity index and the achieved level of economic development of countries expressed in terms of gross domestic product per capita.

Characterising hospitals’ suitability for medical officer internship training in Kenya: analysis of the Kenya Harmonized Health Facility Assessment

Background Kenya has significantly expanded its medical school numbers and internship training hospital numbers to address its workforce gap. The majority of newly accredited internship hospitals are first-level referral/district hospitals, which are considered to have shortage of staff, medications, have limited service capacity and are described as “not organized for training purpose”. Using data from the Kenya Harmonized Health Facility Assessment (KHFA) 2018, we characterise the readiness and capacity of 61 internship hospitals to understand whether they are suitable to provide internship training for medical doctors. Methods We used secondary data from KHFA 2018, which sampled 61 out of all 74 internship hospitals in Kenya. Comparing against the minimum requirement outlined in the national guidelines for medical officer interns, we filtered and identified 166 indicators from the KHFA survey questionnaire and grouped them into 12 domains. An overall readiness and capacity index was calculated as the mean of 12 domain-specific scores for each facility. We compared the readiness and capacity of each domain and overall between Level 4 small hospitals, Level 4 large hospitals and Level 5 & 6 hospitals. Results The average overall capacity and readiness index is 69% for all internship training centres. Hospitals have moderate capacity and readiness (over 60%) for most of the general domains, though there is huge variation between hospitals and only 29 out of 61 hospitals have five or more specialists assigned, employed, seconded or part-time - as required by the national guideline. Quality and safety score was low across all hospitals with an average score of 40%. As for major specialties, all hospitals have good readiness and capacity for surgery and obstetrics-gynaecology, while mental health was poorest in comparison. Level 5 & 6 facilities have higher capacity scores in all domains when compared with Level 4 hospitals. Conclusion Major gaps exist in staffing, equipment and service availability of Kenya internship hospitals. Level 4 hospitals are more likely to have a lower readiness and capacity index, and should be reviewed and improved to provide appropriate and well-resourced training for interns and to utilise appropriate resources to avoid improvising .

EVALUATION OF DRIPPER EFFICIENCY WITH THE USE OF FERTILIZER

The objective of the work was to analyze the uniformity of drip fertigation compared to the use of water alone, analyzing by the Tukey test at 5% significance for CUC and DUC, defining the process capability from the process capacity index (Pc) . The experiment was carried out on a test bench in the irrigation and fertigation laboratory (LIF) of the University of Western Paraná, where in the first treatment, water was used for the irrigation process and in the second treatment, commercial fertilizer was diluted in the reservoir to perform the fertigation. The experimental statistics is constituted in an analysis of variance with subsequent unfolding of the interaction and Tukey test at 5% probability to compare the averages of uniformities, flow and pressure of the system. Finally, the process capability (Pc) was defined for the distribution uniformity variable. The results showed statistical superiority for the fertigation process, however, it is noteworthy, that both treatments achieved excellence in their classifications with values ​​above 90% for both CUC and DUC, according to the reference.

Analysis of the Potential of Beef Cattle Business Development in Indramayu District

This study aimed to analyze the potential for cattle business development in Indramayu Regency. The research locations selected using purposive sampling method were based on the following criteria: (1) Indramayu Regency is the 2nd largest rice producers in the Province of West Java, (2) The area covers 2,099.42 km2 based on secondary data obtained from BPS Indramayu Regency in numbers and (3) The area of agricultural land is still larger than the area of resident housing. The data analysis method used the carrying capacity index (IDD) and Location Question (LQ) analysis formulas. The data were processed and explained descriptively. The results of this study resulted in carrying capacity values consisting of 3 patterns, namely the highest carrying capacity value > 20,000 AU (Gantar and Terisi). Medium carrying capacity value > 10,000 AU (Cikedung and Gabuswetan). Low value carrying capacity 5.000-10.000 AU (Haurgelis, Suyeg, Juntiyuat, Bongdua, Kertasemaya, Patrol, Kedokanbunder and Sindang). The results of the analysis of IDD > 2 there are 12 sub-districts, meaning that based on the availability of forage feed from agricultural land, it is included in the safe category to increase the population of beef cattle. The potential for beef cattle development in Indramayu Regency needs to be prioritized in 12 sub-districts with LQ > 1 and IDD > 2 accompanied by government policies to support investment in livestock marketing facilities and infrastructure for smallholders.

Development of an index to assess Covid-19 hospital care installed capacity in the 450 Brazilian Health Regions

Objective: We assessed the Brazilian health system's ability to respond to the challenges imposed by the Covid-19 pandemic considering hospital capacity in the 450 Health Regions of the country in 2020. Hospital capacity referred to the availability of hospital beds, equipment, and human resources. Methods: Data came from National Register of Health Facilities on the availability of Covid-19 resources in inpatient facilities from January to December,2020. Assessed resources were health professionals, hospital beds, and medical equipment. A synthetic indicator, Installed Capacity Index (ICI) was calculated using Principal Component Analysis. Results: There was an increase in all selected indicators between January and December 2020. We observed differences between the Northeast, North regions, and the other regions of the country. Most Health Regions presented low ICI. The ICI increased especially in regions with considerably high baseline capacity in January 2020. The Northeast and North had a higher concentration of low ICI regions. Conclusion: The information here provided may be used by health authorities, providers, and managers in planning and adjusting for future Covid-19 care and in dimensioning the adequate supply of hospital beds, health care professionals, and devices in Health Regions to reduce associated morbidity and mortality.