In this applied research work, the risk of rock instability in the Aqabat Al-Sulbat road section located in the north-west area of Aseer Province in Saudi Arabia was evaluated, and the primary natural trigger factors of rock slope instability on further environmental components (rock slope stability, road network, and urban areas) were estimated using satellite images (Landsat8), digital terrain models, and geoprocessing in geographical information systems software (classification, overlapping algorithms and production thematic mapping in Arctoolbox). Additionally, field geotechnical investigations testing and over-coring drilling sampling allowed the characterization of the section of road in terms of geological structure and environmental components (geology, morphology, road network, lineaments, and hydrology). As a result, rock slope instability vulnerability mapping was simulated using satellite imagery and geographical information systems (GIS) and ranking natural trigger factors using the combined fuzzy Delphi analytical hierarchic process with the technique for order performance by similarity to ideal solution (TOPSIS) as multiple-criteria decision-making (MCDM) techniques. Additionally, many rock layer discontinuity stations were implemented to evaluate rock slope instabilities, and these were visualized using the Dips program and combined with modeling using 3DEC software to predict rock slope failure based on the distinct element method (DEM) at a small scale. Thereafter, safety factors were computed depending on these previous geospatial data. Finally, vulnerability index mapping was combined with rock instability risk mapping for the Aqabat Al-Sulbat road. Within the framework of sustainable development, these results can be used to inform the urban planning of the municipality of Aseer Province.

The enrichment of heavy metals in the soil–rice system is affected by various factors, which hampers the prediction of heavy metal concentrations. In this research, a prediction model (CNN-HM) of heavy metal concentrations in rice was constructed based on convolutional neural network (CNN) technology and 17 environmental factors. For comparison, other machine learning models, such as multiple linear regression, Bayesian ridge regression, support vector machine, and backpropagation neural networks, were applied. Furthermore, the LH-OAT method was used to evaluate the sensitivity of CNN-HM to each environmental factor. The results showed that the R² values of CNN-HM for Cd, Pb, Cr, As, and Hg were 0.818, 0.709, 0.688, 0.462, and 0.816, respectively, and both the MAE and RMAE values were acceptable. The sensitivity analysis showed that the concentrations of Cd and Pb, mechanical composition, soil pH, and altitude were the main sensitive features for CNN-HM. Compared with CNN-HM based on all input features, the performance of the quick prediction model that was based on the sensitive features did not degrade significantly, thereby indicating that CNN-HM has stronger stability and robustness. The quick prediction model has extensive application value for timely prediction of the enrichment of heavy metals in emergencies. This study demonstrated the effectiveness and practicability of CNNs in predicting heavy metal enrichment in the soil–rice system and provided a new perspective and solution for heavy metal prediction.

River Damodar (India) is one of the most significant tropical large rivers and this river is carrying predominantly industrial effluents, urban sewage, and non-degradable chemical agricultural fertilizers. Several industries, cities, and townships directly depend on this important river throughout the year. It is highly essential to evaluate its surface water quality, characteristics, and improvement status during the COVID-19 lockdown and unlock phases. The major objectives of the present study are to analyse changing nature of heavy metals (Zn, Cd, Pb, Ni, Cr, and Fe) and microbial load (TVC, TC, and FC) of river water and to identify heavy metals impact on water quality and human health in pre, during, and after unlocking of COVID-19 lockdown. Here, a total of 33 water samples have been collected in the pre-lockdown, lockdown, and unlock phases. The results showed that decreasing trend of the microbial load was found in the lockdown phase. Heavy metal pollution index (HPI) illustrated that all samples are highly polluted (HPI > 150) during the pre-lockdown phase, while during the lockdown phase; HPI showed that around 54.54% of samples have been positively changed (low pollution level). During the unlock phase, 45.45% of samples were again amplified to the high pollution level. Pearson’s correlation coefficient and hierarchical cluster analysis indicated strong relation among heavy metals with faecal coliform at a 0.05% level of significance. Noncarcinogenic hazard index (HI) shows the higher possibility of health risk (HI > 1) particularly for children in all the phases and during the lockdown phase, 36.36% of samples showed no possible health risk for adults (HI < 1). However, HI of dermal contact showed no possible health risk for children and adults in the assessment periods. This applied research can definitely assist planners and administrators in making effective solutions regarding public health.

To accelerate the transformation and application of basic research results, the Chinese government has repeatedly mentioned in a government work report that it is necessary to support research and innovation collaborations between knowledge research institutions and enterprises. However, few studies have focused on the evolution of collaborations between these organizations and the impact of collaborations on innovation performance (IP) in the field of renewable energy under the background of government-funded support (GFS). Based on scientific publications, we construct a GFS collaboration network in the wind power field to investigate the evolution of network structure characteristics, attribute proximity variables, and applied research collaboration (ARC), and we study the impact of network evolution on the IP of actors. The results show that the focal actor of the collaboration network prefers to engage in ARC with partners who are familiar and have the same knowledge base in different provinces. This collaboration tendency will reduce geographical proximity and increase the direct ties, indirect ties, technological proximity, and ARC of the ego network. Among them, direct ties have an inverted U-shaped effect on IP, geographical proximity has a significantly negative impact on IP, and the remaining variables have positive impacts on IP. Taken together, when the direct ties is within a certain range, these collaboration tendencies in a GFS collaboration network positively affect the IP of research institutions and enterprises.

Presently, the discovery of effective drugs and pesticides from eco-friendly biological sources is an important challenge in the field of life sciences. The present research was aimed for standardizing an innovative approach in the evaluation of the biological potentiality of the metabolites of fish-associated bacteria. We have identified 17 skin-associated bacteria from the freshwater fish, giant danio, Devario aquipinnatus. They were screened through biofilm forming and extracellular enzyme producing ability. The results of preliminary antibacterial evaluation of the bacterial supernatants underlined the importance of three potential strains (BH8, BH10 and BH11) for further applied research. Hence, such strains were subsequently subjected to a novel extraction procedure to overcome the difficulties found in polar solvents mixed with the supernatant. The lyophilized cell-free supernatant (LCFS) of 3 isolates were individually extracted by using methanol. During the testing of LCFS’s methanolic extract (LCFS-ME) of 3 isolates, only the extract of BH11-strain exhibited potent inhibitory activity against the pathogenic bacteria and fungi. Furthermore, the larvicidal and mosquitocidal assays on the filariasis vector, Culex quinquefasciatus also showed its potent toxicity on both the adults and developmental instars of mosquito. Through molecular and phylogenetic analyses, the BH11 strain was identified as Salmonella bongori (KR350635). The present finding emphasized that the S. bongori could be an important novel source of effective antimicrobials and mosquitocidal agents.

We present a synthetic review and expert consultation that assesses the actual risks posed by arthropod pests in four major crops, identifies targets for integrated pest management (IPM) in terms of cultivated land needing pest control and gauges the implementation “readiness” of non-chemical alternatives. Our assessment focuses on the world’s primary target pests for neonicotinoid-based management: western corn rootworm (WCR, Diabrotica virgifera virgifera) in maize; wireworms (Agriotes spp.) in maize and winter wheat; bird cherry-oat aphid (Rhopalosiphum padi) in winter wheat; brown planthopper (BPH, Nilaparvata lugens) in rice; cotton aphid (Aphis gossypii) and silver-leaf whitefly (SLW, Bemisia tabaci) in cotton. First, we queried scientific literature databases and consulted experts from different countries in Europe, North America, and Asia about available IPM tools for each crop-pest system. Next, using an online survey, we quantitatively assessed the economic relevance of target pests by compiling country-level records of crop damage, yield impacts, extent of insecticide usage, and “readiness” status of various pest management alternatives (i.e., research, plot-scale validation, grower-uptake). Biological control received considerable scientific attention, while agronomic strategies (e.g., crop rotation), insurance schemes, decision support systems (DSS), and innovative pesticide application modes were listed as key alternatives. Our study identifies opportunities to advance applied research, IPM technology validation, and grower education to halt or drastically reduce our over-reliance on systemic insecticides globally.