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Abstract Wheat is an important staple crop not only in Pakistan but all over the globe. Although the area dedicated to wheat cultivation expands annually, the quantity of wheat harvested is declining due to various biotic and abiotic factors. Global wheat production and output have suffered as a result of the drought, which is largely driven by a lack of water and environmental factors. Organic fertilizers have been shown to reduce the severity of drought. The current research was conducted in semi-arid climates to mitigate the negative effects of drought on wheat during its critical tillering (DTS), flowering (DFS), and grain filling (DGFS) stages through the application of three different abscisic acid treatments: ABA0 (0 mgL−1) control, ABA1 (100 mgL−1) and ABA2 (200 mgL−1). Wheat growth and yield characteristics were severely harmed by drought stress across all critical development stages, with the DGFS stage being particularly vulnerable and leading to a considerable loss in yield. Plant height was increased by 24.25%, the number of fertile tillers by 25.66%, spike length by 17.24%, the number of spikelets per spike by 16.68%, grain count per spike by 11.98%, thousand-grain weight by 14.34%, grain yield by 26.93% and biological yield by 14.55% when abscisic acid (ABA) was applied instead of the control treatment. Moreover, ABA2 increased the more physiological indices (water use efficiency (36.12%), stomatal conductance (44.23%), chlorophyll a (24.5%), chlorophyll b (29.8%), transpiration rate (23.03%), photosynthetic rate (24.84%), electrolyte leakage (− 38.76%) hydrogen peroxide (− 18.09%) superoxide dismutase (15.3%), catalase (20.8%), peroxidase (− 18.09%), and malondialdehyde (− 13.7%)) of drought-stressed wheat as compared to other treatments. In the case of N, P, and K contents in grain were maximally improved with the application of ABA2. Through the use of principal component analysis, we were able to correlate our results across scales and provide an explanation for the observed effects of ABA on wheat growth and production under arid conditions. Overall, ABA application at a rate of 200 mgL−1 is an effective technique to boost wheat grain output by mitigating the negative effects of drought stress.

The development of seedless cultivars is a primary goal in grapevine breeding. Since grapes are stenospermic and tend to abort their embryos before development, traditional breeding methods often yield seedless cultivars at a low frequency. Therefore, embryo rescue has emerged as a promising approach for creating seedless grape cultivars. This study aimed to optimize the ideal sampling time and protocol for efficient embryo rescue in grapes. Ovules from immature berries collected at various days after pollination (20DAP, 30DAP, 40DAP, and 50DAP) were cultured on Nitsch and Nitsch (NN) medium with different concentrations of Benzyl aminopurine (BAP) (0.1, 0.2, and 0.5 mg/L) and Activated Charcoal (AC) (1.5, 2, and 2.5 g/L). Several parameters were assessed, including maximum ovule growth, the percentage of enlarged ovules, percentage of collapsed ovules, callus formation percentage, and embryo germination percentage. The study’s results indicated that berries collected at 40 DAP yielded the best outcomes across all parameters. Regarding treatments, the most favorable results were achieved when ovules were cultured on NN medium supplemented with 0.5 mg/L BAP and 2 g/L AC. In conclusion, the study underscores the significance of choosing the right sampling time and treatments to ensure efficient embryo rescue in grapes. The protocol standardized from this research is recommended for effectively rescuing embryos and developing seedless hybrids.

Abstract Chilli peppers are widely consumed for their pungency, as used in flavoring the food and has many pharmaceutical and medicinal properties. Based on these properties an experiment was held using 83 varieties of chilli (Hot pepper and sweet pepper) were grown in suitable environment using Augment Block design and evaluated for fruit pungency and phytochemical contents using high proficiency liquid chromatography. Analysis of variance (ANOVA) of traits showed highly significant for all traits except for fruit length and capsaicin contents. The value of Least significant increase (LSI)was ranged 0.27-1289.9 for all traits showed high variation among varieties. Highly significant correlation was found among fruit diameter to fruit weight 0.98, while moderate to high correlation was present among all traits. The most pungent genotype 24,634 was 4.8 g in weight, while the least pungent genotypes i.e. PPE-311 (32.8 g), green wonder (40.67) had higher in weight. The genotypes 24,627, 32,344, 32,368 and 1108 marked as higher number of seeds in their placental region. It was observed that chilli genotype 24,621 had maximum length with considerable high amount of pungency act as novel cultivar. Principal component analysis (PCA) showed the high variability of 46.97 for two PCs with the eigen value 2.6 and 1.63 was recorded. Biplot analysis showed a considerable variability for fruit pungency, while huge variability was found for all traits among given varieties. PPE-311, T5 and T3 are found as highly divergent for all traits. The findings of this study are instrumental for selecting parents to improve desirable traits in future chilli pepper breeding programs. It will help plant/vegetable breeders for development of highly nutrient and pungent varieties and attractive for the consumer of food sector.

Objective: The current study aimed to observe hypertension educational intervention's effect on general physicians (GPs) to improve blood pressure control and patient outcomes indirectly. Methods: This randomized control trial includes 42 GPs divided into 2 groups. GPs in group 1 receive face-to-face education with structured educational material on hypertension management strategies by a senior cardiologist. GPs in group 2 receive the print version of education material. The data was collected from six major cities in Pakistan. GPs with at least three years of experience in the broad primary care disciplines, with ages above 18 years, were included in the study. Results: A total of 42 physicians (21 from each group) completed questionnaires, while out of 420 hypertension patients, 105 newly diagnosed and already diagnosed patients enrolled under physicians of both groups. The educational material did just as well at informing clinicians as the face-to-face group intervention did and both the interventions had a significant effect on knowledge and BP control. Conclusion: After the 3-month follow-up, both interventions, including face-to-face and educational approaches, demonstrated significant effectiveness in improving knowledge and blood pressure control. Innovation: The study shows that hypertension educational intervention's effect on general physicians indirectly improves blood pressure control and patient outcomes. And emphasize for developing a hypertension educational program targeted at general physicians.

Detecting software requirements defects is crucial in reducing the risk of software project failures. Existing methods for automatic detection, especially during requirements changes, are limited in coverage and often lack robust tool support. Addressing this gap, we define the four most common types of requirements defects (incompleteness, inconsistency, redundancy, and ambiguity) and present algorithms for their detection. We propose a novel behaviour engineering-based approach, translating software requirements into behaviour trees and then into the Web Ontology Language (OWL). We developed ‘requirements defects identifier’, a tool that queries the OWL knowledge base to identify potential defects during requirements analysis and change. Validated on three final-year student projects, our approach demonstrated success in detecting all four defect types, offering broader coverage compared to existing tools. A real-world case study has been used to validate the applicability of the proposed approach. Our experiments demonstrate that the tool can successfully detect all four different types of requirement defects during both requirements analysis and requirements change.

Flexible and environmentally safe electrodes are high in demand for modern energy storage devices. This study reports the fabrication of cerium oxide (CeO2) and cerium oxide-manganese oxide nanoparticles (Ce0.98 Mn0.02 O2 NPs) via co-precipitation synthesis technique and further development of bendable paper-based energy storage electrodes by employing lignocelluloses fibers as binder, extracted from self-growing plant, Monochoria Vaginalis. The structural and optical properties of the prepared products are carried out by X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FTIR). XRD study reveals that Mn ions are incorporated into the CeO2 lattice to synthesize the Ce0.98 Mn0.02 O2 nanoparticles. SEM/EDX and TEM/SAED are performed for surface morphological analysis and successful composite formation of CeO2/LC and Ce0.98 Mn0.02O2/LC sheets. Electrochemical measurements of CeO2/LC and Ce0.98 Mn0.02O2/LC composite sheets were performed where fabricated sheets were employed as freestanding working electrode in 2 M KOH electrolyte. CeO2/LC and Ce0.98 Mn0.02O2/LC composite sheets have shown specific capacitances of 401.5 and 544 F/g, respectively from three electrode cyclic voltammetry (CV) measurements. 2-electrodes galvanostatic charge–discharge (GCD) measurements were performed in symmetric cell which reveal the capacitance of 436 F/g and 560 F/g for CeO2/LC and Ce0.98 Mn0.02O2/LC composite paper sheets, respectively. The Ce0.98 Mn0.02O2/LC (1:1) composite paper sheet shows the capacitance retention stability up to 80% after 1000 cycles at applied current density of 3.3 A/g. It can conclude that the fabricated paper electrodes can employ as working electrodes in modern energy storage devices having dual advantages of flexible and environmentally safe characteristics in the absence of any synthetic substrate/binder.

This paper describes a method for sensor pose estimation, as well as creating large-scale 3D maps, for construction cranes equipped with a sensor system consisting of a camera, 2D lidar, and IMU. To tackle the challenges posed by the crane boom’s complex motion, we utilize an Extended Kalman filter (EKF) to improve the accuracy and reliability of sensor pose estimation. By combining pose estimates from Visual-Inertial Navigation System (VINS) with data from an additional IMU, we estimate the scale value of a monocular camera. This scale value, obtained from the EKF, is then integrated into the VINS algorithm to refine the previously estimated scale value. Slowly rotating 2D lidar is used to build a 3D map. Since there is limited overlap between 2D lidar scans, we leverage the estimated pose to align and construct a comprehensive 3D map. Additionally, we thoroughly evaluate the effectiveness of the latest VINS techniques, as well as the EKF-enhanced VINS approach, in the specific context of crane operations. Through comprehensive performance assessments conducted in both simulated and real environments, we compare the EKF-added VINS method with state-of-the-art VINS techniques. The evaluation results demonstrate that the EKF-added VINS method accurately estimates sensor poses, leading to the generation of high-quality, large-scale 3D point cloud maps for construction cranes.

Glioma is the most occurring brain tumor in the world. Its grade (level of severity) identification, crucial in its treatment planning, is most demanding in a clinical environment. Computer-aided methods have been experimented with to identify the grade of glioma, out of which deep learning-based methods, due to their auto features engineering, have a good impact in terms of their achieved outcomes. In this study, convolutional neural networks (CNNs) have been explored and utilized for the classification of glioma grading, for example, low grade (grade I-II) and high grade (grade III-IV). A CNN-based model, which is light-weighted in terms of layers, size, and learnable parameters, has been proposed. Experimental tests were carried out on benchmarked publicly available datasets, for example, Brats-2017, Brats-2018, & Brats-2019. A locally developed dataset from Bahawal Victoria Hospital, Bahawalpur, Pakistan, has also been employed for experimentation and research to cross-validate the outcomes. Additionally, experiments have been carried out to compare the effectiveness of the proposed model, and results have been compared with the results of state-of-the-art pertained CNN models, i.e., resnet18, squeezenet, and alexnet. The proposed model achieved maximum standard evaluation measures on the benchmarked dataset, i.e., accuracy, specificity, and sensitivity at 97.85%, 98.88%, and 99.88%, respectively. Similarly, these measures were 98.89%, 99.28%, and 99.77% on a locally developed dataset, which is the best compared to the recent state-of-the-art related studies.

Plethora of image encryption schemes exist in literature based on the construct of magic square for realizing the purpose of image obfuscation. This magic square carries out the scrambling project of the encryption. In these schemes, normally single and static magic square is implied. To render greater scrambling effects, this study proposes a novel image encryption scheme using all order-4 magic squares whose frequency reaches to the tune of 880. These magic squares have been dynamically selected to carry out the scrambling project. As the color image is input, it is broken into its gray scale red, green and blue components. These components are joined together to make a big gray scale image. Intertwining logistic map (ILM) has been used for the generation of random data. Besides, one more stream has been created through the arithmetic manipulation of the generated three streams. Streams generated by ILM has been used to realize the effects of confusion and diffusion. First and second streams out of the four streams randomly select the address from the big gray scale image to apply the randomly selected magic square by the third stream, in order to create the scrambling effects. The fourth and last stream of random numbers is used to create the diffusion effects in the scrambled image. Plaintext senstivity has been introduced by tempering the one initial value of the chaotic system through the usage of a characteristic of the given input color image. The experimentation and security analyses sections vividly demonstrate the strength, immunity from the diverse attacks and prospects for the real world application of the proposed image cipher. In particular, we got very promising stats of information entropy (7.9974) and computational time (0.9865 seconds). No doubt, they suggest the potential application of the proposed image cipher in some real world setting.

This paper describes a novel method for large-scale 3D mapping for construction cranes with an arbitrary motion of the sensor system (2D lidar and IMU) attached to the crane boom. A heavy lidar with a slowly rotating base is needed to make a large-scale map both vertically and horizontally for cranes. This sensor configuration and mapping conditions entail handling each 2D scan separately, making it difficult to adopt existing rotating 2D lidar-based methods that construct a virtual 3D scan from a set of 2D scans. In the proposed method, we introduce a complementary filter with moving average filtering for lidar pose estimation, which is more robust to severe vibration than Kalman filter-based methods. As there are only a small amount of overlaps between 2D lidar scans, we propose a map correction method based on a pose graph optimization with planar environmental constraints. We evaluate the proposed method in a simulation and a small-sized real environment and compare it with one of the state-of-the-art methods. The evaluation results reveal that the proposed method can accurately estimate the sensor poses, thereby generating a high-quality, large-scale 3D point cloud map.

A vehicular ad hoc network (VANET) is a sophisticated wireless communication infrastructure incorporating centralized and decentralized control mechanisms, orchestrating seamless data exchange among vehicles. This intricate communication system relies on the advanced capabilities of 5G connectivity, employing specialized topological arrangements to enhance data packet transmission. These vehicles communicate amongst themselves and establish connections with roadside units (RSUs). In the dynamic landscape of vehicular communication, disruptions, especially in scenarios involving high-speed vehicles, pose challenges. A notable concern is the emergence of black hole attacks, where a vehicle acts maliciously, obstructing the forwarding of data packets to subsequent vehicles, thereby compromising the secure dissemination of content within the VANET. We present an intelligent cluster-based routing protocol to mitigate these challenges in VANET routing. The system operates through two pivotal phases: first, utilizing an artificial neural network (ANN) model to detect malicious nodes, and second, establishing clusters via enhanced clustering algorithms with appointed cluster heads (CH) for each cluster. Subsequently, an optimal path for data transmission is predicted, aiming to minimize packet transmission delays. Our approach integrates a modified ad hoc on-demand distance vector (AODV) protocol for on-demand route discovery and optimal path selection, enhancing request and reply (RREQ and RREP) protocols. Evaluation of routing performance involves the BHT dataset, leveraging the ANN classifier to compute accuracy, precision, recall, F1 score, and loss. The NS-2.33 simulator facilitates the assessment of end-to-end delay, network throughput, and hop count during the path prediction phase. Remarkably, our methodology achieves 98.97% accuracy in detecting black hole attacks through the ANN classification model, outperforming existing techniques across various network routing parameters.

Mobile cloud computing (MCC) provides resources to users to handle smart mobile applications. In MCC, task scheduling is the solution for mobile users’ context-aware computation resource-rich applications. Most existing approaches have achieved a moderate service reliability rate due to a lack of instance-centric resource estimations and task offloading, a statistical NP-hard problem. The current intelligent scheduling process cannot address NP-hard problems due to traditional task offloading approaches. To address this problem, the authors design an efficient context-aware service offloading approach based on instance-centric measurements. The revised machine learning model/algorithm employs task adaptation to make decisions regarding task offloading. The proposed MCVS scheduling algorithm predicts the usage rates of individual microservices for a practical task scheduling scheme, considering mobile device time, cost, network, location, and central processing unit (CPU) power to train data. One notable feature of the microservice software architecture is its capacity to facilitate the scalability, flexibility, and independent deployment of individual components. A series of simulation results show the efficiency of the proposed technique based on offloading, CPU usage, and execution time metrics. The experimental results efficiently show the learning rate in training and testing in comparison with existing approaches, showing efficient training and task offloading phases. The proposed system has lower costs and uses less energy to offload microservices in MCC. Graphical results are presented to define the effectiveness of the proposed model. For a service arrival rate of 80%, the proposed model achieves an average 4.5% service offloading rate and 0.18% CPU usage rate compared with state-of-the-art approaches. The proposed method demonstrates efficiency in terms of cost and energy savings for microservice offloading in mobile cloud computing (MCC).

The intelligent transportation system (ITS) relies heavily on the vehicular ad hoc network (VANET) and the internet of vehicles (IoVs), which combine cloud and fog to improve task processing capabilities. As a cloud extension, the fog processes’ infrastructure is close to VANET, fostering an environment favorable to smart cars with IT equipment and effective task management oversight. Vehicle processing power, bandwidth, time, and high-speed mobility are all limited in VANET. It is critical to satisfy the vehicles’ requirements for minimal latency and fast reaction times while offloading duties to the fog layer. We proposed a fuzzy logic-based task scheduling system in VANET to minimize latency and improve the enhanced response time when offloading tasks in the IoV. The proposed method effectively transfers workloads to the fog computing layer while considering the constrained resources of car nodes. After choosing a suitable processing unit, the algorithm sends the job and its associated resources to the fog layer. The dataset is related to crisp values for fog computing for system utilization, latency, and task deadline time for over 5000 values. The task execution, latency, deadline of task, storage, CPU, and bandwidth utilizations are used for fuzzy set values. We proved the effectiveness of our proposed task scheduling framework via simulation tests, outperforming current algorithms in terms of task ratio by 13%, decreasing average turnaround time by 9%, minimizing makespan time by 15%, and effectively overcoming average latency time within the network parameters. The proposed technique shows better results and responses than previous techniques by scheduling the tasks toward fog layers with less response time and minimizing the overall time from task submission to completion.

OBJECTIVE: To determine the prevalence of hypertension and associated factors among age 15 years and above in federally administered tribal areas (FATA) of Pakistan. METHODS: This cross-sectional study was conducted from July 2016 till August 2017 among 729 individuals including both males and females of age 15 years and above were randomly selected from FATA. FATA was divided into 7 areas (Khyber, Mohmand, Bajaur, Orakzai, Kurram, North and south Waziristan). A pre-tested questionnaire in local language with informed consent was taken from 729 respondents who were selected for the study after sample size calculation. Fasting blood was analyzed for sugar, cholesterol. Blood pressure was recorded among both gender. RESULTS: Among 729 individual prevalence of hypertension (≥140/90 mm Hg) was 49.1% (n=358). The prevalence of blood cholesterol 200 mg/dl and above was 156(21.4%). Fasting blood glucose ≥126 mg/dl was 4.7% (n=134). Anxiety and depression was 50.3% (n=367). BMI ≥25 kg/m² was 47.7% (n=347). Current smoker were 19.8% (n=144). Snuff user were 180 (24.7%). Cannabis user were 4.8% (n=35). Family history of Hypertension, increase BMI and male or female age more than 40 years were significantly associated factors for hypertension. CONCLUSION: There was a high prevalence of elevated blood pressure in FATA above the age of 15 and above when compared with local data. Both male and female with age more than 40 years, increase BMI and family history of hypertension were significant associated with hypertension in FATA population.

In this paper, we study the well known Zakhrov equation (ZE) which states to the Langmuir waves propagation in the ionized plasma. The exact soliton solutions of ZE are extracted using new extended direct algebraic method (EDAM). The obtained solutions are given in the form of trigonometric, rational and hyperbolic functions. Moreover, some of the obtained solutions are illustrated graphically to establish the physical nature of the selected solutions. The existence of these exact solutions is guaranteed by the constraints on the used parameters. It is noted that the new EDAM is easy and efficient technique and it can be useful to many other non-linear partial differential equations (PDEs).

Peanut, the only cash crop of rainfed areas of Pakistan, is facing immense challenges due to global warming. Climatic factors particularly the temperature fluctuations and rain pattern shift significantly impact the production and yield of peanut and unavailability of resilient varieties exacerbate this impact. To deal with the cropping pattern change and yield losses, due to climate vagaries, a study was conducted to develop early maturing hybrids using line into tester mating design. The F1 hybrids from the parental lines were produced in the year 2018 using Line × Tester mating design and then grown in the field in the year 2019 for further evaluation. The hybrids were evaluated based on the early maturity and yield-related attributes in comparison with the parental lines. Based on the general combining ability estimate, line V-3 (Golden), was found as best parent with highly significant values for plant height, days to peg formation, days to maturity, number of pegs per plant, number of pods per plants, number of seeds per plant, 100 pod weight 100 seed weight. Similarly, tester V-7 (PI 635006 01 SD) showed highly significant results of GCA for days to germination, day to 50% flowering, plant height, days to peg formation, days to maturity, number of pegs per plant, number of pods per plants, number of seeds per plant, 100 kernel weight, shelling percentage. All the combinations were evaluated for specific combining ability and significant results were observed for V-3 × V-4 (Golden × PI 619175 01 SD) and V-1 × V-6 (BARI-2000 × PI 564846 01 SD) by developing or maturity and yield-related attributes. The hybrid combinations V-3 × V-5 (Golden × PI 635006 01 SD) followed by V-3 × V-6 showed highly significant results for mid parent heterosis and better parent heterosis for days to 50% flowering, plant height, days to peg formation, number of pegs, days to maturity, number of mature seeds per plant, shelling ratio, 100 pod weight and 100 kernel weight. These parents and hybrid combinations with early maturity genes and high yield attributes can further be used for the development of short duration variety.

The elimination of position sensors allows sensorless control to minimizing the size, cost, and additional layers of the permanent magnet synchronous motor (PMSM). In this work, a new flux estimator with an active disturbance rejection controller (ADRC) drift-elimination capability is developed in order to accomplish precise flux estimation. The ESO integrated into the ADRC is capable of detecting and compensating each of the inner model’s fluctuation along with external loads disturbances, enabling a control system with a more effective dynamic response and robust regulations. As a result, the drift issue is greatly mitigated, and the stator flux is accurately predicted throughout a broad range of speeds. The proposed flux observer is validated via sensorless speed vector control of a 15 kW IPMSM motor.

Background: To date, no optimal way of organizing and delivering care to hypertensive patients at the primary carer level has been identified, due to which a significant number of patients fail to meet the treatment goals. The current study aims to observe hypertension educational intervention's effect on general physicians (GPs) to indirectly improve blood pressure control and patient outcomes. Methodology: In this randomized control trial, GPs will be divided into 2 groups and both groups will be asked to fill hypertension awareness questionnaire at baseline and, after randomization. GPs of the group I will receive (1 day) face-to-face education with structured educational material on hypertension management strategies. GPs of group II will receive the print version of educational material (structured educational material) on strategies of structured hypertension management. Both groups of GPs will recruit 10 (5 newly diagnosed + 5 already diagnosed patients) from the study site. The patient will be asked to fill hypertension awareness questionnaire at baseline and share a 12-hour blood pressure measurement. GPs and patients will be reassessed after 3 months of intervention. Discussion: The purpose of designing this study is to observe the effect of hypertension educational intervention for general physicians to improve blood pressure control and patient outcomes indirectly. Results of this study may provide a basis for developing a hypertension educational program targeted at general physicians. Trial registration: The trial was registered with ClinicalTrials.gov NCT05038774.

Abstract Node failures are inevitable in wireless sensor networks (WSNs) because sensor nodes in WSNs are miniature and equipped with small and often irreplaceable batteries. Due to battery drainage, sensor nodes can fail at any instance. Moreover, WSNs operate in hostile environments and environmental factors may also contribute to nodes failure. Failure of nodes leads to disruption of inter-node connectivity and might also lead to network partitioning. Failure to communicate with each other and with the base station can compromise the basic operation of the sensor network. For restoration of connectivity, a robust recovery mechanism is required. The existing connectivity restoration mechanisms suffer from shortcomings because they do not focus on energy-efficient operation and coverage-aware mechanisms while performing connectivity restoration. As a result, most of these mechanisms lead to the excessive mobility of nodes, which itself causes the utilization of excessive battery. In this work, we propose a novel technique called smart node relocation (SNR). SNR is capable of detecting and restoring the connectivity caused by either single or multiple node failures. For achieving energy efficiency, SNR relies on transmitting a lesser number of control packets. For achieving the goal of being coverage-aware, it tries to relocate only essential nodes while trying to restore connectivity. By performing extensive simulations, we prove that SNR outperforms the existing approaches concerning multiple performance metrics including but not limited to the total number of packets transmitted, total distance moved for connectivity restoration, the percentage reduction in field coverage.

Salinity is one of the most common factors affecting alfalfa (Medicago sativa L.), and NaCl is one of the main factors of salinity stress which can cause heavy losses in agricultural production in the world. The application of exogenous melatonin (MT) plays a major role in numerous plants against various stress environments. The effects of exogenous MT on the NaCl tolerance of alfalfa treated with the control, 100 µmol L−1 MT, 150 mmol L−1 NaCl, or 150 mmol L−1 NaCl+ 100 µmol L−1 MT were investigated. The results showed that MT increased growth parameters, inhibited chlorophyll degradation and promoted photosynthetic gas exchange parameters (photosynthetic rate, conductance to H2O, and transpiration rate) and stomatal opening under NaCl stress. Osmotic regulation substances such as soluble sugar, proline and glycine betaine were the highest in the NaCl treatment and the second in the NaCl+MT treatment. Nitrogen, phosphorus, potassium, calcium and magnesium were reduced and sodium was increased by NaCl, whereas these levels were reversed by the NaCl+MT treatment. MT inhibited cell membrane imperfection, lipid peroxidation and reactive oxygen species (ROS) accumulation caused by NaCl stress. MT up-regulated the gene expression and activity of antioxidant enzymes and increased the content of antioxidant non-enzyme substances to scavenge excessive ROS in NaCl-treated plants. In addition, all indicators interacted with each other to a certain extent and could be grouped according to the relative values. All variables were divided into PC 1 (89.2 %) and PC 2 (4 %). They were clustered into two categories with opposite effects, and most of them were significant variables. Hence, these findings reveal that exogenous MT alleviates the inhibitory effects of NaCl stress on photosynthesis, stomata opening, osmotic adjustment, ion balance and redox homeostasis, enhancing tolerance and growth of alfalfa. Furthermore, it suggests that MT could be implemented to improve the NaCl tolerance of alfalfa.

Agricultural production is greatly influenced by environmental parameters such as temperature, rainfall, humidity, and wind speed. The accurate information about environmental parameters plays a vital and useful role when making policies for the agriculture sector as well as for other sectors. Pakistan meteorological department observed these environmental parameters at more than 90 stations. The allocation of these monitoring stations is not made systematically correct. This leads to inaccurate predictions for unobserved locations. The study aims to propose a monitoring network by which these prediction errors of the environmental parameters can be minimized. The well-known prediction techniques named, model-based ordinary kriging and model-based universal kriging (UK) with the known Matheron variogram model are used for prediction purposes. We investigate the monitoring network of Pakistan for rainfall and focus on both the optimal deletion/addition of monitoring stations from/to this network. The two stochastic search algorithms, spatial simulated annealing, and genetic algorithm are used for optimization purposes. Furthermore, the minimization of the Average Kriging Variance (AKV) is taken as the interpolation accuracy measure. The spatial simulated annealing exhibits a lower AKV as compared to the Genetic algorithm when adding/removing the optimal/redundant locations from the monitoring network.

Background: One of the most frequently observed valvular heart lesions is Mitral stenosis (MS), characterized by left ventricular inflow tract obstruction at the mitral valve level caused by different etiologies. Early detection of symptomatic mitral stenosis with a thrombus in LAA, not responding to anticoagulation in due course of time are sent for Mitral valve replacement rather than undergoing PTMC. The current study aims to see the anticoagulation response in different types of left atrial appendages so that non-responders can be referred for surgery on the first TEE and Cardiac CT. Methodology: The current study will observe the frequency of left atrial appendage (LAA) thrombus resolution after three months of anticoagulation in patients with severe MS. It will assess the response rate in different morphologies of LAA so that non-responders can be referred for surgery on the very first TEE and Cardiac CT. Consecutive MS patients with thrombus in LAA detected via transesophageal echocardiography will be included in the study. According to the standard procedure, TEE followed by Cardiac CT will be performed after obtaining informed consent from the patients. Discussion: Detecting the anticoagulation response using follow-up TEE in different types of left atrial appendages might be helpful for the non-responders that can be referred for surgery after TEE and Cardiac CT on first examination and TEE only at the end of three months. Trial registration number: The trial was registered on ClinicalTrials.gov (NCT05186649).

In this article, four new families named as Weibull extended exponentiated-X (WEE-X), Lomax extended exponentiated-X (LEE-X), Logistic extended exponentiated-X (LGCEE-X), and Burr III extended exponentiated-X (BIIIEE-X) with their quantile functions are proposed. The expressions for distribution function and density function of BIIIEE-X family are written in terms of linear combinations of the exponentiated densities based to parent model. New models, i.e., Weibul extended exponentiated Weibull (WEEW), Lomax extended exponentiated Weibull (LEEW), Logistic extended exponentiated Weibull (LGCEEW), and Burr III extended exponentiated-Weibull (BIIIEEW) distributions are derived, were plotted for functions of probability density and hazard rate at different levels of parameters. Some mathematical properties of the BIIIEEW model are disclosed. The maximum likelihood method for the BIIIEEW model are described. Numerical applications of the BIIIEEW model to disease of cancer datasets are provided.

A vehicular ad hoc network (VANET) is a technique that uses vehicles with the ability to sense data from the environment and use it for their safety measures. Flooding is a commonly used term used for sending network packets. VANET may cause redundancy, delay, collision, and the incorrect receipt of the messages to their destination. Weather information is one of the most important types of information used for network control and provides an enhanced version of the network simulation environments. The network traffic delay and packet losses are the main problems identified inside the network. In this research, we propose a routing protocol which can transmit the weather forecasting information on demand based on source vehicle to destination vehicles, with the minimum number of hop counts, and provide significant control over network performance parameters. We propose a BBSF-based routing approach. The proposed technique effectively enhances the routing information and provides the secure and reliable service delivery of the network performance. The results taken from the network are based on hop count, network latency, network overhead, and packet delivery ratio. The results effectively show that the proposed technique is reliable in reducing the network latency, and that the hop count is minimized when transferring the weather information.

The study discusses the climate of Balochistan with a special focus on the variation of weather condition taking into account the mean monthly precipitation, temperature, humidity, atmospheric circulation, air pressure, evapotranspiration and solar energy covering the time duration of 1931-2020 (normal data). The physical barriers that bring variations in the climate of Balochistan contain geographical location, ocean, geomorphology, land use, natural vegetation, and continental extent. Based on precipitation, the province has been divided into two main regions that are arid and semi-arid, while the temperature zones are hot, warm, mild and cool. The southern and eastern part of the province receives heavy rain in the summers (monsoon), whereas it is from the western depressions during the winter season. Balochistan experiences four rainy seasons in winter (cold), pre-monsoon season (warm), monsoon season (hot), and post-monsoon season (mild). Owing to tropical (coastal) and sub-tropical continental characteristics, the area familiarizes two foremost seasons namely winter and summer. The summers of the area long for 5 months in hilly areas while 7 months in continental plains and coastal regions; whereas, winters cover five months in the plains and seven months in the mountains. Based on the appropriationand fluctuation in climate constituents, Balochistan has been classified into two main, 6 meso, and 9 microclimate and weather zones. According to Global Climate Risk Index, 2021, Pakistan has been ranked at 8th in the list of top ten global climate high-risk countries, which are exposed to the ongoing climate change and requires attention to resolve the issue.

Common bean (Phaseolus vulgaris L.) is a legume crop grown all over the world and is a very important food of mountain population of Pakistan for protein intake. The Western Himalayan Mountains are rich in biodiversity including unexplored landraces of the common bean crop. Unfortunately, very little attention has been given to this valuable crop in Pakistan, and it is being exported, majorly from Ethiopia, to meet the country’s requirements. The exploitation, utilization, preservation and multiplication of existing germplasm within the area are very important for sustainable production of the crop and enhancing the nutrition value for the local community in mountain regions. A research study was conducted for evaluation of biological diversity of common bean landraces from Azad Kashmir and Northern areas of Pakistan using morpho-physiological and molecular markers. Thirty-five common bean ecotypes along with one check variety were collected from different altitudes of Azad Kashmir and Northern Pakistan and screened for biological diversity. Morphological characterization revealed high genetic diversity in parameters including stem anthocyanin, growth type, days to flowering, pods/plant and 100 seeds weight. Genomic characterization using SSR markers, for allelic diversity evaluation among germplasm, also provided diverse profile with 83.3% polymorphism in banding pattern. The bulk of gene pool diversity evaluated within bean landraces may help to initiate breeding program for common bean improvement.

This study determined the influence of renewable energy and natural resources on carbon emissions by paying special attention to the transboundary trade relationship between South East Asian (SEA) economies. To highlight the importance of international trade relationships among the SEA economies, an interaction term is introduced. Moreover, to estimate the results, panel data from 1996 to 2019 is analyzed by applying Cross-Sectional Augmented Autoregressive Distributed Lag (CS-ARDL). The results show that renewable energy consumption is a significant factor that can reduce carbon emissions. The employment of interaction term shows that international trade improves the influence of renewable energy to control carbon emissions. The findings also depict that natural resources consumption is stimulating carbon emissions. While a strong trade bond is helping to reduce the influence of natural resources consumption on carbon emissions. The findings of this study highlight the importance of international trade at the regional level to mitigate carbon emissions. The study suggests that improvement in international trade may prove a helpful strategy to promote renewable energy sources and diminish the reliance on natural resources such as fossil fuels, this will ultimately help to mitigate carbon emissions.

The study of hydrological characteristics has a vital role in designing, planning, and managing water resources. The selection of appropriate probability distributions and methods of estimations are basic elements in hydrology analyses. In this article, a new family named the ‘exponentiated power alpha index generalized’ (EPAIG)-G is proposed to develop several new distributions. Using this proposed family, we developed a new model, called the EPAIG-exponential (EPAIG-E). A few structural properties of the EPAIG-G were obtained. The EPAIG-E parameters were estimated through the method of maximum likelihood (MML). The study of the Monte Carlo simulation (MCS) was produced for the EPAIG-E. The model performance is illustrated using real data.

Green food in China refers to a wide array of primary and processed agricultural products that are safe, nutritious and of high quality for human consumption. Green food has been certified and produced following the principle of sustainability since the 1990s, making historic achievements in providing quality food, protecting the environment, increasing farmer income, and nurturing agricultural brands over the past 30 years in China. Today, the green food industry enters a steady-growth stage in terms of cultivation area, product number and sales. This article summarizes the history of the development of green food in China and current achievements, analyze major challenges that may hamper further development of the industry, and propose strategies to address these challenges, i.e., optimization of the food supply chain, deep food processing, and utilization of food wastes.

Alfalfa (Medicago sativa L.) is one of the most widely cultivated forage crops in the world. China is the second largest producer of alfalfa in terms of the planting area worldwide, with Gansu, Henan, Inner Mongolia, and Shaanxi provinces being the production hubs. Alfalfa viruses have been reported on a small-scale survey in some of these areas, but they have not been well characterized. In the present study, seven viruses were detected in 12 fields of 10 cities/counties of the four abovementioned provinces by high-throughput sequencing and assembly of small RNA. Their incidence, distribution, and genetic diversity were analyzed by enzyme-linked immunosorbent assay, polymerase chain reaction (PCR)/reverse transcription-PCR and clone sequencing. The results showed that alfalfa mosaic virus (AMV), pea streak virus (PeSV), lucerne transient streak virus (LTSV), alfalfa dwarf virus (ADV), Medicago sativa alphapartitivirus 1 (MsAPV1), MsAPV2, and alfalfa leaf curl virus (ALCV) were the main viruses infecting alfalfa in four examined provinces. AMV and MsAPV1 had the highest incidences in all 4 provinces. SDT analysis of the 7 viruses isolated in China revealed a highly conserved among AMV, LTSV, ADV, MsAPV1, MsAPV2, and ALCV, but the sequence was a high variation between China isolates to abroad isolates in PeSV, ADV, and ALCV. To our knowledge, this is the first report of ADV in Inner Mongolia and Gansu, ALCV in Inner Mongolia, MsAPV1 and MsAPV2 in all 4 provinces, and PeSV and LTSV in China. These findings provide a basis for future research on the genetic evolution of alfalfa viruses in China and on strategies to prevent diseases in alfalfa caused by these viruses.

In computational mathematics, it is a matter of deep concern to recognize which of the given iteration schemes converges quickly with lesser error to the desired solution. Fixed point iterative schemes are constructed to be used for solving equations emerging in many fields of science and engineering. These schemes reformulate a nonlinear equation fs=0 into a fixed point equation of the form s=gs ; such application determines the solution of the original equation via the support of fixed point iterative method and is subject to existence and uniqueness. In this manuscript, we introduce a new modified family of fixed point iterative schemes for solving nonlinear equations which generalize further recursive methods as particular cases. We also prove the convergence of our suggested schemes. We also consider some of the mathematical models which are categorically nonlinear in essence to authenticate the performance and effectiveness of these schemes which can be seen as an expansion and rationalization of some existing techniques.

Abstracts: Drought stress is a severe global environmental concern for the growth and development of alfalfa (Medicago sativa L.), which is crucial for agricultural sustainability owing to its high feed value and economic prospects. Melatonin (MT) has emerged as a multifaceted biomolecule that plays a fundamental role in regulating plants' multiple environmental stresses. However, the significant effect of exogenous MT on drought stress tolerance in the alfalfa forage crops remains inexpressible. We investigated the effects of foliar-sprayed melatonin at different doses (50, 100, 200 μM) on alfalfa seedlings' growth and physiological responses under drought stress. Our results showed that drought stress reduced morphological growth attributes (by 12 to 54%), gas exchange parameters (by 37 to 88%), photosynthetic pigments (Chl a, and Chl b contents by 29%, and 40%), mineral contents, and increased lipid peroxidation (by 69%) and accumulation of reactive oxygen species (ROS) in alfalfa plants. In contrast, exogenous foliar application of MT significantly revived growth characteristics, boosted antioxidant enzyme activities, improved photosynthetic performance, decreased ROS-induced oxidative damage by increasing plant mineral elements, and lowered lipid peroxidation in plants under drought stress. In addition, MT also stimulated the accumulation of total soluble sugar and proline content by 48% and by 75% respectively, which were positively associated with improved leaf water status in drought-stressed plants. Meanwhile, our findings revealed that the highest efficiency was achieved at 100 μM melatonin. In conclusion, these findings bring a new perspective on melatonin-mediated drought stress tolerance and provide a foundational understanding of alfalfa's future cultivation in drought-prone regions globally.

The phytotoxicity of nanoparticles has attracted considerable interest, given the broad applications of nanomaterials in different fields. Alfalfa (Medicago sativa L.) is a major forage crop grown worldwide with a high protein content. The molecular regulation mechanisms involved in nanomaterial-treated alfalfa were examined in this research. In our lab, 18 cDNA libraries of Golden Empress (GE) and Bara 310SC (SC) under control (CK), middle (10 g kg−1)- and high (20 g kg−1)-graphene stress treatments were constructed in 2019. All clean reads were matched to the reference Medicago_truncatula genome, the mapping ratio was higher than 50%, and a total of 3946 differentially expressed genes (DEGs) were obtained. The number of DEGs that reflect transcriptional activity is proportional to the degree of stress. For example, 1241/610 and 1794/1422 DEGs were identified as significant in the leaves of GE/SC under mid- and high-graphene treatment, respectively. Furthermore, GO analysis of the DEGs annotated in some significant biochemical process terms included ‘response to abiotic stimulus’, ‘oxidation-reduction process’, ‘protein kinase activity’, and ‘oxidoreductase activity’. KEGG pathway analysis of the DEGs revealed strongly mediated graphene-responsive genes in alfalfa mainly linked to the ‘biosynthesis of amino acids’, ‘isoflavonoid biosynthesis’, ‘linoleic acid metabolism’, and ‘phenylpropanoid biosynthesis’ pathways. In addition, hundreds of DEGs, including photosynthetic, antioxidant enzyme, nitrogen metabolism, and metabolic sucrose and starch genes, have been identified as potentially involved in the response to graphene. Physiological findings revealed that enzymes related to the metabolism of nitrogen play a crucial role in the adaptation of graphene stress to alfalfa. Ultimately, in response to graphene stress, a preliminary regulatory mechanism was proposed for the self-protective mechanism of alfalfa, which helps to explain the phytotoxicity of the molecular mechanism of nanoparticle-treated crops.

The purpose of this article is to achieve new soliton solutions of the Gilson–Pickering equation (GPE) with the assistance of Sardar’s subequation method (SSM) and Jacobi elliptic function method (JEFM). The applications of the GPE is wider because we study some valuable and vital equations such as Fornberg–Whitham equation (FWE), Rosenau–Hyman equation (RHE) and Fuchssteiner–Fokas–Camassa–Holm equation (FFCHE) obtained by particular choices of parameters involved in the GPE. Many techniques are available to convert PDEs into ODEs for extracting wave solutions. Most of these techniques are a case of symmetry reduction, known as nonclassical symmetry. In our work, this approach is used to convert a PDE to an ODE and obtain the exact solutions of the NLPDE. The solutions obtained are unique, remarkable, and significant for readers. Mathematica 11 software is used to derive the solutions of the presented model. Moreover, the diagrams of the acquired solutions for distinct values of parameters were demonstrated in two and three dimensions along with contour plots.

Here, we propose a new generalized exponential extended exponentiated (NGE3) family of distributions. Some statistical properties of proposed family are gained. The most extreme probability method, maximum likelihood (ML), is utilized for parameter estimation. We explore an exceptional model called NGE3-Exponential (NGE3E). NGE3E is estimated with ML, and the performance of estimators is demonstrated by utilizing a simulation. Moreover, two applications are given to show the significance and adaptability of the proposed model in comparison to some generalized models (GMs).

Multidimensional achievement tests are recently gaining more importance in educational and psychological measurements. For example, multidimensional diagnostic tests can help students to determine which particular domain of knowledge they need to improve for better performance. To estimate the characteristics of candidate items (calibration) for future multidimensional achievement tests, we use optimal design theory. We generalize a previously developed exchange algorithm for optimal design computation to the multidimensional setting. We also develop an asymptotic theorem saying which item should be calibrated by examinees with extreme abilities. For several examples, we compute the optimal design numerically with the exchange algorithm. We see clear structures in these results and explain them using the asymptotic theorem. Moreover, we investigate the performance of the optimal design in a simulation study.

This paper proposes an AC micro-grid structure, which was based on diesel engine, synchronous generator and hybrid energy storage (HES) subsystem, consisting of battery and ultra-capacitor. In system operation, the diesel generator works as the sole voltage source of the micro-grid under islanding mode and the HES cooperate to achieve the power balance of the system. The corresponding energy management strategy was proposed, where the net power of the micro-grid is separated into high and low frequency components. The high frequency part is suppressed by the ultra-capacitor unit, whereas the low frequency part is compensated by the diesel generator and the battery unit together. Through the designed energy management strategy, power balanced of the micro-grid system can be achieved. MATLAB/SIMULINK simulation results under different conditions have verified this proposed micro-grid structure and the proposed energy management strategy. Keywords: AC micro-grid, Diesel generator, Power balance, Ultra-capacitor and battery

In recent years, the trigonometric families of continuous distributions have found a place of choice in the theory and practice of statistics, with the Sin-G family as leader. In this paper, we provide some contributions to the subject by introducing a flexible extension of the Sin-G family, called the transformed Sin-G family. It is constructed from a new polynomial-trigonometric function presenting a desirable "versatile concave/convex" property, among others. The modelling possibilities of the former Sin-G family are thus multiplied. This potential is also highlighted by a complete theoretical work, showing stochastic ordering results, studying the analytical properties of the main functions, deriving several kinds of moments, and discussing the reliability parameter as well. Then, the applied side of the proposed family is investigated, with numerical results and applications on the related models. In particular, the estimation of the unknown model parameters is performed through the use of the maximum likelihood method. Then, two real life data sets are analyzed by a new extended Weibull model derived to the considered trigonometric mechanism. We show that it performs the best among seven comparable models, illustrating the importance of the findings.

Here, a new method is recommended to characterize a new continuous distribution class, named the generalized alpha exponent power family of distributions (GAEPFDs). A particular sub-model is presented for exemplifying the objective. The basic statistical properties, such as ordinary moments, the probability weighted moments, mode, quantile, order statistics, entropy measures, and moment generating functions, etc., were explored. To gauge the GAEPPRD parameters, the ML technique was utilized. The estimator behaviour was studied by a Monte Carlo simulation study (MCSS). The effectiveness of GAEPFDs was demonstrated observationally through lifetime data. The applications show that GAEPFDs can offer preferable results over other competitive models.

Abstract At‐site flood frequency analysis is a direct method of flood estimation at a given site. The choice of an appropriate probability distribution and parameter estimation method plays a vital role in at‐site frequency analysis. In the current article, flood frequency analysis is carried out at five gauging sites of the Ume River in Sweden. Generalised extreme value, three‐parameter log‐normal, generalised logistic and Gumbel distributions are fitted to the annual maximum peak flow data. The L‐moment and the maximum likelihood methods are used to estimate the parameters of the distributions. Based on different goodness‐of‐fit tests and accuracy measures, the three‐parameter log‐normal distribution has been identified as the best‐fitted distribution by using the L‐moments method of estimation for gauging sites Harrsele Krv, Gardiken and Överuman Nedre. The generalised extreme value distribution with the L‐moments estimation provided the best fit to maximum annual streamflow at gauging sites Solberg and Stornorrfors Krv. Finally, the best‐fitted distribution for each gauging site is used to predict the maximum flow of water for return periods of 5, 10, 25, 50, 100, 200, 500, and 1000 years.

Amongst various environmental constraints, abiotic stresses are increasing the risk of food insecurity worldwide by limiting crop production and disturbing the geographical distribution of food crops. Millets are known to possess unique features of resilience to adverse environments, especially infertile soil conditions, although the underlying mechanisms are yet to be determined. The small diploid genome, short stature, excellent seed production, C4 photosynthesis, and short life cycle of foxtail millet make it a very promising model crop for studying nutrient stress responses. Known to be a drought-tolerant crop, it responds to low nitrogen and low phosphate by respective reduction and enhancement of its root system. This special response is quite different from that shown by maize and some other cereals. In contrast to having a smaller root system under low nitrogen, foxtail millet enhances biomass accumulation, facilitating root thickening, presumably for nutrient translocation. The low phosphate response of foxtail millet links to the internal nitrogen status, which tends to act as a signal regulating the expression of nitrogen transporters and hence indicates its inherent connection with nitrogen nutrition. Altogether, the low nitrogen and low phosphate responses of foxtail millet can act as a basis to further determine the underlying molecular mechanisms. Here, we will highlight the abiotic stress responses of foxtail millet with a key note on its low nitrogen and low phosphate adaptive responses in comparison to other crops.

Magnesium deficiency is a frequently occurring limiting factor for crop production due to low levels of exchangeable Mg (ex-Mg) in acidic soil, which negatively affects sustainability of agriculture development. How Mg fertilization affects crop yield and subsequent physiological outcomes in different crop species, as well as agronomic efficiencies of Mg fertilizers, under varying soil conditions remain particular interesting questions to be addressed. A meta-analysis was performed with 570 paired observations retrieved from 99 field research articles to compare effects of Mg fertilization on crop production and corresponding agronomic efficiencies in different production systems under varying soil conditions. The mean value of yield increase and agronomic efficiency derived from Mg application was 8.5% and 34.4 kg kg-1 respectively, when combining all yield measurements together, regardless of the crop type, soil condition, and other factors. Under severe Mg deficiency (ex-Mg < 60 mg kg-1), yield increased up to 9.4%, nearly two folds of yield gain (4.9%) in the soil containing more than 120 mg kg-1 ex-Mg. The effects of Mg fertilization on yield was 11.3% when soil pH was lower than 6.5. The agronomic efficiency of Mg fertilizers was negatively correlated with application levels of Mg, with 38.3 kg kg-1 at lower MgO levels (0–50 kg ha-1) and 32.6 kg kg-1 at higher MgO levels (50–100 kg ha-1). Clear interactions existed between soil ex-Mg, pH, and types and amount of Mg fertilizers in terms of crop yield increase. With Mg supplementation, Mg accumulation in the leaf tissues increased by 34.3% on average; and concentrations of sugar in edible organs were 5.5% higher compared to non-Mg supplemented treatments. Our analysis corroborated that Mg fertilization enhances crop performance by improving yield or resulting in favorable physiological outcomes, providing great potentials for integrated Mg management for higher crop yield and quality.

Micronutrient (iron, zinc and boron) deficiencies are a basic and prominent factor affecting grape quality and yield in the Pothwar region. To overcome these deficiencies, different levels of micronutrients were applied foliarly on grapevines at five different berry developmental stages during two consecutive growing seasons (2018 and 2019). The data suggested that foliar treatment of micronutrients significantly increased the yield, number of bunches per vine, bunch weight, yield per vines, bunch length, berry number per cluster, berry diameter, berry weight and cluster compactness. The biochemical quality attributes of berries, including sugars (reducing, non-reducing as well as total sugars), ascorbic acid content, pH and TSS values, were at their highest levels in grapevines supplemented with Fe, Zn and B treatment at 200 ppm, respectively, i.e., the highest concentrations used. Biochemical leaf values, including chlorophyll a and b and leaf micronutrient content (Fe, Zn and B), were also highest in grapevines that were sprayed with Fe, Zn and B at 200 ppm. Overall, the results revealed that the performance of grapevine cv. ‘Flame Seedless’ growing in agroclimatic conditions of the Pothwar region was improved as a result of the foliar application of Fe, Zn and B at 200 ppm. The results also suggested that a further increase in the concentration of each nutrient might be helpful to obtain berries of improved quantity and quality.

Peaches are well-liked amongst the stone fruits in Pakistan. The peach industry faces significant losses, from harvesting to marketing. The objective of this study was to investigate the effectiveness of foliar sprays of salicylic acid (SA) on the fruit quality of peaches (cv. ‘Flordaking’) at the harvest and postharvest life or stages. Different concentrations of SA (control, 1, 2 and 3 mM) were sprayed on the plants at three growth stages of fruit, i.e., the cell division, cell enlargement and pit-hardening stages. In general, all the SA treatments improved the fruit quality at harvest and maintained higher levels of flesh firmness, titratable acidity and ascorbic acid during storage. However, fruit weight loss, soluble solid contents, membrane leakage, chilling injury, color development, disease and decay incidence and the climacteric peak of ethylene were lowered by SA treatment after six weeks of low-temperature storage. SA at a 3-mM concentration was proven to be the most effective in maintaining the quality for a longer period of time during low-temperature storage. Based on the results, it can be concluded that the application of SA at fruit development stages can improve the harvest quality and storability of ‘Flordaking’ peaches.

This research proposes a generic smart cloud-based system in order to accommodate multiple scenarios where agriculture farms using Internet of Things (IoTs) need to be monitored remotely. The real-time and stored data are analyzed by specialists and farmers. The cloud acts as a central digital data store where information is collected from diverse sources in huge volumes and variety, such as audio, video, image, text, and digital maps. Artificial Intelligence (AI) based machine learning models such as Support Vector Machine (SVM), which is one of many classification types, are used to accurately classify the data. The classified data are assigned to the virtual machines where these data are processed and finally available to the end-users via underlying datacenters. This processed form of digital information is then used by the farmers to improve their farming skills and to update them as pre-disaster recovery for smart agri-food. Furthermore, it will provide general and specific information about international markets relating to their crops. This proposed system discovers the feasibility of the developed digital agri-farm using IoT-based cloud and provides solutions to problems. Overall, the approach works well and achieved performance efficiency in terms of execution time by 14%, throughput time by 5%, overhead time by 9%, and energy efficiency by 13.2% in the presence of competing smart farming baselines.

Expectiles have gained considerable attention in recent years due to wide applications in many areas. In this study, the k-nearest neighbours approach, together with the asymmetric least squares loss function, called ex-kNN, is proposed for computing expectiles. Firstly, the effect of various distance measures on ex-kNN in terms of test error and computational time is evaluated. It is found that Canberra, Lorentzian, and Soergel distance measures lead to minimum test error, whereas Euclidean, Canberra, and Average of (L1,L∞) lead to a low computational cost. Secondly, the performance of ex-kNN is compared with existing packages er-boost and ex-svm for computing expectiles that are based on nine real life examples. Depending on the nature of data, the ex-kNN showed two to 10 times better performance than er-boost and comparable performance with ex-svm regarding test error. Computationally, the ex-kNN is found two to five times faster than ex-svm and much faster than er-boost, particularly, in the case of high dimensional data.

This study proposes the machine learning based classification of medical plant leaves. The total six varieties of medicinal plant leaves-based dataset are collected from the Department of Agriculture, The Islamia University of Bahawalpur, Pakistan. These plants are commonly named in English as (herbal) Tulsi, Peppermint, Bael, Lemon balm, Catnip, and Stevia and scientifically named in Latin as Ocimum sanctum, Mentha balsamea, Aegle marmelos, Melissa officinalis, Nepeta cataria, and Stevia rebaudiana, respectively. The multispectral and digital image dataset are collected via a computer vision laboratory setup. For the preprocessing step, we crop the region of the leaf and transform it into a gray level format. Secondly, we perform a seed intensity-based edge/line detection utilizing Sobel filter and draw five regions of observations. A total of 65 fused features dataset is extracted, being a combination of texture, run-length matrix, and multi-spectral features. For the feature optimization process, we employ a chi-square feature selection approach and select 14 optimized features. Finally, five machine learning classifiers named as a multi-layer perceptron, logit-boost, bagging, random forest, and simple logistic are deployed on an optimized medicinal plant leaves dataset, and it is observed that the multi-layer perceptron classifier shows a relatively promising accuracy of 99.01% as compared to the competition. The distinct classification accuracy by the multi-layer perceptron classifier on six medicinal plant leaves are 99.10% for Tulsi, 99.80% for Peppermint, 98.40% for Bael, 99.90% for Lemon balm, 98.40% for Catnip, and 99.20% for Stevia.

Peanut (Arachis hypogaea) is an important legume and oilseed crop, native to South America and grown in all tropical and temperate regions of the world. A simplified and rapid direct gene delivery system in peanut was developed by vortexing silicon carbide whiskers with callus and with plasmid harboring chitinase and hygromcin genes. The effects of callus age and whisker quantity on transformation efficiency were evaluated. Transformation efficiency (6.88%) was highest when 200 mg of whiskers were used with 5 µg plasmid for 2 g of 20-day-old callus. Hygromcin-resistant calli were regenerated to complete plants which produced seeds normally. Transgene insertion and number of transgene copieswere confirmed by PCR and southern blot analyses, respectively. Transgene expression was evaluated by a pathogenecity test and RT-PCR analysis. In transgenic events, the resistance level to leaf spot disease was far higher than in control plants.