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The arrival of virtual power plants (VPPs) marks important progress in the energy sector, providing optimistic solutions to the increasing need for energy flexibility, resilience, and improved energy systems’ integration. VPPs harness several characteristics to bring together distributed energy resources (DERs), resulting in economic gains and improved power grid reliability. Nevertheless, VPPs encounter major challenges when it comes to engaging in energy markets, mainly because there is no all-encompassing policy and regulatory framework specifically designed to accommodate their unique characteristics. This underscores the necessity for research endeavours to develop more advanced methods and structures for the long-term viability of VPPs. To address this concern, the study advocates for the implementation of a multi-aspect framework (MAF) as a systematic approach to thoroughly examine each aspect of virtual power plants (VPPs). A STEEP (social, technological, environmental, economic, and political) analytical tool is utilized to evaluate the challenges, opportunities, and benefits of a VPP in the existing energy markets. The proposed approach highlights important factors and actions that need to be taken to tackle the challenges related to VPP’ entry into energy markets. This study suggests that further support is required to promote the fast and widespread adoption of long-term VPP implementations. For this reason, a more favourable policy and regulatory framework based on social, technological, economic, environmental, and policy considerations is necessary to realize the genuine contributions of VPPs.

Enhancing the transparency of high-transmission-ratio linear actuators is crucial for improving the safety and capability of high-force robotic systems having physical contact with humans in unstructured environments. However, realizing such enhancement is challenging. A proposed solution for active body weight support systems involves employing a macro–mini linear actuator incorporating an electrorheological-fluid brake to connect a high-force unit with an agile, highly back-drivable unit. This paper introduces the use of an electromagnetic (EM) brake with reduced rotor inertia to address this challenge. The increased torque capacity of the EM brake enables integration with a low-gear-ratio linear transmission. The agile translation of the endpoint is propelled by a low-inertia motor (referred to as the “mini”) via a pulley-belt mechanism to achieve high transparency. The rotor of the EM brake is linked to the pulley. Damping modulation under high driving force is achieved through the adjustment of the brake torque relative to the rotational speed of the pulley. When the brake is engaged, it prevents any relative motion between the endpoint and the moving carrier. The endpoint is fully controlled by the ball screw of the high-force unit, referred to as the “macro”. A scaled prototype was constructed to experimentally characterize the damping force generated by the mini motor and the EM brake. The macro–mini linear actuator, equipped with an intrinsic failsafe feature, can be utilized for active body weight support systems that demand high antigravity force.

Vector quantization (VQ) is a block coding method that is famous for its high compression ratio and simple encoder and decoder implementation. Linde–Buzo–Gray (LBG) is a renowned technique for VQ that uses a clustering-based approach for finding the optimum codebook. Numerous algorithms, such as Particle Swarm Optimization (PSO), the Cuckoo search algorithm (CS), bat algorithm, and firefly algorithm (FA), are used for codebook design. These algorithms are primarily focused on improving the image quality in terms of the PSNR and SSIM but use exhaustive searching to find the optimum codebook, which causes the computational time to be very high. In our study, our algorithm enhances LBG by minimizing the computational complexity by reducing the total number of comparisons among the codebook and training vectors using a match function. The input image is taken as a training vector at the encoder side, which is initialized with the random selection of the vectors from the input image. Rescaling using bilinear interpolation through the nearest neighborhood method is performed to reduce the comparison of the codebook with the training vector. The compressed image is first downsized by the encoder, which is then upscaled at the decoder side during decompression. Based on the results, it is demonstrated that the proposed method reduces the computational complexity by 50.2% compared to LBG and above 97% compared to the other LBG-based algorithms. Moreover, a 20% reduction in the memory size is also obtained, with no significant loss in the image quality compared to the LBG algorithm.

Land use has a great impact on soil dynamics. The soils of various land use systems in Central Karakoram have been under immense pressure in the recent past due to certain anthropogenic activities such as land use practices and land use cover changes. These influences have an impact on the spatial distribution of metallic elements (MEs) in the soils of various land uses. Herein, we investigated the occurrence of the MEs, copper (Cu), zinc (Zn), and nickel (Ni), in soils of various land uses such as the permafrost, pasture, forest, and agricultural lands of the Central Karakorum region. The MEs were extracted in exchangeable, adsorbed, organically bound, carbonated, precipitated, and residual forms. The concentrations of MEs showed a significant dependence on the extraction method used, and the extraction trend followed the order of EDTA > HNO3 > KNO3 > NaOH > H2O. Zn showed the highest concentration compared to Ni and Cu in all extractions, whereas the land uses’ ME concentration followed the order of agricultural land > permafrost > forest > pasturelands. The highest values of total Zn, Ni, and Cu were 712 ± 01 mg/kg, 656 ± 02 mg/kg, and 163 ± 02 mg/kg, respectively, in agricultural soil. The ME concentration showed significant variations between different land uses, and the highest concentration was noted in agricultural soil. Zn was found to be a dominant ME compared to Ni and Cu. We believe this effort will provide opportunities for scholars to investigate MEs around the globe.

Fluoride (F−) pollution in potable groundwater (GW) is a serious environmental concern in Pakistan with substantial human health hazard reports. The research on F− pollution in GW resources in Sindh Province is still incomplete. To explore the realistic conditions, the present research aimed to investigate the GW quality of community tube wells concerning F− contamination in Tharparkar, Sindh, Pakistan. A total of 53 samples were collected and examined for F−, along with other physicochemical parameters. The F− values observed varied from 0.2–4.2 mg/L, with a mean value of 1.63 mg/L. Among the 53 samples, 46% had F− levels that were higher than the World Health Organization’s (WHO) recommended limit (1.5 mg/L). The water type of the studied region was Ca-HCO3 type, which can be attributed to fresh recharged water. The interaction of rock–water contact controls the hydrochemistry of GW. The GW resources of the research zone were highly saturated with fluorite minerals. Human health risk calculation outcomes exposed that 21 samples showed high HQ values for children and 7 samples showed high values for adults in the research zone. Children are at high risk in the study area from drinking F−-contaminated GW. WQI results showed that 31 samples were not suitable for drinking.

Children’s health is one of the most significant fields in medicine. Most diseases that result in children’s death or long-term morbidity are caused by preventable and treatable etiologies, and they appear in the child at the early stages as mild symptoms. This research aims to develop a machine learning (ML) framework to detect the severity of disease in children. The proposed framework helps in discriminating children’s urgent/severe conditions and notifying parents whether a child needs to visit the emergency room immediately or not. The model considers several variables to detect the severity of cases, which are the symptoms, risk factors (e.g., age), and the child’s medical history. The framework is implemented by using nine ML methods. The results achieved show the high performance of the proposed framework in identifying serious pediatric diseases, where decision tree and random forest outperformed the other methods with an accuracy rate of 94%. This shows the reliability of the proposed framework to be used as a pediatric decision-making system for detecting serious pediatric illnesses. The results are promising when compared to recent state-of-the-art studies. The main contribution of this research is to propose a framework that is viable for use by parents when their child suffers from any commonly developed symptoms.

Wind energy has been recognized as a clean energy source with significant potential for reducing carbon emissions. However, its inherent variability poses substantial challenges for power system operators due to its unpredictable nature. As a result, there is an increased dependence on conventional generation sources to uphold the power system balance, resulting in elevated operational costs and an upsurge in carbon emissions. Hence, an urgent need exists for alternative solutions that can reduce the burden on traditional generating units and optimize the utilization of reserves from non-fossil fuel technologies. Meanwhile, vehicle-to-grid (V2G) technology integration has emerged as a remedial approach to rectify power capacity shortages during grid operations, enhancing stability and reliability. This research focuses on harnessing electric vehicle (EV) storage capacity to compensate for power deficiencies caused by forecasting errors in large-scale wind energy-based power systems. A real-time dynamic power dispatch strategy is developed for the automatic generation control (AGC) system to integrate EVs and utilize their reserves optimally to reduce reliance on conventional power plants and increase system security. The results obtained from this study emphasize the significant prospects associated with the fusion of EVs and traditional power plants, offering a highly effective solution for mitigating real-time power imbalances in large-scale wind energy-based power systems.

Land use has a greater impact on trace element (TE) concentration present in soils. In mountainous regions of the western Himalayas, some dominating geogenic and human-dependent anthropogenic factors are involved in the spatial distribution of TEs in various land uses. Soil samples were collected from permafrost, pasture, forest, and agricultural land-use systems of Babusar Valley and Fairy Meadows in Diamer districts and the Rama region in Astore Districts in replications for investigation of three TEs, i.e., copper (Cu), zinc (Zn), and nickel (Ni). These samples were analyzed for exchangeable, adsorbed, organically bound, carbonate precipitated, and residual forms. Significant differences among these TEs were observed. Differences in the levels of TEs within soil samples were observed to be influenced by land usage patterns. The physicochemical properties of soil samples were also investigated. Additionally, the total metals (Ni, Zn, Cu) were extracted and their concentrations were measured in all samples. The concentration of soil TEs was observed in the following order: adsorbed < organically bound < exchangeable < residual < carbonate precipitated form across all the land uses. The results indicate that the contents of TEs (Ni, Zn, Cu) in agricultural soils were greater than in the permafrost pasture and forest soil samples. The total TE concentration varied as Zn > Ni > Cu irrespective of the area and land uses. We believe this work will open avenues for researchers to explore TEs in various regions of the world.

With the increasing integration of wind energy sources into conventional power systems, the demand for reserve power has risen due to associated forecasting errors. Consequently, developing innovative operating strategies for automatic generation control (AGC) has become crucial. These strategies ensure a real-time balance between load and generation while minimizing the reliance on operating reserves from conventional power plant units. Wind farms exhibit a strong interest in participating in AGC operations, especially when AGC is organized into different regulation areas encompassing various generation units. Further, the integration of flexible loads, such as electric vehicles and thermostatically controlled loads, is considered indispensable in modern power systems, which can have the capability to offer ancillary services to the grid through the AGC systems. This study initially presents the fundamental concepts of wind power plants and flexible load units, highlighting their significant contribution to load frequency control (LFC) as an important aspect of AGC. Subsequently, a real-time dynamic dispatch strategy for the AGC model is proposed, integrating reserve power from wind farms and flexible load units. For simulations, a future Pakistan power system model is developed using Dig SILENT Power Factory software (2020 SP3), and the obtained results are presented. The results demonstrate that wind farms and flexible loads can effectively contribute to power-balancing operations. However, given its cost-effectiveness, wind power should be operated at maximum capacity and only be utilized when there is a need to reduce power generation. Additionally, integrating reserves from these sources ensures power system security, reduces dependence on conventional sources, and enhances economic efficiency.

Biogenic nanoparticles are considered effective alternatives to chemical pesticides for the management of pathogenic plant diseases. This study was focused on the synthesis of stable silver nanoparticles (AgNPs) to control challenging plant pathogenic bacteria in vitro and in planta. We synthesized AgNPs by reacting different proportions of silver nitrate and aqueous extract of Hedera nepalensis. The physicochemical properties of the synthesized AgNPs were determined by using various physical techniques. The TEM analysis revealed the AgNPs less than 50 nm in size and spherical shaped. For antibacterial assays, different concentrations (1000–15.62 µg/mL, 2-fold dilutions) of the extract-free AgNPs (Ef-AgNPs) or extract-mixed AgNPs (Em-AgNPs), and fruit extracts (FE) were used against plant pathogenic bacteria Erwinia carotovora subsp. carotovora, Erwinia carotovora subsp. atroseptica, and Ralstonia solanacearum. In the in vitro assays, we found significant inhibition of both bacterial species in response to maximum concentrations of AgNPs. Overall, Ef-AgNPs exhibited a higher percent inhibition of bacterial pathogens. In potato tubers assay, complete inhibition of Erwinia carotovora was observed, except for the lowest AgNPs concentration of 15.62 µg/mL. Similarly, exposure of tomato plants to Ralstonia solanacearum suspensions (OD600 = 0.2) in the soil-drenching experiment and post-exposure treatment with 1000 µg/mL and 125 µg/mL of AgNPs resulted in disease inhibition. This study provides the basis that biogenic nanoparticles prepared from Hedera nepalensis are one of the best substitutes to synthetic pesticide, having displayed better results to control the growth of phytopathogenic microbes. However, field studies need to be conducted in a controlled environment to scale up the current work and find out the efficacy of nanoparticles on a larger scale.

Compliance actuation systems are efficient and safe, drawing attention to their development. However, compliance has caused bandwidth loss, instability, and mechanical vibration in robotic systems. Variable physical damping was introduced to address these issues. This paper presents a technique for obtaining variable damping properties using an electromagnetic brake. The relationship mapping of the voltage and the braking torque is studied and applied to the variable damping concept. A new model is proposed to demonstrate the actuation system performance gained by introducing physical damping. The experimental setup comprises an electromagnetic brake and a motor with an integrated controller for speed control and torque feedback. The motor provides the motion, while the electromagnetic brake replicates the damping through a friction mechanism. The variable damping concept was evaluated experimentally using a 1-degree-of-freedom rotational system. Experimental results show that the proposed concept can generate the desired mechanical damping with a high degree of fidelity.

Mushrooms play a crucial role in human life as well as in nature, providing food, medicine, and carrying out vital processes of decomposition, nutrient recycling, and developing mycorrhizal association with plants. The traditional system of knowledge about identification, collection, and usage of mushrooms has been accumulated through the shared experiences of many generations. Unfortunately, there have been continuous threats to the folk knowledge of mushrooms mainly due to habitat degradation, urbanization, and contemporary medication. The current research was, therefore, aimed to document an ethnomycological knowledge possessed by the ethnic communities of Swat, Pakistan. The purposive randomized sampling was carried out using chain referral method. Ethno-mycological information was collected from 62 informants using free listing, preference ranking, and use totaled methods. In total, 34 species of mushrooms belonging to 31 genera and 21 families were reported. About 85% of the reported species belong to Basidiomycetes, and 12.5% to Ascomycetes are used as food and for medicinal purposes. Morchella angusticeps, M. esculenta, Pleurotus sp., Auricularia sp., Flammulina velutipes, Agaricus bisporus, Ganoderma lucidum, and Sanghuangporus sanghuang were among the most cited edible and medicinal mushrooms. The current study revealed that district Swat is rich in wild edible and medicinal mushrooms (WEMs), and the local communities possess rich traditional knowledge about their collection, storage, and utilization. The diversity of WEMs of this region could contribute substantially to the socio-economic uplifting of the local communities through appropriate domestication and commercialization. Anthropogenic factors, coupled with depletion of traditional knowledge, threaten the diversity of WEMs in the region; therefore, in situ and ex situ conservation strategies are highly recommended.

A large amount of Cr (VI)-polluted wastewater produced in electroplating, dyeing and tanning industries seriously threatens water ecological security and human health. Due to the lack of high-performance electrodes and the coulomb repulsion between hexavalent chromium anion and cathode, the traditional DC-mediated electrochemical remediation technology possesses low Cr (VI) removal efficiency. Herein, by modifying commercial carbon felt (O-CF) with amidoxime groups, amidoxime-functionalized carbon felt electrodes (Ami-CF) with high adsorption affinity for Cr (VI) were prepared. Based on Ami-CF, an electrochemical flow-through system powered by asymmetric AC was constructed. The mechanism and influencing factors of efficient removal of Cr (VI) contaminated wastewater by an asymmetric AC electrochemical method coupling Ami-CF were studied. Scanning Electron Microscopy (SEM), Fourier Transform Infrared (FTIR), and X-ray photoelectron spectroscopy (XPS) characterization results showed that Ami-CF was successfully and uniformly loaded with amidoxime functional groups, and the adsorption capacity of Cr (VI) was more than 100 times higher than that of O-CF. In particular, the Coulomb repulsion effect and the side reaction of electrolytic water splitting were inhibited by the high-frequency anode and cathode switching (asymmetric AC), the mass transfer rate of Cr (VI) from electrode solution was increased, the reduction efficiency of Cr (VI) to Cr (III) was significantly promoted and a highly efficient removal of Cr (VI) was achieved. Under optimal operating conditions (positive bias 1 V, negative bias 2.5 V, duty ratio 20%, frequency 400 Hz, solution pH = 2), the asymmetric AC electrochemistry based on Ami-CF can achieve fast (30 s) and efficient removal (>99.11%) for 0.5–100 mg·L−1 Cr (VI) with a high flux of 300 L h−1 m−2. At the same time, the durability test verified the sustainability of the AC electrochemical method. For Cr (VI)-polluted wastewater with an initial concentration of 50 mg·L−1, the effluent concentration could still reach drinking water grade (−1) after 10 cycling experiments. This study provides an innovative approach for the rapid, green and efficient removal of Cr (VI) containing wastewater at low and medium concentrations.

Task scheduling algorithms are crucial for optimizing the utilization of computing resources. This work proposes a unique approach for improving task execution in real-time systems using an enhanced Round Robin scheduling algorithm variant incorporating dynamic time quantum and priority. The proposed algorithm adjusts the time slice allocated to each task based on execution time and priority, resulting in more efficient resource utilization. We also prioritize higher-priority tasks and execute them as soon as they arrive in the ready queue, ensuring the timely completion of critical tasks. We evaluate the performance of our algorithm using a set of real-world tasks and compare it with traditional Round Robin scheduling. The results show that our proposed approach significantly improves task execution time and resource utilization compared to conventional Round Robin scheduling. Our approach offers a promising solution for optimizing task execution in real-time systems. The combination of dynamic time quantum and priorities adds a unique element to the existing literature in this field.

As tomatoes are the most consumed vegetable in the world, production should be increased to fulfill the vast demand for this vegetable. Global warming, climate changes, and other significant factors, including pests, badly affect tomato plants and cause various diseases that ultimately affect the production of this vegetable. Several strategies and techniques have been adopted for detecting and averting such diseases to ensure the survival of tomato plants. Recently, the application of artificial intelligence (AI) has significantly contributed to agronomy in the detection of tomato plant diseases through leaf images. Deep learning (DL)-based techniques have been largely utilized for detecting tomato leaf diseases. This paper proposes a hybrid DL-based approach for detecting tomato plant diseases through leaf images. To accomplish the task, this study presents the fusion of two pretrained models, namely, EfficientNetB3 and MobileNet (referred to as the EffiMob-Net model) to detect tomato leaf diseases accurately. In addition, model overfitting was handled using various techniques, such as regularization, dropout, and batch normalization (BN). Hyperparameter tuning was performed to choose the optimal parameters for building the best-fitting model. The proposed hybrid EffiMob-Net model was tested on a plant village dataset containing tomato leaf disease and healthy images. This hybrid model was evaluated based on the best classifier with respect to accuracy metrics selected for detecting the diseases. The success rate of the proposed hybrid model for accurately detecting tomato leaf diseases reached 99.92%, demonstrating the model’s ability to extract features accurately. This finding shows the reliability of the proposed hybrid model as an automatic detector for tomato plant diseases that can significantly contribute to providing better solutions for detecting other crop diseases in the field of agriculture.

Currently, Pakistan is facing severe energy crises and global warming effects. Hence, there is an urgent need to utilize renewable energy generation. In this context, Pakistan possesses massive wind energy potential across the coastal areas. This paper investigates and numerically analyzes coastal areas’ wind power density potential. Eight different state-of-the-art numerical methods, namely an (a) empirical method, (b) graphical method, (c) wasp algorithm, (d) energy pattern method, (e) moment method, (f) maximum likelihood method, (g) energy trend method, and (h) least-squares regression method, were analyzed to calculate Weibull parameters. We computed Weibull shape parameters (WSP) and Weibull scale parameters (WCP) for four regions: Jiwani, Gwadar, Pasni, and Ormara in Pakistan. These Weibull parameters from the above-mentioned numerical methods were analyzed and compared to find an optimal numerical method for the coastal areas of Pakistan. Further, the following statistical indicators were used to compare the efficiency of the above numerical methods: (i) analysis of variance (R2), (ii) chi-square (X2), and (iii) root mean square error (RMSE). The performance validation showed that the energy trend and graphical method provided weak performance for the observed period for four coastal regions of Pakistan. Further, we observed that Ormara is the best and Jiwani is the worst area for wind power generation using comparative analyses for actual and estimated data of wind power density from four regions of Pakistan.

The rise of the intelligent, local charging facilitation and environmentally friendly aspects of electric vehicles (EVs) has grabbed the attention of many end-users. However, there are still numerous challenges faced by researchers trying to put EVs into competition with internal combustion engine vehicles (ICEVs). The major challenge in EVs is quick recharging and the selection of an optimal charging station. In this paper, we present the most recent research on EV charging management systems and their role in smart cities. EV charging can be done either in parking mode or on-the-move mode. This review work is novel due to many factors, such as that it focuses on discussing centralized and distributed charging management techniques supported by a communication framework for the selection of an appropriate charging station (CS). Similarly, the selection of CS is evaluated on the basis of battery charging as well as battery swapping services. This review also covered plug-in charging technologies including residential, public and ultra-fast charging technologies and also discusses the major components and architecture of EVs involved in charging. In a comprehensive and detailed manner, the applications and challenges in different charging modes, CS selection, and future work have been discussed. This is the first attempt of its kind, we did not find a survey on the charging hierarchy of EVs, their architecture, or their applications in smart cities.

Large-scale wind power integration has raised concerns about the reliability and stability of power systems. The rotor circuit of a doubly fed induction generator (DFIG) is highly vulnerable to unexpected voltage dips, which can cause considerable electromotive force in the circuit. Consequently, the DFIG must fulfil the fault-ride through (FRT) criteria to ensure the system’s performance and contribute to voltage regulation during severe grid outages. This paper provides a hybrid solution for DFIG wind turbines with FRT capabilities, using both a modified switch-type fault current limiter (MSFTCL) and a direct current (DC) chopper. The proposed system has the merit of keeping the rotor current and the DC-link voltage within the permissible limits, enhancing the FRT capability of generators. Moreover, the boundness of supply voltage into its reference value ensures dynamic stability during symmetric and asymmetric grid failures. Further, electromagnetic torque variations are significantly reduced during fault events. Finally, the performance validation of the proposed scheme is performed in a simulation setup, and the results are compared with the existing sliding mode control (SMC) and proportional-integral (PI) controller-based approaches. The comparison results show that a hybrid strategy with advanced controllers provides superior performance for all critical parameters.

Potable groundwater contamination through arsenic (As) is a common environmental problem in many developing countries, including Pakistan, with significant human health risk reports. The current research was conducted in District Nankana Sahib, which is a major industrial site in Punjab, Pakistan. According to the Punjab Directorate of Industries in Pakistan, there are more than a thousand industries in this area. These industries produce a lot of waste and effluent, which contaminate the environment with harmful and toxic materials. Continuous irrigation with industrial effluent and sewage sludge may make groundwater sources vulnerable. Therefore, this research was aimed as assessing the hydrochemical profile of groundwater concerning As contamination in the study area using sixty-seven groundwater samples. Multivariate statistical analysis, graphical plots, geochemical modeling, and the water quality index (WQI) were applied to investigate the hydrochemistry of the research area. The outcomes of the WQI revealed that 43% of the samples were of poor quality and not fit for human consumption. About 28% of the groundwater samples showed high arsenic contamination beyond the permitted limit of the World Health Organization (WHO). The piper plot identified three distinct types of water in the research area: calcium, chloride, and calcium-chloride types. The Gibbs diagram illustrated that rock–water interaction influenced the hydrochemistry. Terrigenous sedimentary rocks, such as stream deposits, flood plain deposits, and detrital sedimentary rocks, among other types of sedimentary rocks covered most part the study area. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) indicated that the arsenic in groundwater exhibited a significant positive correlation for pH, Fe and As. Health risk assessments indicated a hazard quotient (HQ) greater than 1, indicating a 28% contribution showing that groundwater ingestion is highly toxic to the local habitats. The results of this study further help in managing future sustainable groundwater management approaches in the Nankana District, Punjab, Pakistan.

The environment and the economy are negatively impacted by conventional energy sources, such as coal, gasoline, and other fossil fuels. Pakistan’s reliance on these resources has resulted in a catastrophic energy crisis. This has driven the government to make critical decisions such as early retail closures, power outages for the industrial sector, and an increase to two days a week vacations. Wind energy, accessible and affordable, will become a viable option for meeting Pakistan’s present and future energy demands. Approximately 3% of Pakistan’s land can produce nearly 132 GW of power with an installed capacity of 5 MW per km2. In this study, four zones (Karachi, Thatta, Badin, and Jamshoro) in Sindh province are assessed for the feasibility of wind energy generation. The installed capacity, generator types, and detailed specifications are provided for each zone. Moreover, the wind mapping of Pakistan is presented considering the four potential zones. The zones are analyzed using annual wind speed and power output considering wind data measured at 50 m height over one year. The higher mean speed is recorded at Jamshoro compared to other zones. The analysis indicates that all four sites are suitable for large-scale wind power generation due to their energy potential.

By coordinating the operation of regionally interconnected virtual power plants (VPPs), the growing penetration problem of renewable energy sources (RESs) into the power system can be addressed. This study presents an interactive trading cooperation model of regionally interconnected VPPs using bilateral contracts. The proposed model maximizes overall electricity market social welfare (SW) (i.e., maximization of consumer benefits while minimizing energy costs). The focus of the proposed approach is to design and develop a parallel energy exchange cooperation model of interconnected VPPs, ensuring the operational efficiency and reliability of interconnected power systems over the planning horizon. Given that adjacent VPPs may have differences in their energy generation and usage patterns, a scenario tree method is used to model the uncertainties associated with solar irradiation and load demand. A case study of two interconnected VPPs is used, the operational scenario is designed, and the corresponding computational model is developed. The results highlight that the proposed approach gives VPPs the option to utilize their internal network’s maximum capacity. As a result, there will be less reliance on the main grid for interconnected VPPs, and an improvement in key performance indicators, including the cost of the VPPs systems and renewable power variations.

Functional materials, in the combination of lignocelluloses, known as natural fibers, with oxide materials, can result in cultivating functional properties such as flexibility, relativity good electrical conduction, good electrical charge storage capacity, and tunable electric permittivity. This study presents the morphological, dielectric, and impedance properties of lignocellulose–lead oxide (LC/PbO2) composite sheets electrodeposited with silver metallic nanoparticles for various time spans. The uncoated samples show a rather simple behavior where the impedance data fit well to the two-system model with different relaxation times. On the other side, the impedance spectra of the electrodeposited sample have varying features, which mainly depend upon the deposition thickness of the Ag particles. The common feature is the drift of conductive species, as seen from the straight-line behavior in the Nyquist plots, which were fitted using a Warburg element in the equivalent circuit model.

The Kingdom of Saudi Arabia is considered to be one of the world leaders in olive production accounting for about 6% of the global olive production. Given the fact that 94% of the olive groves are mainly rain-fed using traditional methods of production, the annual olive production is witnessing a noticeable fluctuation which is worse due to infectious diseases and climate change. Thus, early and effective detection of plant diseases is both required and urgent. Most farmers use traditional methods, for example, visual inspection or laboratory examination, to identify plant diseases. Currently, deep learning (DL) techniques have been shown to be useful methods for diagnosing olive leaf diseases and many other fields. In this work, we use a deep feature concatenation (DFC) mechanism to combine features extracted from input images using the two modern pretrained CNN models, i.e., ResNet50 and MobileNet. Hence, we propose MobiRes-Net: A neural network that is a concatenation of the ResNet50 and MobileNet models for overall improvement of prediction capability. To build the dataset used in the study, 5400 olive leaf images were collected from an olive grove using a remote-controlled agricultural unmanned aerial vehicle (UAV) equipped with a camera. The overall performance of the MobiRes-Net model achieved a classification accuracy of 97.08% which showed its superiority over ResNet50 and MobileNet that achieved classification accuracies of 94.86% and 95.63%, respectively.

The study recorded the food uses of wild food plants (WFPs) among the Sarikoli diaspora and the dominant Wakhi in Broghil Valley, North Pakistan, to understand their food adaptation, mainly by looking through the lens of food ethnobotanies. A total of 30 participants took part in the study, which included 15 elderly individuals from each ethnic group. Data were gathered through semi-structured interviews. We recorded 29 WFPs, mostly used as cooked vegetables and snacks. The food uses, as well as the local plant nomenclatures, linked to WFPs of the two studied groups were completely homogenized, which could be attributed to the cultural assimilation of the Sarikoli people to Wakhi culture. We found that although traditional knowledge on WFPs has been homogenized, social change in nearby regions is also threatening the traditional knowledge of the two communities, as evidenced by the smaller number of plants reported compared to that of all other field ethnobotanical studies conducted in nearby regions. Moreover, the growth of legal restrictions and sanctions on accessing natural resources are posing serious challenges to cultural resilience in the valley, and the restrictions on cross-border movement in particular are creating challenges for those who have cross-border kinship relationships between the two groups. We suggest specific measures, such as the promotion of food tourism and educational activities, to protect traditional knowledge and bicultural heritage from further erosion in the region.

The power systems of today seem inseparable from clean energy sources such as wind turbines (WTs) and photovoltaics (PVs). However, due to their uncertain nature, operational challenges are expected when WT and PV energy is added to the electricity network. It is necessary to introduce new technologies to compensate for the intermittent nature of renewable energy sources (RESs). Therefore, rationally implementing a demand response (DR) program with energy storage systems (ESSs) in a virtual power plant (VPP) environment is recommended as a way forward to minimize the volatile nature of RESs and improve power system reliability. Our proposed approach aims to maximize social welfare (SW) (i.e., maximization of consumer benefits while minimizing energy costs). Our method assesses the impact of the DR program on SW maximization. Two scenarios are examined, one with and one without a DR program. Stochastic programming theory is used to address the optimization problem. The uncertain behavior of WTs, PVs, and load demand is modeled using a scenario-based approach. The correctness of the proposed approach is demonstrated on a 16-bus UK generic distribution system. Our results show that SW and active power dispatch capacity of WT, PV, and ESS are fairly increased using the proposed approach.

The penetration of renewable energy sources (RESs) in the electrical power system has increased significantly over the past years due to increasing global concern about climate change. However, integrating RESs into the power market is highly problematic. The output of RESs such as wind turbines (WTs) and photovoltaics (PVs) is highly uncertain. Their correlation with load demand is not always guaranteed, which compromises system reliability. Distributed energy resources (DERs), especially demand response (DR) programs and energy storage systems (ESSs), are possible options to overcome these operational challenges under the virtual power plant (VPP) setting. This study investigates the impact of using a DR program and battery energy storage system (BESS) on the VPP’s internal electricity market, and also cost-minimization analysis from a utility viewpoint. Three different constrained optimal power flow (OPF) problems are solved such as base case, DR case, and BESS case to determine total incurred costs, locational marginal prices (LMPs), and generator commitments. A scenario tree approach is used to model the uncertainties associated with WTs, PVs, and load demand. The proposed model is investigated on a 14-bus distribution system. The simulation results obtained demonstrate a favorable impact of DR and a BESS on renewable operational challenges.

Biologically synthesized silver nanoparticles are emerging as attractive alternatives to chemical pesticides due to the ease of their synthesis, safety and antimicrobial activities in lower possible concentrations. In the present study, we have synthesized silver nanoparticles (AgNPs) using the aqueous extract of the medicinal plant Euphorbia wallichii and tested them against the plant pathogenic bacterium Xanthomonas axonopodis, the causative agent of citrus canker, via an in vitro experiment. The synthesized silver nanoparticles were characterized by techniques such as UV-Vis spectroscopy, Fourier transform infrared spectroscopy, energy-dispersive X-ray spectroscopy, X-ray diffraction analysis and transmission electron microscopy. Moreover, the plant species were investigated for phenolics, flavonoids and antioxidant activity. The antioxidant potential of the extract was determined against a DPPH radical. The extract was also evaluated for phenolic compounds using the HPLC technique. The results confirmed the synthesis of centered cubic, spherical-shaped and crystalline nanoparticles by employing standard characterization techniques. A qualitative and quantitative phytochemical analysis revealed the presence of phenolics (41.52 mg GAE/g), flavonoids (14.2 mg QE/g) and other metabolites of medicinal importance. Different concentrations (1000 µg/mL to 15.62 µg/mL—2 fold dilutions) of AgNPs and plant extract (PE) alone, and both in combination (AgNPs-PE), exhibited a differential inhibition of X. axanopodis in a high throughput antibacterial assay. Overall, AgNPs-PE was superior in terms of displaying significant antibacterial activity, followed by AgNPs alone. An appreciable antioxidant potential was recorded as well. The observed antibacterial and antioxidant potential may be attributed to eight phenolic compounds identified in the extract. The Euphorbia wallichii leaf-extract-induced synthesized AgNPs exhibited strong antibacterial activity against X. axanopodis, which could be exploited as effective alternative preparations against citrus canker in planta in a controlled environment. In addition, as a good source of phenolic compounds, the plant could be further exploited for potent antioxidants.

This work investigates sensor fault diagnostics and fault-tolerant control for a voltage source converter based microgrid (model) using a sliding-mode observer. It aims to provide a diagnosis of multiple faults (i.e., magnitude, phase, and harmonics) occurring simultaneously or individually in current/potential transformers. A modified algorithm based on convex optimization is used to determine the gains of the sliding-mode observer, which utilizes the feasibility optimization or trace minimization of a Ricatti equation-based modification of H-Infinity (H∞) constrained linear matrix inequalities. The fault and disturbance estimation method is modified and improved with some corrections in previous works. The stability and finite-time reachability of the observers are also presented for the considered faulty and perturbed microgrid system. A proportional-integral (PI) based control is utilized for the conventional regulations required for frequency and voltage sags occurring in a microgrid. However, the same control block features fault-tolerant control (FTC) functionality. It is attained by incorporating a sliding-mode observer to reconstruct the faults of sensors (transformers), which are fed to the control block after correction. Simulation-based analysis is performed by presenting the results of state/output estimation, state/output estimation errors, fault reconstruction, estimated disturbances, and fault-tolerant control performance. Simulations are performed for sinusoidal, constant, linearly increasing, intermittent, sawtooth, and random sort of often occurring sensor faults. However, this paper includes results for the sinusoidal nature voltage/current sensor (transformer) fault and a linearly increasing type of fault, whereas the remaining results are part of the supplementary data file. The comparison analysis is performed in terms of observer gains being estimated by previously used techniques as compared to the proposed modified approach. It also includes the comparison of the voltage-frequency control implemented with and without the incorporation of the used observer based fault estimation and corrections, in the control block. The faults here are considered for voltage/current sensor transformers, but the approach works for a wide range of sensors.

The modern power system is characterized by the massive integration of renewables, especially wind power. The intermittent nature of wind poses serious concerns for the system operator owing to the inaccuracies in wind power forecasting. Forecasting errors require more balancing power for maintaining frequency within the nominal range. These services are now offered through conventional power plants that not only increase the operational cost but also adversely affect the environment. The modern power system emphasizes the massive penetration of wind power that will replace conventional power plants and thereby impact the provision of system services from conventional power plants. Therefore, there is an emergent need to find new control and balancing solutions, such as regulation reserves from wind power plants and electric vehicles, without trading off their natural behaviors. This work proposes real-time optimized dispatch strategies for automatic generation control (AGC) to utilize wind power and the storage capacity of electric vehicles for the active power balancing services of the grid. The proposed dispatch strategies enable the AGC to appropriately allocate the regulating reserves from wind power plants and electric vehicles, considering their operational constraints. Simulations are performed in DIgSILENT software by developing a power system AGC model integrating the generating units and an EVA model. The inputs for generating units are considered by selecting a particular day of the year 2020, when wind power plants are generating high power. Different coordinated dispatch strategies are proposed for the AGC model to incorporate the reserve power from wind power plants and EVs. The performance of the proposed dispatch strategies is accessed and discussed by obtaining responses of the generating units and EVs during the AGC operation to counter the initial power imbalances in the network. The results reveal that integration of wind power and electric vehicles alongside thermal power plants can effectively reduce real-time power imbalances acquainted in power systems due to massive penetration of wind power that subsequently improves the power system security. Moreover, the proposed dispatch strategy reduces the operational cost of the system by allowing the conventional power plant to operate at their lower limits and therefore utilizes minimum reserves for the active power balancing services.

The fast increase in adoption and development of Electric Vehicles (EVs) has invited a significant challenge to the existing charging management techniques and infrastructure. It is necessary to efficiently manage a large number of mobile EVs. As compared to fuel and gasoline type vehicles, the EV has a limited driving range and needs to recharge its battery frequently during long journeys. Hence, with plug-in charging services one major concern is the long duration of battery recharging. In this paper, we employ heterogeneous BS (Battery Switching) technology to provide an alternative charging option to minimize the charging duration of EV. Furthermore, enabling BS reservation in a centralized manner for mobile EVs, load balancing algorithm and optimal selection of Battery Switching Station (BSS) across the network are proposed. In addition, we suggest a scheduling technique for depleted batteries to recharge effectively in BSS to minimize power loss and queuing time at selected BSS. We have conducted a performance evaluation by comparing the proposed scheme with other benchmarks, in terms of average trip duration, total trip energy consumption, etc. Finally, it is proven that the battery stock is managed across the network efficiently through the proposed scheme.

Arsenic (As) contamination in drinking groundwater is a common environmental problem in Pakistan. Therefore, sixty-one groundwater samples were collected from various groundwater sources in District Sanghar, Sindh province, Pakistan, to understand the geochemical behavior of elevated As in groundwater. Statistical summary showed the cations and anions abundance in decreasing order of Na+ > Ca2+ > Mg2+ > K+, and HCO3− > Cl− > SO42− > NO3−. Arsenic was found with low to high concentration levels ranging from 5 µg to 25 µg/L with a mean value of 12.9 µg/L. A major water type of groundwater samples was mixed with NaCl and CaHCO3 type, interpreting the hydrochemical behavior of rock–water interaction. Principal component analysis (PCA) showed the mixed anthropogenic and natural sources of contamination in the study area. Moreover, rock weathering and exchange of ions controlled the hydrochemistry. Chloro-alkaline indices revealed the dominance of the reverse ion exchange mechanism in the region. The entropy water quality index (EWQI) exposed that 17 samples represent poor water, and 11 samples are not suitable for drinking.

Groundwater is a critical water supply for safe drinking water, agriculture, and industry worldwide. In the Khanewal district of Punjab, Pakistan, groundwater has severely deteriorated during the last few decades due to environmental changes and anthropogenic activities. Therefore, 68 groundwater samples were collected and analyzed for their main ions and trace elements to investigate the suitability of groundwater sources for drinking and agricultural purposes. Principal component analysis (PCA) and cluster analysis (CA) were employed to determine the major factors influencing groundwater quality. To assess the groundwater’s appropriateness for drinking and irrigation, drinking and agricultural indices were used. The pH of the groundwater samples ranged from 6.9 to 9.2, indicating that the aquifers were slightly acidic to alkaline. The major cations were distributed as follows: Na+ > Ca2+ > Mg2+ > K+. Meanwhile, the anions are distributed as follows: HCO3− > SO42− > Cl− > F−. The main hydrochemical facies were identified as a mixed type; however, a mixed magnesium, calcium, and chloride pattern was observed. The reverse ion exchange process helps in exchanging Na+ with Ca2+ and Mg2+ ions in the groundwater system. Rock weathering processes, such as the dissolution of calcite, dolomite, and gypsum minerals, dominated the groundwater hydrochemistry. According to the Weight Arithmetic Water Quality Index (WAWQI), 50% of the water samples were unsafe for drinking. The Wilcox diagram, USSL diagram, and some other agricultural indices resulted in around 32% of the groundwater samples being unsuitable for irrigation purposes. The Khanewal’s groundwater quality was vulnerable due to geology and the influence of anthropogenic activities. For groundwater sustainability in Khanewal, management strategies and policies are required.

The subject of food–medicines (foods ingested in order to obtain a therapeutic activity or to prevent diseases) is garnering increasing attention from both ethnobiologists and ethnopharmacologists as diet-related chronic diseases are one of the major problems resulting in a large proportion of deaths globally, which calls for interest from the scientific community to make sensible decisions in the field of food and medicine. In this regard, the current study is an important attempt at providing baseline data for developing healthy and curative food ingredients. This study aimed at recording the culinary and medicinal uses of wild food plants (WFPs) in the remote Mastuj Valley, located at the extreme north of Chitral District, Pakistan. An ethnobotanical survey was completed via 30 in-depth semi-structured interviews with local knowledge holders to record the food and medicinal uses of WFPs in the study area. A total of 43 WFPs were recorded, most of which were used as cooked vegetables and raw snacks. Leaves were the most frequently used plant part. A remarkable proportion (81%) of use reports for the recorded wild plant taxa were quoted as food–medicines or medicinal foods, while very few were reported as either food or medicines, without any relationship between uses in these two domains. Previous ethnomedicinal studies from nearby regions have shown that most of the recorded wild plants have been used as medicines, thus supporting the findings of the current study. A literature survey revealed that many of the reported medicinal uses (33%) for the quoted WFPs were not verifiable on PubMed as they have not been studied for their respective medicinal actions. We observed that most of the plants quoted here have disappeared from the traditional food and medicinal system, which may be attributed to the invasion of the food market and the prevalence of allopathic medicine. However, knowledge of these wild plants is still alive in memory, and women are the main holders of cultural knowledge as they use it to manage the cooking and processing of WFPs. Therefore, in this context, we strongly recommend the preservation of local biocultural heritage, promoted through future development and educational programs, which could represent a timely response to the loss of cultural and traditional knowledge.

Automatic Generation Control (AGC) delivers a high quality electrical energy to energy consumers using efficient and intelligent control systems ensuring nominal operating frequency and organized tie-line power deviation. Subsequently, for the AGC analysis of a two-area interconnected hydro-gas-thermal-wind generating unit, a novel Fractional Order Integral-Tilt Derivative (FOI-TD) controller is proposed that is fine-tuned by a powerful meta-heuristic optimization technique referred as Improved-Fitness Dependent Optimizer (I-FDO) algorithm. For more realistic analysis, various constraints, such as Boiler Dynamics (BD), Time Delay (TD), Generation Rate Constraint (GRC), and Governor Dead Zone (GDZ) having non-linear features are incorporated in the specified system model. Moreover, a comparative analysis of I-FDO algorithm is performed with state-of-the-art approaches, such as FDO, teaching learning based optimization, and particle swarm optimization algorithms. Further, the proposed I-FDO tuned controller is compared with Fractional Order Tilt Integral Derivative (FOTID), PID, and Integral-Tilt Derivative (I-TD) controllers. The performance analysis demonstrates that proposed FOI-TD controller provides better performance and show strong robustness by changing system parameters and load condition in the range of  ± 50%, compared to other controllers.

Electronic learning management systems provide live environments for students and faculty members to connect with their institutional online portals and perform educational activities virtually. Although modern technologies proactively support these online sessions, students’ active participation remains a challenge that has been discussed in previous research. Additionally, one concern for both parents and teachers is how to accurately measure student performance using different attributes collected during online sessions. Therefore, the research idea undertaken in this study is to understand and predict the performance of the students based on features extracted from electronic learning management systems. The dataset chosen in this study belongs to one of the learning management systems providing a number of features predicting student’s performance. The integrated machine learning model proposed in this research can be useful to make proactive and intelligent decisions according to student performance evaluated through the electronic system’s data. The proposed model consists of five traditional machine learning algorithms, which are further enhanced by applying four ensemble techniques: bagging, boosting, stacking, and voting. The overall F1 scores of the single models are as follows: DT (0.675), RF (0.777), GBT (0.714), NB (0.654), and KNN (0.664). The model performance has shown remarkable improvement using ensemble approaches. The stacking model by combining all five classifiers has outperformed and recorded the highest F1 score (0.8195) among other ensemble methods. The integration of the ML models has improved the prediction ratio and performed better than all other ensemble approaches. The proposed model can be useful for predicting student performance and helping educators to make informed decisions by proactively notifying the students.

Machine Learning (ML) is one of the major driving forces behind the fourth industrial revolution. This study reviews the ML applications in the life cycle stages of biofuels, i.e., soil, feedstock, production, consumption, and emissions. ML applications in the soil stage were mostly used for satellite images of land to estimate the yield of biofuels or a suitability analysis of agricultural land. The existing literature have reported on the assessment of rheological properties of the feedstocks and their effect on the quality of biofuels. The ML applications in the production stage include estimation and optimization of quality, quantity, and process conditions. The fuel consumption and emissions stage include analysis of engine performance and estimation of emissions temperature and composition. This study identifies the following trends: the most dominant ML method, the stage of life cycle getting the most usage of ML, the type of data used for the development of the ML-based models, and the frequently used input and output variables for each stage. The findings of this article would be beneficial for academia and industry-related professionals involved in model development in different stages of biofuel’s life cycle.

An ethnobotanical field study focusing on traditional wild food botanical taxa was carried out in Kaniguram, South Waziristan, Pakistan, among Ormur (or Burki or Baraki) peoples, which represent a diasporic minority group, as well as among the surrounding Pashtuns. Through sixty semi-structured interviews, fifty-two wild food plants (taxa) were recorded, and they were primarily used raw as snacks and cooked as vegetables. Comparative analysis found a remarkable overlap of the quoted plant uses between the two studied groups, which may reflect complex socio-cultural adaptations Ormur speakers faced. Ormur people retain a rich knowledge of anthropogenic weeds and the phytonyms reveal important commonalities with Persian and Kurdish phytonyms, which may indicate their possible horticultural-driven human ecological origin from the Middle East. Some novel or rare food uses of Cirsiumarvense, Nannorrhops ritchiana, Periploca aphylla, Perovskia atriplicifolia, Viscum album,Oxalis corniculata and Withania coagulans were documented. Since the Ormuri language represents a moribund language, still spoken by only a few thousand speakers in NW Pakistan and Afghanistan, it is recommended that the traditional bio-cultural and gastronomical heritage of this minority group be appropriately protected and bolstered in future rural development programs.

A central authority, in a conventional centralized energy trading market, superintends energy and financial transactions. The central authority manages and controls transparent energy trading between producer and consumer, imposes a penalty in case of contract violation, and disburses numerous rewards. However, the management and control through the third party pose a significant threat to the security and privacy of consumers’/producers’ (participants) profiles. The energy transactions between participants involving central authority utilize users’ time, money, and impose a computational burden over the central controlling authority. The Blockchain-based decentralized energy transaction concept, bypassing the central authority, is proposed in Smart Grid (SG) by researchers. Blockchain technology braces the concept of Peer-to-Peer (P2P) energy transactions. This work encompasses the SolarCoin-based digital currency blockchain model for SG incorporating RE. Energy transactions from Prosumer (P) to Prosumer, Energy District to Energy District, and Energy District to SG are thoroughly investigated and analyzed in this work. A robust demand-side optimized model is proposed using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to maximize Prosumer Energy Surplus (PES), Grid revenue (GR), percentage energy transactions accomplished, and decreased Prosumer Energy Cost (PEC). Real-time averaged energy data of Australia are employed, and a piece-wise energy price mechanism is implemented in this work. The graphical analysis and tabular statistics manifest the efficacy of the proposed model.

Automatic generation control (AGC) is primarily responsible for ensuring the smooth and efficient operation of an electric power system. The main goal of AGC is to keep the operating frequency under prescribed limits and maintain the interchange power at the intended level. Therefore, an AGC system must be supplemented with modern and intelligent control techniques to provide adequate power supply. This paper provides a comprehensive overview of various AGC models in diverse configurations of the power system. Initially, the history of power system AGC models is explored and the basic operation of AGC in a multi-area interconnected power system is presented. An in-depth analysis of various control methods used to mitigate the AGC issues is provided. Application of fast-acting energy storage devices, high voltage direct current (HVDC) interconnections, and flexible AC transmission systems (FACTS) devices in the AGC systems are investigated. Furthermore, AGC systems employed in different renewable energy generation systems are overviewed and are summarized in tabulated form. AGC techniques in different configurations of microgrid and smart grid are also presented in detail. A thorough overview of various AGC issues in a deregulated power system is provided by considering the different contract scenarios. Moreover, AGC systems with an additional objective of economic dispatch is investigated and an overview of worldwide AGC practices is provided. Finally, the paper concludes with an emphasis on the prospective study in the field of AGC.

Brain tumor classification plays an important role in clinical diagnosis and effective treatment. In this work, we propose a method for brain tumor classification using an ensemble of deep features and machine learning classifiers. In our proposed framework, we adopt the concept of transfer learning and uses several pre-trained deep convolutional neural networks to extract deep features from brain magnetic resonance (MR) images. The extracted deep features are then evaluated by several machine learning classifiers. The top three deep features which perform well on several machine learning classifiers are selected and concatenated as an ensemble of deep features which is then fed into several machine learning classifiers to predict the final output. To evaluate the different kinds of pre-trained models as a deep feature extractor, machine learning classifiers, and the effectiveness of an ensemble of deep feature for brain tumor classification, we use three different brain magnetic resonance imaging (MRI) datasets that are openly accessible from the web. Experimental results demonstrate that an ensemble of deep features can help improving performance significantly, and in most cases, support vector machine (SVM) with radial basis function (RBF) kernel outperforms other machine learning classifiers, especially for large datasets.

The health industry is one of the most auspicious domains for the application of Internet of Things (IoT) based technologies. Lots of studies have been carried out in the health industry field to minimize the use of resources and increase the efficiency. The use of IoT combined with other technologies has brought quality advancement in the health sector at minimum expense. One such technology is the use of wireless body area networks (WBANs), which will help patients incredibly in the future and will make them more productive because there will be no need for staying at home or a hospital for a long time. WBANs and IoT have an integrated future as WBANs, like any IoT application, are a collection of heterogeneous sensor-based devices. For the better amalgamation of the IoT and WBANs, several hindrances blocking their integration need to be addressed. One such problem is the efficient routing of data in limited resource sensor nodes (SNs) in WBANs. To solve this and other problems, such as transmission of duplicate sensed data, limited network lifetime, etc., energy harvested and cooperative-enabled efficient routing protocol (EHCRP) for IoT-WBANs is proposed. The proposed protocol considers multiple parameters of WBANs for efficient routing such as residual energy of SNs, number of hops towards the sink, node congestion levels, signal-to-noise ratio (SNR) and available network bandwidth. A path cost estimation function is calculated to select forwarder node using these parameters. Due to the efficient use of the path-cost estimation process, the proposed mechanism achieves efficient and effective multi-hop routing of data and improves the reliability and efficiency of data transmission over the network. After extensive simulations, the achieved results of the proposed protocol are compared with state-of-the-art techniques, i.e., E-HARP, EB-MADM, PCRP and EERP. The results show significant improvement in network lifetime, network throughout, and end-to-end delay.

A wild food ethnobotanical field study was conducted in the Ishkoman and Yasin valleys, located in the Hindukush Mountain Range of Gilgit-Baltistan, northern Pakistan. These valleys are inhabited by diverse, often marginalized, linguistic and religious groups. The field survey was conducted via one hundred and eighty semistructured interviews to record data in nine villages. Forty gathered wild food botanical and mycological taxa were recorded and identified. Comparative analysis among the different linguistic and religious groups revealed that the gathered wild food plants were homogenously used. This may be attributed to the sociocultural context of the study area, where most of the population professes the Ismaili Shia Islamic faith, and to the historical stratifications of different populations along the centuries, which may have determined complex adaptation processes and exchange of possibly distinct pre-existing food customs. A few wild plants had very rarely or never been previously reported as food resources in Pakistan, including Artemisia annua, Hedysarum falconeri, Iris hookeriana, Lepidium didymium and Saussurea lappa. Additionally, the recorded local knowledge is under threat and we analyzed possible factors that have caused this change. The recorded biocultural heritage could, however, represent a crucial driver, if properly revitalized, for assuring the food security of the local communities and also for further developing ecotourism and associated sustainable gastronomic initiatives in the area.

Submerged floating tunnels (SFTs) are innovative structural solutions to waterway crossings, such as sea-straits, fjords and lakes. As the width and depth of straits increase, the conventional structures such as cable-supported bridges, underground tunnels or immersed tunnels become uneconomical alternatives. For the realization of SFT, the structural response under extreme environmental conditions needs to be evaluated properly. This study evaluates the displacements and internal forces of SFT under hydrodynamic and three-dimensional seismic excitations to check the global performance of an SFT in order to conclude on the optimum design. The formulations incorporate modeling of ocean waves, currents and mooring cables. The SFT responses were evaluated using three different mooring cable arrangements to determine the stability of the mooring configuration, and the most promising configuration was then used for further investigations. A comparison of static, hydrodynamic and seismic response envelope curves of the SFT is provided to determine the dominant structural response. The study produces useful conclusions regarding the structural behavior of the SFT using a three-dimensional numerical model.