Integrating predictive modeling techniques with geospatial data for landslide susceptibility assessment in northern Pakistan

The eastern road section of the ChinaPakistan Economic Corridor(CPEC)traverses the challenging terrain of northern Pakistan,where frequent landslides pose a significant threat to socioeconomic development and infrastructure.However,the insufficient data on landslide hazards presents a substantial challenge to practical mitigation efforts.Therefore,we conducted an extensive study to gain insight into landslide assessment along the Mansehra-Muzaffarabad-Mirpur and Mangla(MMMM)Expressway.This study involved preparing a landslide inventory,analyzing landslide causative factors,and developing landslide susceptibility models(LSMs)using published data,remote sensing interpretations,field excursions and integrated predictive techniques.We first used Pearson's correlation coefficient(PCC),variable importance factors(VIF),and information gain ratio(IGR)to evaluate multicollinearity among the selected landslide causative factors(LCFs).Then,the topographic roughness index(TRI)with VIF>5 and PCC>0.7 was considered a redundant factor and thus removed before the data modeling.Finally,we adopted multiple machine-learning methods to analyze landslide susceptibility.The results indicate that the landslide inventory contains 1,776 events,of which 674 were classified based on geometrical and lithological configurations.The IGR results show that the rainfall,lithology,PGA,drainage density,slope,and distance to fault are the most effective LCFs.The AUC values for random forest(RF)(0.901),extreme gradient boosting(XGBoost)(0.884),and K-nearest neighbor(KNN)(0.872)remained higher than evidential belief function(EBF)(0.833),weight of evidence(WoE)(0.820),and certainty factor(CF)(0.810),respectively.The RF model outperformed all other models in terms of prediction.However,these models are accurate but newer in the area;thus,susceptible zones were verified with comprehensive field investigations.The northern and central regions accounted for the high and very high susceptibility classes in the final landslide susceptibility mapping(LSM)compared to the southern areas.

Effects of medicinal plants on Alzheimer＇s disease and memory deficits

Alzheimer＇s disease is an age-related neurodegenerative disorder characterized by memory deficits. Various studies have been carried out to find therapeutic approaches for Alzheimer＇s disease. However, the proper treatment option is still not available. There is no cure for Alzheimer＇s disease, but symptomatic treatment may improve the memory and other dementia related problems. Traditional medicine is practiced world- wide as memory enhancer since ancient times. Natural therapy including herbs and medicinal plants has been used in the treatment of memory deficits such as dementia, amnesia, as well as Alzheimer＇s disease since a long time. Medicinal plants have been used in different systems of medicine, particularly Unani system of medicines and exhibited their powerful roles in the management and cure of memory disorders. Most of herbs and plants have been chemically evaluated and their efficacy has also been proven in clinical trials. However, the underlying mechanisms of actions are still on the way. In this paper, we have reviewed the role of different medicinal plants that play an important role in the treatment of Alzheimer＇s disease and memory deficits using conventional herbal therapy.

Effect of Plant Growth-Promoting Rhizobacteria on Growth,Nodulation and Nutrient Accumulation of Lentil Under Controlled Conditions

Application of plant growth-promoting rhizobacteria (PGPR) has been shown to increase legume growth and development under field and controlled environmental conditions. The present study was conducted to isolate plant growth-promoting rhizobacteria (PGPR) from the root nodules of lentil (Lens culinaris Medik.) grown in arid/semi-arid region of Punjab, Pakistan and examined their plant growth-promoting abilities. Five bacterial isolates were isolated, screened in vitro for plant growth-promoting (PGP) characteristics and their effects on the growth of lentil were assessed under in vitro, hydroponic and greenhouse (pot experiment) conditions. All the isolates were Gram negative, rod-shaped and circular in form and exhibited the plant growth-promoting attributes of phosphate solubilization and auxin (indole acetic acid, IAA) production. The IAA production capacity ranged in 0.5-11.0 μg mL-1 and P solubilization ranged in 3-16 mg L-1 . When tested for their effects on plant growth, the isolated strains had a stimulatory effect on growth, nodulation and nitrogen (N) and phosphorus (P) uptake in plants on nutrient-deficient soil. In the greenhouse pot experiment, application of PGPR significantly increased shoot length, fresh weight and dry weight by 65%, 43% and 63% and the increases in root length, fresh weight and dry weight were 74%, 54% and 92%, respectively, as compared with the uninoculated control. The relative increases in growth characteristics under in vitro and hydroponic conditions were even higher. PGPR also increased the number of pods per plant, 1 000-grain weight, dry matter yield and grain yield by 50%, 13%, 28% and 29%, respectively, over the control. The number of nodules and nodule dry mass increased by 170% and 136%, respectively. After inoculation with effective bacterial strains, the shoot, root and seed N and P contents increased, thereby increasing both N and P uptake in plants. The root elongation showed a positive correlation (R2 = 0.67) with the IAA production and seed yield exhibited a positive correlation (R2 = 0.82) with root nodulation. These indicated that the isolated PGPR rhizobial strains can be best utilized as potential agents or biofertilizers for stimulating the growth and nutrient accumulation of lentil.

Qualitative properties and bifurcations of discrete-time Bazykin–Berezovskaya predator–prey model

The positive connection between the total individual fitness and population density is called the demographic Allee effect.A demographic Allee effect with a critical population size or density is strong Allee effect.In this paper,discrete counterpart of Bazykin–Berezovskaya predator–prey model is introduced with strong Allee effects.The steady states of the model,the existence and local stability are examined.Moreover,proposed discrete-time Bazykin–Berezovskaya predator–prey is obtained via implementation of piecewise constant method for differential equations.This model is compared with its continuous counterpart by applying higher-order implicit Runge–Kutta method(IRK)with very small step size.The comparison yields that discrete-time model has sensitive dependence on initial conditions.By implementing center manifold theorem and bifurcation theory,we derive the conditions under which the discrete-time model exhibits flip and Niemark–Sacker bifurcations.Moreover,numerical simulations are provided to validate the theoretical results.