This study explores the potential of ten arid dryland plant species as novel bioherbicides for the sustainable suppression of purple nutsedge (Cyperus rotundus L.), a troublesome weed in many ecosystems. We investigated the allelopathic effects of Calotropis procera, Alhagi maurorum, Tamarix aphylla, Aerva javanica, Capparis decidua, Withania coagulans, Leptadenia pyrotechnica, Haloxylon salicornicum, Fagonia indica, and Rhazya stricta using a factorial experiment arranged in a completely randomized design with four replications. Three treatments were compared: a control (C1), a crude aqueous extract (C2), and a 20-fold concentrated extract (C3). The concentrated extracts (C3) of all species completely inhibited the sprouting and growth of purple nutsedge. Notably, F. indica (P9 × C2) achieved 100% suppression even at the crude extract level, performing comparably to the concentrated extracts of the other species. H. salicornicum (P8 × C2) and L. pyrotechnica (P7 × C2) showed statistically similar suppression to F. indica. Other crude extracts significantly reduced sprouting percentage, sprouting energy, vigor index, shoot and root length, biomass, and chlorophyll index compared to the control, except for A. maurorum and W. coagulans. These findings underscore the potential of dryland plant extracts, especially F. indica, as promising natural herbicide sources for managing purple nutsedge. Incorporating these bioherbicides into integrated weed management strategies offers a sustainable and eco-friendly alternative to synthetic herbicides, reducing their usage and enhancing ecological health.

The study aims to investigate the safety and feasibility of retrograde CTO intervention via collateral connection grade 0 (CC‐0) septal channel and to identify predictors of collateral tracking failure.

Cyperus rotundus is rapidly growing plant can quickly form dense colonies through the extensive underground system of tuber and rhizomes. It is highly competitive for resources and causes a significant yield reduction in field crops. The nonchemical method has recently been practiced to control this noxious weed species. The current study was conducted to determine the effect of sowing depths (4, 8, 12, and 16 cm) and water regimes (deficit irrigation, medium irrigation, and frequent irrigation treatments) on C. rotundus shoot growth and underground growth of tubers in pots soil. Weed emergence, i.e., mean emergence time (MET), emergence index (EI), final emergence percentage (FEP), and shoot growth traits i.e. shoot density, shoot fresh weight, shoot dry weight, and tuber growth traits i.e. tuber density, tuber weight, root density, and root weight were recorded under the completely randomized (CRD) factorial design. The greatest suppression of C. rotundus emergence and growth was found at 16 cm sowing depth. The mean emergence time (MET) of C. rotundus was increased by increasing the sowing depth. Minimum value of shoot densty (9.5 g) and tuber density (15.75 g) were recorded at 16 cm sowing depth under deficit irrigation treatment respectively.While shoot and tuber desnity traits were also drastically suppressed by increasing sowing depth at the deficit irrigation level. It is concluded that sowing depths and deficit irrigation methods can be included as a reliable approach for controlling purple nutsedge shoot and tuber growth.

Wastewater irrigation is becoming a massive challenge for sustainable agriculture. Particularly, copper (Cu) presence in wastewater poses a great threat to the food chain quality. Thus, scientists need to address this issue by using chemical and organic soil amendments to restore the soil ecosystem. Therefore, this study aims to examine the efficacy of sulphur, compost, acidified animal manure and sesame straw biochar for Cu immobilization, adsorption and Brassica growth in wastewater irrigated soil. The current findings presented that all the soil amendments prominently improved brassica yield and significantly minimized the Cu uptake by Brassica shoots and roots in sesame straw biochar (SB) (64.2% and 50.2%), compost (CP) (48% and 32.5%), acidified manure (AM) (37% and 23.2%) and Sulphur (SP) (16% and 3.1%) respectively relative to untreated soil. In addition, Cu bioavailability was reduced by 51%, 34%, 16.6%, and 7.4% when SB, CP, AM, and SP were incorporated in wastewater irrigated polluted soil. The Cu adsorption isotherm results also revealed that SB treated soil has great potential to increase Cu adsorption capacity by 223 mg g− 1 over control 89 mg g− 1. Among all the treatments, SB and CP were considered suitable candidates for the restoration of Cu polluted alkaline nature soil.

Objective: To compare fistulectomy and ligation of inter sphincteric tract in patients of fistula in ano in terms of postoperative pain and duration of wound healing. Study Design: Prospective Experimental study. Setting: Department of General Surgery of Faisalabad Medical University and Affiliated Hospitals. Period: 22-01-2021 to 21-07-2021. Material & Methods: Computer-generated random numbers were used to assign the type of treatment (group. A or B). Group A patient underwent treatment with fistulectomy. Group B patient underwent treatment with ligation of inter sphincteric tract (LIFT). Post-operative pain was noted and scored at 12, 24 and 48 hours and after one week on visual analogue scale (VAS). Healing time noted in both groups and patients followed up for 5 months. Results: Out of these 60 study cases, 46 (76.7 %) were male patients while 14 (23.3 %) were female patients. Mean age of our study cases was 43.17 ± 10.77 years. Mean pain score in group A was noted to be 4.77 ± 0.858 while that of group B was noted to be 3.07 ± 1.01 (P<0.001). Mean duration of wound healing in group A was 7 weeks and in group B was 3 weeks(P<0.001). Conclusion: Ligation of inter-sphincteric tract is better than fistulectomy in patients of fistula in ano as it is associated with significant reduction of pain and duration of wound healing.

Task allocation is a fundamental requirement for multi-robot systems working in dynamic environments. An efficient task allocation algorithm allows the robots to adjust their behavior in response to environmental changes such as fault occurrences, or other robots' actions to increase overall system performance. To address these challenges, this paper presents a Task Allocation technique based on a threshold level which is an accumulative value aggregated by a centralized unit using the Task-Robot ratio and the number of the available resource in the system. The threshold level serves as a reference for task acceptance and the task acceptance occurs despite resource shortage. The deficient resources for the accepted task are acquired through an auction process using objective minimization. Despite resource shortage, task acceptance occurs. The threshold approach and the objective minimization in the auction process reduce the overall completion time and increase the system's resource utilization up to 96%, which is demonstrated theoretically and validated through simulations and real experimentation.

Rapid climate change is causing abiotic stress on a mass level, threatening crop production especially in cereals. Frequent climate changes and reappearance of abiotic stresses are the major threats to food security and sustainability for the crop production system. Drought and heat are evident ones among other stresses, causing significant reduction in yields. These stresses have emerged as a major concern in sustainable agriculture, as food demand is increasing with every passing day. In addition to other strategies, we emphasise the use of plant growth regulators for protection against various environmental stress; this is a viable approach to make crop production more resilient to short exposures to drought and heat stresses. In this chapter we will discuss in detail the use of a few plant growth regulators which help in developing crop species that are resilient to climate change, particularly drought and heat stress.

Stroke is a cerebrovascular disease (CVD), which results in hemiplegia, paralysis, or death. Conventionally, a stroke patient requires prolonged sessions with physical therapists for the recovery of motor function. Various home-based rehabilitative devices are also available for upper limbs and require minimal or no assistance from a physiotherapist. However, there is no clinically proven device available for functional recovery of a lower limb. In this study, we explored the potential use of surface electromyography (sEMG) as a controlling mechanism for the development of a home-based lower limb rehabilitative device for stroke patients. In this experiment, three channels of sEMG were used to record data from 11 stroke patients while performing ankle joint movements. The movements were then decoded from the sEMG data and their correlation with the level of motor impairment was investigated. The impairment level was quantified using the Fugl-Meyer Assessment (FMA) scale. During the analysis, Hudgins time-domain features were extracted and classified using linear discriminant analysis (LDA) and artificial neural network (ANN). On average, 63.86% ± 4.3% and 67.1% ± 7.9% of the movements were accurately classified in an offline analysis by LDA and ANN, respectively. We found that in both classifiers, some motions outperformed others (p < 0.001 for LDA and p = 0.014 for ANN). The Spearman correlation (ρ) was calculated between the FMA scores and classification accuracies. The results indicate that there is a moderately positive correlation (ρ = 0.75 for LDA and ρ = 0.55 for ANN) between the two of them. The findings of this study suggest that a home-based EMG system can be developed to provide customized therapy for the improvement of functional lower limb motion in stroke patients.

Wastewater irrigation in croplands is recognized as one of the major threat, seriously affecting soil health and sustainable agriculture around the globe. Heavy metals presence especially cadmium (Cd) in wastewater irrigated soils is posing serious physiological and morphological disorder in plants due to its highest mobility. Therefore, metals mobility in wastewater irrigated soils can be curtailed through eco-friendly and cost effective organic soil amendments compost (CP), press mud (PM) and moringa leaf extract (ME) at 3% rate that eventually reduces its translocation from polluted soil to plant. This study explored the possible effects of various types of organic soil amendments on Cd phytoavailability in wastewater degraded soil and its subsequent accumulation in maize tissues. Maize plant was grown in Ghazi University as a test plant and Cd accumulation was recorded in its tissues, translocation from root to shoot, chlorophyll contents, plant biomass, yield and soil properties (pH, NPK, OM and Soluble Cd) were also examined. Results revealed that the addition of amendments significantly minimized Cd mobility in soil by 45.8%, 23% and 19.4% when CP, PM and ME were added at 3% over control. Comparing the control soil, Cd uptake effectively reduced via plants shoots by 33.3%, 27.7% and 19.4% when CP, PM and ME. In addition, NPK were significantly increased among all the added treatments in the soil-plant system as well as improved chlorophyll contents relative to non-treated soil. The Current study suggested that among all the amendments, compost at 3% rate performed well and can be considered a suitable approach for maize growth in polluted soil.

Analysis of visual cues for fruit classification and sorting allows to automate the visual inspection and packaging process in agricultural applications that is performed so far by human workers. Challenges for automated multi class sorting systems are similarity in color and shape of different fruit varieties and variation among the same category of fruit. A major constraint in using well known deep neural networks for fruit classification arises because deep neural networks require large training datasets for achieving high accuracies which are generally not available in case of agricultural products especially various fruits and vegetable varieties. A thorough analysis is required to find an appropriate combination of various handcrafted features that could give precise and accurate classification results for small datasets. This paper investigates the use of various handcrafted visual features for fruit classification using traditional machine learning techniques. Different color, shape and texture features are analyzed by comparing the results obtained from six supervised machine learning techniques including K nearest neighbors, Support Vector Machines, Naive Bayes, Linear Discriminant Analysis, Decision Trees and Feed forward back propagation neural network. We propose a novel combination of Hue, Color-SIFT, Discrete Wavelet Transform and Haralick features in fruit classification problem that outperforms other handcrafted visual features. This feature combination is found to be invariant to rotation and illumination effects and works well with intra class variations providing good results for identifying subcategories of fruits along with high classification accuracies obtained for difficult fruit categories that are visually similar. It is found that Color SIFT features alone work very well for fruit classification problem by outperforming other individual handcrafted features. Our approach is trained and tested on publicly available Fruits 360 dataset. Out of different classifiers best results are obtained using Back Propagation Neural Network, SVM and KNN classifier with classification accuracies between 99% and 100%.

Soil amendment with two types of composts: animal manure (AC) and vegetable waste (VC) induced composts have potential to alleviate Cd toxicity to maize in contaminated soil. Therefore, Cd mobility in waste water irrigated soil can be addressed through eco-friendly and cost effective organic soil amendments AC and VC that eventually reduces its translocation from polluted soil to maize plant tissues. The comparative effectiveness of AC and VC at 3% rate were evaluated on Cd solubility, its accumulation in maize tissues, translocation from root to shoot, chlorophyll contents, plant biomass, yield and soil properties (pH, NPK, OM). Results revealed that the addition of organic soil amendments significantly minimized Cd mobility and leachability in soil by 58.6% and 47%, respectively in VC-amended soil over control. While, the reduction was observed by 61.7% and 57%, respectively when AC was added at 3% over control. Comparing the control soil, Cd uptake effectively reduced via plants shoots and roots by 50%, 46% respectively when VC was added in polluted soil. However, Cd uptake was decreased in maize shoot and roots by 58% and 52.4% in AC amended soil at 3% rate, respectively. Additionally, NPK contents were significantly improved in polluted soil as well as in plant tissues in both composts amended soil Comparative to control, the addition of composts significantly improved the maize dry biomass and chlorophyll contents at 3% rate. Thus, present study confirmed that the addition of animal manure derived compost (AC) at 3% rate performed well and might be consider the suitable approach relative to vegetable compost for maize growth in polluted soil.

Agricultural production is vital for the stability of the country's economy. Controlling weed infestation through agrochemicals is necessary for increasing crop productivity. However, its excessive use has severe repercussions on the environment (damaging the ecosystem) and the human operators exposed to it. The use of Unmanned Aerial Vehicles (UAVs) has been proposed by several authors in the literature for performing the desired spraying and is considered safer and more precise than the conventional methods. Therefore, the study's objective was to develop an accurate real-time recognition system of spraying areas for UAVs, which is of utmost importance for UAV-based sprayers. A two-step target recognition system was developed by using deep learning for the images collected from a UAV. Agriculture cropland of coriander was considered for building a classifier for recognizing spraying areas. The developed deep learning system achieved an average F1 score of 0.955, while the classifier recognition average computation time was 3.68 ms. The developed deep learning system can be deployed in real-time to UAV-based sprayers for accurate spraying.

Cadmium contamination in croplands is recognized as one of the major threats, seriously affecting soil health and sustainable agriculture around the globe. Cd mobility in wastewater irrigated soils can be curtailed through eco-friendly and cost effective organic soil amendments compost (CP), press mud (PM) and moringa leaf extract (ME) at 3% rate that eventually reduces its translocation from polluted soil to plant. This study explored the possible effects of various types of organic soil amendments on cadmium (Cd) phytoavailability in wastewater degraded soil and its subsequent accumulation in maize tissues. Maize plant was grown in Ghazi University as a test plant and Cd accumulation was recorded in its tissues, translocation from root to shoot, chlorophyll contents, plant biomass, yield and soil properties (pH, NPK, OM and Soluble Cd) were also examined. Results revealed that the addition of amendments significantly minimized Cd mobility in soil by 45.8%, 23% and 19.4% when CP, PM and ME were added at 3% over control. Comparing the control soil, Cd uptake effectively reduced via plants shoots by 33.3%, 27.7% and 19.4% when CP, PM and ME. In addition, NPK were significantly increased among all the added treatments in the soil-plant system as well as improved chlorophyll contents relative to non-treated soil. The Current study suggested that among all the amendments, compost at 3% rate performed well and might be considered a suitable approach for maize growth in polluted soil.

Experimentation and analysis of Functional near-infrared spectroscopy (fNIRS) in Brain-Computer Interface (BCI) has increasingly been studied as a communication possibility for patients who are severely paralyzed. This study has applied this technique to distinguish brain activities during four different mental tasks. These tasks include Mental Arithmetic (MA), Motor Imagery of Left-Hand (LHMI) and Right-Hand (RHMI) and Rest. fNIRS data used is from an open access dataset of 29 individuals which was collected by Continuous-wave imaging system (NIR Scout). In this research Data integration is performed before the data is preprocessed. Usual preprocessing is done using Butterworth filter to minimize or eliminate any unwanted signal distortion. After that an extensive signal analysis is done in which six different statistical features (Signal Mean (SM), Skewness (SK), Kurtosis (KR), Standard Deviation (SD), Signal Peak (SP), and Signal Variance (SV)) are obtained in the time domain and 13 Mel Frequency Cepstral Coefficients (MFCC) features are obtained from the frequency domain. As per literature review, MFCC has never been used as feature towards classification of fNIRS signal, which is a novel contribution towards this study. Separate Classification analysis is performed on each domain features. We were able to compare, differentiate and distinguish the brain signal activities captured while performing four different tasks using three different classifiers i.e. Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) and K Nearest Neighbor (KNN). The average classification accuracy of 90.54% is achieved from K Nearest Neighbors (KNN) using the time domain features and accuracy achieved from Support Vector Machine (SVM) using the frequency domain features is 95.7%. Comparison with benchmark study shows the efficiency of MFCC as suitable features for improved classification accuracy.

Drought is one of the major and most detrimental abiotic stresses, and uncertainty in precipitation pattern further kindled the situation. Based upon the serious threat, we speculated that to reduce time to see required results, screening of different genotypes at a very early development stage can come up with the identification of potential genotypes and can be used for further breeding. Therefore, this study was conducted to screen ten wheat genotypes viz; V0-7096, V0-7076, V0-5082, V0-5066, Sehar-06, Inqlab-91, FSD-08, Lasani-08, Chakwal-50 and AARI-11 for drought resistance. Firstly, under laboratory conditions, these genotypes were subjected to drought stress in Petri plates by application of polyethylene glycol (PEG-6000) solution maintaining − 0.17, − 0.32, − 0.47 and –0.62 MPa osmotic potential. Following this, pot experiment in controlled glass house was conducted at 25%, 50% and 85% field capacity (FC) to further examine the response of wheat genotypes under drought stress. The present study concluded that at the osmotic potential of − 0.62 MPa, Chakwal-50 performed better and attained highest values of emergence index (EI) 27.16%, mean emergence time (MET) 6%, promptness index (PI) 6.50%, germination percentage (GP) 65%, germination stress tolerance index (GSI) 74.35%, plant height stress tolerance index (PHSI) 85.76%, root length stress tolerance index (RLSI) 124.90% and dry matter stress tolerance indices (DMSI) 90.39%, while Sehar-06 showed the lowest values in these traits and showed reduction of 22.0%, 4.20%, 4.33%, 30%, 50.12%, 47.71%, 71.23% and 26.90% respectively as compared to control. Remaining wheat genotypes were intermediate in tolerating drought stress. Overall research study concluded that under drought stress conditions, Chakwal-50 performed best than all other wheat genotypes and could be used for further investigation to develop drought-resistant wheat genotype for maximum production.

Climate change scenarios predict that an extended period of drought is a real threat to food security, emphasizing the need for new crops that tolerate these conditions. Quinoa is the best option because it has the potential to grow under water deficit conditions. There is considerable variation in drought tolerance in quinoa genotypes, and the selection of drought-tolerant quinoa germplasms is of great interest. The main goal of this work is to evaluate the crop yield and characterize the physiology of 20 quinoa genotypes grown under water deficit in a wirehouse. The experiment was a complete randomized design (CRD) factorial with three replications. Seedling growth, i.e., fresh weight (FW), dry weight (DW), root length (RL), shoot length (SL), relative growth rate of root length (RGR-RL), shoot length (RGR-SL), and physiological performance, i.e., chlorophyll content (a and b), carotenoid, leaf phenolic content, leaf proline content, membrane stability index (MSI), and leaf K+ accumulation were evaluated in a hydroponic culture under different water-deficit levels developed by PEG 6000 doses (w/v) of 0% (control), 0.3%, and 0.6%. Yield attributes were evaluated in a pot at three different soil moisture levels, as determined by soil gravimetric water holding capacity (WHC) of 100 (control), 50% WHC (50 % drought stress) and 25% WHC (75% stress). In both experiments, under the water stress condition, the growth (hydroponic study) and yield traits (pot study) were significantly reduced compared to control treatments. On the drought tolerance index (DTI) based on seed yield, genotype 16 followed by 10, 1, 4, 5, 7, and 12 could be considered drought-tolerant genotypes that produced maximum grain yield and improved physiological characteristics under severe water stress conditions in hydroponic culture. In both studies, genotypes 3, 8, 13, and 20 performed poorly and were considered drought-sensitive genotypes with the lowest DTI values under water-stressed conditions. All the studied agronomic traits (grain yield, root and shoot length, shoot fresh and dry weights) and physiological traits (leaf phenolic, proline content, carotenoid, K+ accumulation, membrane stability index, and relative water content) were firmly inter-correlated and strongly correlated with DTI. They can be regarded as screening criteria, employing a large set of quinoa genotypes in a breeding program.

Nitrogen application rates and plant density are vital factors that influence cotton production considerably. The aim of the experiment was to study the effect of varied nitrogen (N) rate and planting densities (PD) on growth and yield performance of two cotton cultivars from different origins. The research was laid out in Randomized Complete Block Design (RCBD) with split plot arrangements. There were two nitrogen levels; low N level (F1 with 120 kg ha−1) and high N level (F2 with 180 kg ha−1) with three plant densities; 8 plants m−2 as low plant density (LPD), 10 plants m−2 as medium plant density (MPD) and 12 plants m−2 as high plant density (HPD). During this study we observed the interactive effect of N application levels and PD on cotton growth, yield performance. Results showed that FH-142 took more number of days to reach maturity as compared with Huamian-3109. Cotton plant dry biomass and crop growth rate (CGR) was also considerably influenced by N and PD levels. FH-142 produced maximum dry biomass under F1 with HPD and F2 with MPD respectively while least plant dry biomass production was noted under F1 with LPD. High CGR was noted in FH-142 under F2 with MPD. Another side, Huamian-3109 showed maximum plant dry biomass only under F1 with HPD. Least plant dry biomass production was noted under F1 with LPD. Higher total yield produced by FH-142 under F2 with MPD while Huamian-3109 produced similar and relatively higher seed cotton yield and lint yield in F1 with HPD and F2 with MPD. These combinations were recommended for better production of both cotton cultivars in agro climatic conditions of Pakistan.

In the current study, populations of whitefly (Bemisia tabaci) and thrips (Thrips tabaci) were observed in 2016 under three different conditions: 75 cm row to row spacing without mapiquate chloride (growth inhibitor), 30 cm row to row spacing without mapiquate chloride, and 30 cm row to row spacing with mapiquate chloride, using two cotton varieties (BS-15 and BS-70). The results indicated that the population of both sucking pests was more on variety BS-70 as compared to BS-15 cotton variety. Thrips and whitefly populations varied significantly in three row spacings (P<0.01) with the maximum population recorded in line spacing of 30 cm without mapiquate chloride (9.12-11.15 thrips/leaf and 9.18-7.83 whitefly/leaf), followed by line spacing of 75 cm without mapiquate chloride (8.65-9.12 thrips/ leaf and 5.97-5.06 whitefly/leaf) and 30 cm with mapiquate chloride (4.57-5.41 thrips/leaf and 2.64-2.88 whitefly/leaf). The peak population was observed on June 5, 2016 for thrips (15.46-27.53 nymphs and adults/leaf) and August 29, 2016 for whitefly (11.40-20.80 nymph and adults/leaf).

Nitrogen (N) affects all levels of plant function from metabolism to resource allocation, growth, and development and Magnesium (Mg) is a macronutrient that is necessary to both plant growth and health. Radish (Raphanus sativus L.) occupies an important position in the production and consumption of vegetables globally, but there are still many problems and challenges in its nutrient management. A pot trial was conducted to investigate the effects of nitrogen and magnesium fertilizers on radish during the year 2018–2019. Nitrogen and magnesium was applied at three rates (0, 0.200, and 0.300 g N kg−1 soil) and (0, 0.050, and 0.100 g Mg kg−1 soil) respectively. The experiment was laid out in a completely randomized design (CRD) and each treatment was replicated three times. Growth, yield and quality indicators of radish (plant height, root length, shoot length, plant weight, total soluble sugar, ascorbic acid, total soluble protein, crude fiber, etc.) were studied. The results indicated that different rates of nitrogen and magnesium fertilizer not only influence the growth dynamics and yields but also enhances radish quality. The results revealed that the growth, yield and nutrient contents of radish were increased at a range of 0.00 g N. kg−1 soil to 0.300 g N. kg−1 soil and 0.00 g Mg. kg−1 soil to 0.050 g Mg. kg−1 soil and then decreased gradually at a level of 0.100 g Mg. kg−1 soil. In contrast, the crude fiber contents in radish decreased significantly with increasing nitrogen and magnesium level but increased significantly at Mg2 level (0.050 g Mg. kg−1 soil). The current study produced helpful results for increasing radish quality, decreasing production costs, and diminishing underground water contamination.