India is being projected internationally as a country of good growth and good governance, which in turn, it is asserted, should lead to prosperity for its people, especially in terms of proper employment, income, and overall standard of living. Drawing on certain dimensions of decent work proposed by International Labour Organization to measure the quality of employment in India, this article explores how far the notions of good/high growth and good governance discourse are corroborated by the evidence of good quality employment in India. The study is based on the nationally representative unit/individual-level data published by the Government of India during the three periods 2009–10, 2018–19, and 2022–23. The main findings of analysis are: (a) the overall quality of employment in India is poor and stagnant or deteriorating, and (b) the macro-level (sub-national state-level) aspects, such as the overall volume of economic activities, the extent of quality governance, the flexibility of business regulatory environment and better labour law-related compliance, have had a significant negative influence on the quality of employment. Thus, this paper suggests that the very policy environment and the pattern of economic growth have put a drag on the quality of employment in India. Given this, we suggest a variety of countervailing policy options and emphasise the role of civil society and politics.

Aqueous suspensions of allophane show relatively high viscosity, presumably because of strong particle interaction between the unit particles. To test this hypothesis, we measured the particle weight and particle size of allophane during a dispersion using the light scattering method. The particle weight was more than several hundred times larger than that of the unit particle, and the size was 100–400 nm, whereas the Stokes’ diameter of the particles in the sample was less than 50 nm. Particle weight and size varied with the pH of the sample. Particle sizes were cross-checked by ultrafiltration through membrane filters. The experimental findings show that the unit particles of allophane within dilute dispersions appear to be associated like strings of beads, forming domains (primary floccules) about 100 nm in diameter. When these domains coagulate under certain conditions, they do not grow analogously but form clusters, such as secondary floccules, then precipitate. Formation of secondary flocculation of loose structure accounts for the maximum relative viscosity at the transition pH between dispersion and coagulation.

Barriers to suicide cluster detection and monitoring include requiring advanced software and statistical knowledge. We tested face validity of a simple method using readily accessible household software, Excel 3D Maps, to identify suicide clusters in this county, years 2014–2019. For spatial and temporal clusters, respectively, we defined meaningful thresholds of suicide density as 1.39/km2 and 33.9/yearly quarter, defined as the 95th percentile of normal logarithmic and normal scale distributions of suicide density per area in each ZIP Code Tabulated Area and 24 yearly quarters from all years. We generated heat maps showing suicide densities per 2.5 km viewing diameter. We generated a one-dimensional temporal map of 3-month meaningful cluster(s). We identified 21 total population spatial clusters and one temporal cluster. For greater accessibility, we propose an alternative method to traditional scan statistics using Excel 3D Maps potentially broadly advantageous in detecting, monitoring, and intervening at suicide clusters.

In this paper, we expolore Multi-Agent Reinforcement Learning (MARL) methods for unmanned aerial vehicle (UAV) cluster. Considering that the current UAV cluster is still in the program control stage, the fully autonomous and intelligent cooperative combat has not been realised. In order to realise the autonomous planning of the UAV cluster according to the changing environment and cooperate with each other to complete the combat goal, we propose a new MARL framework. It adopts the policy of centralised training with decentralised execution, and uses Actor-Critic network to select the execution action and then to make the corresponding evaluation. The new algorithm makes three key improvements on the basis of Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. The first is to improve learning framework; it makes the calculated Q value more accurate. The second is to add collision avoidance setting, which can increase the operational safety factor. And the third is to adjust reward mechanism; it can effectively improve the cluster’s cooperative ability. Then the improved MADDPG algorithm is tested by performing two conventional combat missions. The simulation results show that the learning efficiency is obviously improved, and the operational safety factor is further increased compared with the previous algorithm.

African yam bean (AYB) is an affordable protein source capable of diversifying the food base in sub-Saharan Africa. However, research efforts made towards the crop's improvement and in expanding production are limited. This study characterized 169 AYB accessions at Jimma, Ethiopia, using 31 phenotypic characters. The analysis of variance revealed highly significant (P < 0.01) differences for days to 50% flowering, days to first flowering, leaf area, number of seeds per pod, pod length, seed thickness, total seed weight, petiole length and significant (P < 0.05) difference for terminal leaf length. Accession TSs62B produced the highest number of seeds per pod (17.65) and recorded the highest 100 seed weight (25.30 g), while 3A was the earliest to flower at an average of 84.50 d. Principal component analysis (PCA) of qualitative traits attributed 77.6% of observed variations to the first five principal components, of which the first two PC axes accounted for 53.6% of total variations. Cluster analysis and PCA biplot distinctly grouped the accessions into two major groups, cluster I had the highest number of accessions (108). The analytical approaches used confirmed considerable diversity across the germplasm with a distance matrix ranging from 0.37 to 0.85. The extent of diversity reflected in the current study provides breeders the baseline information to design breeding strategies, which might help identify materials for release as variety or parental lines for hybridization programmes.

The maladaptive nature of Perfectionistic Automatic Thoughts (PAT) increases the importance of evaluating the construct. This study aims to identify different clusters of PAT in undergraduates, and to check possible inter-cluster differences in the dimensions of dispositional empathy and emotional intelligence in a sample of 691 Spanish undergraduates (Mage = 23.1; SD = 5.26). The Perfectionism Cognitions Inventory, the Interpersonal Reactivity Index and the Trait Meta-Mood Scale were used. Three clusters with low (LPAT), moderate (MPAT) and high (HPAT) levels of PAT were identified. Statistically significant differences were observed between these clusters in terms of dispositional empathy and emotional intelligence dimensions. HPAT significantly scored higher than LPAT on Fantasy, Empathic Concern, Personal Discomfort and Perception, as well as in comparison with MPAT on Fantasy, Empathic Concern, Personal Discomfort and Perception. Moreover, MPAT obtained significantly higher scores on Comprehension and Repair than LPAT. Effect sizes for these differences were of a small magnitude, except for the HPAT and LPAT contrasts, whose differences were of a moderate magnitude.

Hubei province in China has had the most confirmed coronavirus disease 2019 (COVID-19) cases and has reported sustained transmission of the disease. Although Lu'an city is adjacent to Hubei province, its community transmission was blocked at the early stage, and the impact of the epidemic was limited. Therefore, we summarised the overall characteristics of the entire epidemic course in Lu'an to help cities with a few imported cases better contain the epidemic. A total of 69 confirmed COVID-19 cases and 11 asymptomatic carriers were identified in Lu'an during the epidemic from 12 January to 21 February 2020. Fifty-two (65.0%) cases were male, and the median age was 40 years. On admission, 56.5% of cases had a fever as the initial symptom, and pneumonia was present in 89.9% of cases. The mean serial interval and the mean duration of hospitalisation were 6.5 days (95% CI: 4.8–8.2) and 18.2 days (95% CI: 16.8–19.5), respectively. A total of 16 clusters involving 60 cases (17 first-generation cases and 43 secondary cases) were reported during the epidemic. We observed that only 18.9% (7/37) index cases resulted in community transmission during the epidemic in Lu'an, indicating that the scale of the epidemic was limited to a low level in Lu'an city. An asymptomatic carrier caused the largest cluster, involving 13 cases. Spread of COVID-19 by asymptomatic carriers represents an enormous challenge for countries responding to the pandemic.