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To understand the current status of water resource utilization and explore the coordination degree of the coupled human–water–ecosystem in the Tarim River Basin, we used the water shortage rates and index of WUE to analyze the dynamic changes in water shortage and water use efficiency. We also applied the Gini coefficient to study the evolutionary trend of the degree of matching between water consumption and GDP in each sector. Based on the above analysis, we developed a human–water–ecosystem coupling coordination degree model incorporating various indicators relevant to the three subsystems to quantitatively assess the coupled and coordinated development status of the three subsystems of the human–water–ecosystem in the basin from 2004 to 2020. The main findings are as follows: (1) The Tarim River Basin suffers from water shortage in dry years, with a prominent supply–demand contradiction. In the severe drought years of 2009 and 2014, the water shortage rates reached 10.20% and 10.93%, respectively. (2) From 2004 to 2020, the Tarim River Basin’s water use efficiency (WUE), and its five prefectures showed a clear upward trend. On a multi-year average, Bayingol Mongolian Autonomous Prefecture had the highest WUE, while the Hotan region had the lowest. (3) The multi-year average value of the match between water consumption and GDP for the entire basin is 0.28. By industry, the primary industry’s match between water consumption and GDP is higher, while the secondary and tertiary industries have lower matches. (4) From 2004 to 2020, the coordination degree of coupled human–water–ecosystem in the five prefectures showed different degrees of upward trend, generally developing towards better coordination. In terms of multi-year averages, Bayingol Mongolian Autonomous Prefecture, located in the east, has the highest degree of coupled coordination. Meanwhile, Hotan, in the south, lags significantly behind the remaining four regions. Therefore, the Tarim River Basin should further improve and optimize the development model of sustainable water resource use.

Promoting flexible energy demand through response programs in residential neighborhoods would play a vital role in addressing the issues associated with increasing the share of distributed solar systems and balancing supply and demand in energy networks. However, accurately identifying baseline-related energy measurements when activating energy demand response events remains challenging. In response, this study presents a deep learning-based, data-driven framework to improve short-term estimates of demand response baselines during the activation of response events. This framework includes bidirectional long-term memory (BiLSTM), long-term memory (LSTM), gated recurrent unit (GRU), convolutional neural networks (CNN), deep neural networks (DNN), and recurrent neural networks (RNN). Their performance is evaluated by considering different aggregation levels of the demand response baseline profile for 337 dwellings in the city of La Rochelle, France, over different time horizons, not exceeding 24 h. It is also compared with fifteen traditional statistical and machine learning methods in terms of forecasting accuracy. The results demonstrated that deep learning-based models, compared to others, significantly succeeded in minimizing the gap between the actual and forecasted values of demand response baselines at all different aggregation levels of dwelling units over the considered time-horizons. BiLSTM models, followed by GRU and LSTM, consistently demonstrated the lowest mean absolute percentage error (MAPE) in most comparison experiments, with values up to 9.08%, 8.71%, and 9.42%, respectively. Compared to traditional statistical and machine learning models, extreme gradient boosting (XGBoost) was among the best, with a value up to 11.56% of MAPE, but could not achieve the same level of forecasting accuracy in all comparison experiments. Such high performance reveals the potential of the proposed deep learning approach and highlights its importance for improving short-term estimates of future baselines when implementing demand response programs in residential neighborhood contexts.

The heavy metal burden on natural freshwater ecosystems is uninterruptedly increasing, which could affect their biodiversity, particularly regarding avian species. Three river barrages were selected for the sampling of water birds from autumn 2021 to spring 2022. Seven heavy metals—nickel (Ni), copper (Cu), cobalt (Co), zinc (Zn), lead (Pb), cadmium (Cd), and manganese (Mn)—were estimated in the hearts and livers of Fulica atra (Common Coot), Anas strepera (Gad-wall), and Anas crecca (Eurasian Teal) (Linnaeus, 1758) by atomic absorption spectrometry. The mean concentrations of metals were found to be significantly (p < 0.05) different among the sam-pling sites, species, and tissues. In the livers of F. atra and A. strepera, respectively, the highest mean concentration among the metals belonged to Zn (521 µg/g), and the lowest belonged to Cd (0.17 µg/g). The concentrations of Zn, Cu, Pb, Cd, and Co were highest in A. strepera heart samples. However, the concentrations of Zn, Pb, Cd, and Mn were found to be highest in the livers of F. atra. A comparison between both tissues indicated that the concentration of Ni is significantly (p < 0.05) higher (except for F. atra from Trimmu barrage) in the livers of water birds and that the concentration of Cd is significantly higher in the hearts of water birds. The mean metal concentrations were higher than the background limits reported in various studies, suggesting that the wintering water birds of Pakistan are under a significant load of heavy metal pollution. Histopathological analysis suggested that the observed heavy metals altered the normal histologies of hearts and livers of Fulica atra (Common Coot), Anas strepera (Gadwall), and Anas crecca (Eurasian Teal) sampled from three wetlands of Punjab Pakistan.

Patient satisfaction with prompt and high-quality healthcare services plays a pivotal role in healthcare settings. The delivery of high-quality services within the healthcare sector is closely associated with continuous quality improvement (CQI), which is an incremental and progressive process that prioritizes the safety of all participants, favorable outcomes, systematic processes, and a regulated and improved working environment, particularly in later stages. Surprisingly, these aspects are less frequently explored in Middle Eastern countries. Thus, this research paper aims to assess the impact of quality services on patient satisfaction in tertiary care clinics located in the Middle East. To improve the quality of services in our clinic, we employed patient feedback as a valuable resource. We proactively reached out to all patients who had visited our hospital via mobile phone messages and requested their feedback on the services they received. Approximately 5% of all visitors responded and completed a comprehensive questionnaire. The majority of respondents expressed satisfaction with the services provided across various departments. However, they also offered valuable suggestions that helped us identify further areas for improvement and enhance the overall patient experience within our clinic. Drawing upon the feedback received, we meticulously considered the identified issues, redesigned our policies, and implemented strategic changes. Following the implementation of these new approaches, we once again sought patients’ feedback on the quality of our services. Patient feedback highlighted the significant impact of optimized service delivery methods, resulting in a substantial increase in patient satisfaction. Overall, this study sheds light on the vital factors that can enhance patients’ experience in outpatient clinics, emphasizing the importance of integrating patient feedback into continuous quality improvement initiatives. By utilizing this approach, healthcare providers, administrators, and researchers can effectively improve service quality and patient satisfaction. Consequently, this research paper serves as a valuable reference for public health stakeholders, administrators, and researchers in their pursuit of delivering exceptional healthcare experiences.

Periodontal diseases are polymicrobial immune–inflammatory diseases that can severely destroy tooth-supporting structures. The critical bacteria responsible for this destruction include red complex bacteria such as Porphoromonas gingivalis, Tanerella forsythia and Treponema denticola. These organisms have developed adaptive immune mechanisms against bacteriophages/viruses, plasmids and transposons through clustered regularly interspaced short palindromic repeats (CRISPR) and their associated proteins (Cas). The CRISPR-Cas system contributes to adaptive immunity, and this acquired genetic immune system of bacteria may contribute to moderating the microbiome of chronic periodontitis. The current research examined the role of the CRISPR-Cas system of red complex bacteria in the dysbiosis of oral bacteriophages in periodontitis. Whole-genome sequences of red complex bacteria were obtained and investigated for CRISPR using the CRISPR identification tool. Repeated spacer sequences were analyzed for homologous sequences in the bacteriophage genome and viromes using BLAST algorithms. The results of the BLAST spacer analysis for T. denticola spacers had a 100% score (e value with a bacillus phage), and the results for T. forsthyia and P. gingivalis had a 56% score with a pectophage and cellulophage (e value: 0.21), respectively. The machine learning model of the identified red complex CRISPR sequences predicts with area an under the curve (AUC) accuracy of 100 percent, indicating phage inhibition. These results infer that red complex bacteria could significantly inhibit viruses and phages with CRISPR immune sequences. Therefore, the role of viruses and bacteriophages in modulating sub-gingival bacterial growth in periodontitis is limited or questionable.

The current study was designed to assess the comparative morphology of eight olive cultivars with different geographical origins and diverse genetic backgrounds, introduced to a new climatic zone. The morphological parameters of eight (five exotic and three domestic) olive cultivars (Bari Zaitoon-1, Bari Zaitoon-2, Favolosa (FS-17), Koroneiki, Balkasar, Ottobratica, Leccino, and Arbequina) were compared at the experimental area of the Department of Botany, The Islamia University of Bahawalpur, Pakistan (29°24′0″ North, 71°41′0″ East, 401–421 feet above sea level). Plant height, number of leaves/15 cm shoot, leaf size characteristics (leaf length, leaf width, leaf area, and length/width ratio), leaf shape characteristics (margin, leaf axil, base, and apex angles), leaf pigments (Chlorophyll a, Chlorophyll b, total chlorophyll contents, and carotenoids), phyllotaxy, and leaf color and venation were recorded. The highest plant height (28 cm) was obtained by Bari Zaitoon-2 followed by Bari Zaitoon-1 (24 cm), both of which are domestic cultivar of Pakistan, while the shortest height (5 cm) was obtained by Koroneiki. Leccino displayed the highest average number of leaves (17.8) on main shoot, followed by BARI-2 (16.4) and the lowest score was from Balkasar (10.4). Leaf area ranged from 5.66 cm2 (Bari Zaitoon-1) to 3.08 cm2 (Koroneiki). The longest leaf length (5.74 cm) was found in Bari Zaitoon-1 and the shortest (4.04 cm) in Koroneiki, while the broadest leaves were found in Leccino (1.54 cm) and the narrowest (1.12 cm) in Koroneiki. Bari Zaitoon-2 led in leaf length to width ratio (4.058) followed by Bari Zaitoon-1 (3.772) with small lanceolate leaves hardly reaching the value of 4, with the lowest value illustrated by Leccino. The total chloroplast pigments were highest in FS-17 followed by Bari Zaitoon-1 and Bari Zaitoon-2, while the lowest was in Arbequina. Chlorophyll a was highest in Bari Zaitoon-1 followed by FS-17 and Balkasar, with the lowest rate in Arbequina. Chlorophyll b content of FS-17 was the highest whereas the Chlorophyll b and total chlorophyll contents in Arbequina were the lowest of all the cultivars. The highest value of total carotenoids was found in Balkasar followed by FS-17 with the lowest value in Arbequina. The phyllotaxy was categorized into three types, i.e., alternate, opposite, and whorled. The combination of two or more types was usually observed on the same branch. The whorl of four leaves was also present in rare cases. Leaf venation was both pinnate and reticulate. The leaf base of most (four) of the olive cultivars, i.e., Arbequina, Balkasar, Leccino and FS-17, were cuneate having acute, rounded, apiculate, and cuspidate leaf tips, respectively. The findings revealed remarkable variations in olive morphology, especially in the leaves and a successful record of the preliminary data of olive cultivars from the study area was made. The present research demonstrated that local olive cultivars have unique characteristics that differentiate them from imported cultivars. Thus, local cultivars provide novel genetic resources that should be conserved.

The rapid growth of cloud computing has led to the development of the Software-Defined Network (SDN), which is a network strategy that offers dynamic management and improved performance. However, security threats are a growing concern, particularly with the SDN controller becoming an attractive target for malicious actors and potential Distributed Denial of Service (DDoS) attacks. Many researchers have proposed different approaches to detecting DDoS attacks. However, those approaches suffer from high false positives, leading to low accuracy, and the main reason behind this is the use of non-qualified features and non-realistic datasets. Therefore, the deep learning (DL) algorithmic technique can be utilized to detect DDoS attacks on SDN controllers. Moreover, the proposed approach involves three stages, (1) data preprocessing, (2) cross-feature selection, which aims to identify important features for DDoS detection, and (3) detection using the Recurrent Neural Networks (RNNs) model. A benchmark dataset is employed to evaluate the proposed approach via standard evaluation metrics, including false positive rate and detection accuracy. The findings indicate that the recommended approach effectively detects DDoS attacks with average detection accuracy, average precision, average FPR, and average F1-measure of 94.186 %, 92.146%, 8.114%, and 94.276%, respectively.

Software-defined networking (SDN) is a revolutionary innovation in network technology with many desirable features, including flexibility and manageability. Despite those advantages, SDN is vulnerable to distributed denial of service (DDoS), which constitutes a significant threat due to its impact on the SDN network. Despite many security approaches to detect DDoS attacks, it remains an open research challenge. Therefore, this study presents a systematic literature review (SLR) to systematically investigate and critically analyze the existing DDoS attack approaches based on machine learning (ML), deep learning (DL), or hybrid approaches published between 2014 and 2022. We followed a predefined SLR protocol in two stages on eight online databases to comprehensively cover relevant studies. The two stages involve automatic and manual searching, resulting in 70 studies being identified as definitive primary studies. The trend indicates that the number of studies on SDN DDoS attacks has increased dramatically in the last few years. The analysis showed that the existing detection approaches primarily utilize ensemble, hybrid, and single ML-DL. Private synthetic datasets, followed by unrealistic datasets, are the most frequently used to evaluate those approaches. In addition, the review argues that the limited literature studies demand additional focus on resolving the remaining challenges and open issues stated in this SLR.

Software-defined networking (SDN) is a unique network architecture isolating the network control plane from the data plane, offering programmable elastic features that allow network operators to monitor their networks and efficiently manage them. However, the new technology is security deficient. A DDoS attack is one of the common attacks that threaten SDN controllers, leading to the degradation or even collapse of the entire SDN network. Entropy-based approaches and their variants are considered the most efficient approaches to detecting DDoS attacks on SDN controllers. Therefore, this work analyzes the feasibility and impacts of an entropy-based DDoS attack detection approach for detecting low-rate and high-rate DDoS attacks against the controller, measured in terms of detection rate (DR) and false-positive rate (FPR), triggered by a single or multiple host attacks targeting a single or multiple victims. Eight simulation scenarios, representing low and high DDoS attack traffic rates on the controller, have been used to evaluate an entropy-based DDoS attack detection approach. The experimental results reveal that the entropy-based approach enhances the average DR for detecting high-rate DDoS attack traffic compared with low-rate DDoS attack traffic by 6.25%, 20.26%, 6.74%, and 8.81%. In addition, it reduces the average FPRs for detecting a high DDoS attack traffic rate compared with a low DDoS attack traffic rate by 67.68%, 77.54%, 66.94%, and 64.81.

The mathematical modelling and optimization of nonlinear problems arising in diversified engineering applications is an area of great interest. The Hammerstein structure is widely used in the modelling of various nonlinear processes found in a range of applications. This study investigates the parameter optimization of the nonlinear Hammerstein model using the abilities of the marine predator algorithm (MPA) and the key term separation technique. MPA is a population-based metaheuristic inspired by the behavior of predators for catching prey, and utilizes Brownian/Levy movement for predicting the optimal interaction between predator and prey. A detailed analysis of MPA is conducted to verify the accurate and robust behavior of the optimization scheme for nonlinear Hammerstein model identification.

The purpose of this research is to develop an efficient single-phase grid-connected PV system using a better performing asymmetrical multilevel inverter (AMI). Circuit component reduction, harmonic reduction, and grid integration are critical criteria for better inverter efficiency. The proposed inverter’s optimized topology requires seven unidirectional switches, three symmetric dc sources, and three diodes to produce an asymmetric fifteen level output; whereas, the same configuration will generate 7, 11, and 15-level output with an appropriate choice of dc source magnitudes. It is possible to reduce inverter losses and boost efficiency by decreasing the number of switches used. The integration of an asymmetric 15-level inverter with a grid-connected solar photovoltaic system is discussed in this article. A grid-connected solar photovoltaic (GCSPV) system is modelled and simulated using an asymmetric 15-level inverter. The dc sources of the 15-level inverter are replaced with PV sources. The results were analyzed with different operating temperatures and solar irradiance conditions. The GCSPV system is controlled by a closed-loop control system using Particle Swarm Optimization (PSO), Harris Hawk Optimization (HHO), and Hybrid Particle Swarm Optimization-Genetic Algorithm (PSOGA) based Proportional plus Integral (PI) controllers. Grid voltage, grid current, grid power, and total harmonic distortion (THD) of grid currents were analyzed. The performance of the 15-level asymmetric inverter was evaluated by comparing the THD of the grid current and the efficiency of the grid-connected photovoltaic system.

Axial flux permanent magnet (AFPM) machines are good candidates for electric vehicle applications due to their high torque density, improved efficiency, and better flux distribution; thus, they are often used. A dual-rotor single-stator AFPM machine with four differently shaped permanent magnet (PM) rotors is investigated. The main aim of this paper is to enhance the average torque while minimizing the cogging torque and torque ripples at the expense of low PM volume. The proposed machines are analyzed in terms of flux linkage, back-EMF, cogging torque, average torque, and torque ripples. The analysis reveals that the machine with an arc-shaped PM rotor performs better than the others. In addition, the trapozoidal arc-shaped PM used in the AFPM machine outperforms the hexagonal, skew arc, and traditional trapezoidal PMs. The torque density of the trapezoidal-shaped PM machine is 40.23 (KNm/m3), while that of the hexagonal shape is 32.46 (KNm/m3), that of the skew arc shape is 39.78 (KNm/m3), and that of the arc shape is 50.38 (KNm/m3).

Slotting effect in electric machines reduces flux per pole that effect magnetic flux density distribution in the air gap which induces harmonics in magnetic flux density causing flux pulsation, that in turn generates dominant torque pulsation in the form of cogging torque and torque ripples. To overcome the abovesaid demerits, a novel outer rotor field-excited flux-switching machine (OR-FSFSM) with a combined semi-closed and open slots stator is proposed in this study. The developed OR-FEFSM offers a high-power factor, due to the utilization of the semi-closed slot for armature coils. The open slot stator structure was chosen for the field excitation coil, which effectively suppresses leakage reluctance that causes flux pulsation. Thus, the influence of torque ripples is reduced, and the average torque is improved. In order to investigate the effectiveness of the proposed OR-FEFSM, a detailed study of stator slot and rotor pole combinations are performed. Based on simplified mathematical formulation, 12S/7P (stator slot/rotor poles), 12S/11P, 12S/13P, and 12S/17P are the most feasible combinations. Finite Element Analysis (FEA) based on comprehensive electromagnetic performance is performed on each combination, and found that 12S/13P offers the highest average torque of 4.62 Nm, whereas 3.72 Nm, 2.72Nm, and 1.68 Nm average torque is offered by 12S/17P, 12S/7P, and 12S/11P, respectively. Based on the initial analysis, 12S/13P was considered for further analysis and optimized using JMAG built-in Genetic Algorithm (GA). Moreover, thermal analysis was performed, and the proposed design was compared with the conventional design.

Nature-inspired metaheuristic algorithms have gained great attention over the last decade due to their potential for finding optimal solutions to different optimization problems. In this study, a metaheuristic based on the dwarf mongoose optimization algorithm (DMOA) is presented for the parameter estimation of an autoregressive exogenous (ARX) model. In the DMOA, the set of candidate solutions were stochastically created and improved using only one tuning parameter. The performance of the DMOA for ARX identification was deeply investigated in terms of its convergence speed, estimation accuracy, robustness and reliability. Furthermore, comparative analyses with other recent state-of-the-art metaheuristics based on Aquila Optimizer, the Sine Cosine Algorithm, the Arithmetic Optimization Algorithm and the Reptile Search algorithm—using a nonparametric Kruskal–Wallis test—endorsed the consistent, accurate performance of the proposed metaheuristic for ARX identification.

In this paper, a defused decision boundary which renders misclassification issues due to the presence of cross-pairs is investigated. Cross-pairs retain cumulative attributes of both classes and misguide the classifier due to the defused data samples’ nature. To tackle the problem of the defused data, a Tomek Links technique targets the cross-pair majority class and is removed, which results in an affine-segregated decision boundary. In order to cope with a Theft Case scenario, theft data is ascertained and synthesized randomly by using six theft data variants. Theft data variants are benign class appertaining data samples which are modified and manipulated to synthesize malicious samples. Furthermore, a K-means minority oversampling technique is used to tackle the class imbalance issue. In addition, to enhance the detection of the classifier, abstract features are engineered using a stochastic feature engineering mechanism. Moreover, to carry out affine training of the model, balanced data are inputted in order to mitigate class imbalance issues. An integrated hybrid model consisting of Bi-Directional Gated Recurrent Units and Bi-Directional Long-Term Short-Term Memory classifies the consumers, efficiently. Afterwards, robustness performance of the model is verified using an attack vector which is subjected to intervene in the model’s efficiency and integrity. However, the proposed model performs efficiently on such unseen attack vectors.

The number of unsecured and portable Internet of Things (IoT) devices in the smart industry is growing exponentially. A diversity of centralized and distributed platforms have been implemented to defend against security attacks; however, these platforms are insecure because of their low storage capacities, high power utilization, single node failure, underutilized resources, and high end-to-end delay. Blockchain and Software-Defined Networking (SDN) are growing technologies to create a secure system and to ensure safe network connectivity. Blockchain technology offers a strong and trustworthy foundation to deal with threats and problems, including safety, privacy, adaptability, scalability, and security. However, the integration of blockchain with SDN is still in the implementation phase, which provides an efficient resource allocation and reduced latency that can overcome the issues of industrial IoT networks. We propose an energy-efficient blockchain-integrated software-defined networking architecture for Industrial IoT (IIoT) to overcome these challenges. We present a framework for implementing decentralized blockchain integrated with SDN for IIoT applications to achieve efficient energy utilization and cluster-head selection. Additionally, the blockchain-enabled distributed ledger ensures data consistency throughout the SDN controller network and keeps a record of the nodes enforced in the controller. The simulation result shows that the proposed model provides the best energy consumption, end-to-end latency, and overall throughput compared to the existing works.

The present research concerns the development of a new device and process intended for the purification and treatment of sulfurous elements, and more particularly, of hydrogen sulfide (H2S) from the biogas produced at the time of the anaerobic fermentation in the purification stations. The controlled dumps or any other unit are likely to produce biogas with concentrations of H2S harmful to the operation of the machines for the valorization of the produced biogas or deodorization. This device uses new biochar from a mixture of dehydrated digested sludge from sewage treatment plants and margins from traditional crushing units, followed by biological treatment in a liquid medium at a controlled temperature. The liquid medium is based on a margin (nutrient) with culture support (large granules of biochar) in suspension by the injection under the pressure of biogas coming from the biochar filter. Physico-chemical characterization of the biochar and a test practiced on the new device of raw biogas treatment were realized. The results showed that the newly synthesized biochar has a low specific surface and a highly undeveloped porosity. The spectrum corresponding to the images of the biochar reveals the presence of C, O, N, Al, Si, P, and Fe, as significant elements with the following respective percentages: 37.62%, 35.78%, 1.87%, 4.26%, 7.33%, 8.56%, and 4.58%. It is important to note that the C content of the biochar thus synthesized found by EDX is quite comparable to that estimated from ATG. Biogas treatment test results on the prototype object of the invention eliminated 97% of the H2S from the biogas produced.

Rice yellow mottle virus (RYMV), transmitted by chrysomelids, is one of the major viral pathogens that has devastated rice production in Sub-Saharan Africa. RYMV is a member of the genus Sobemoviruses in the family Solemoviridae and harbors a positive-sense single-stranded RNA (+ssRNA). Here, we used 50 RYMV strains, applying the codon usage bias (CUB). Both base content and relative synonymous codon usage (RSCU) analysis revealed that GC-ended codons were more frequently used in the genome of RYMV. Further low codon usage bias was observed from the effective number of codons (ENC) value. The neutrality plot analysis suggested the dominant factor of natural selection was involved in the structuring of CUB in RYMV. Based on RSCU values, the RYMV and its host relationship indicate that the RYMV develops codon usage patterns similar to its host. Generally, both natural selection and mutational pressure impact the codon usage pattern of the protein-coding genes in RYMV. This study is important because it characterized the codon usage pattern in the RYMV genomes and provided the necessary data for a basic evolutionary study on them. Additionally, we recommend that experiments such as whole genome sequencing (WGS) or dual RNA sequencing (DRS) should be considered in order to correlate these in-silico findings with viral diseases in the future.

Depression is one of the most common mental health disorders, affecting approximately 280 million people worldwide. This condition is defined as emotional dysregulation resulting in persistent feelings of sadness, loss of interest and inability to experience pleasure. Early detection can facilitate timely intervention in the form of psychological therapy and/or medication. With the widespread public adoption of wearable devices such as smartwatches and fitness trackers, it is becoming increasingly possible to gain insights relating the mental states of individuals in an unobtrusive manner within free-living conditions. This work presents a machine learning (ML) approach that utilizes retrospectively collected data-derived consumer-grade wearables for passive detection of depression severity. The experiments conducted in this work reveal that multimodal analysis of physiological signals in terms of their discrete wavelet transform (DWT) features exhibit considerably better performance than unimodal scenarios. Additionally, we conduct experiments to view the impact of severity on emotional valence-arousal detection. We believe that our work has implications towards guiding development in the domain of multimodal wearable-based screening of mental health disorders and necessitates appropriate treatment interventions.

The advent of carbapenem-resistant Klebsiella pneumoniae (CRKP) poses a significant challenge to public health, as carbapenems are typically employed as a last resort to treat nosocomial infections caused by such organisms, especially in intensive care units (ICUs). This study aims to characterize the CRKP isolated from patients admitted to the Zagazig University Hospitals (ZUHs) ICU in Egypt. About 56.2%, 41.0%, and 32.4% of the isolates indicated the presence of blaNDM, blaOXA-48, and blaKPC, respectively. Carbapenemase-encoding genes were found in many isolates, and blaNDM was the most predominant gene. Nevertheless, this situation has become a heavy burden in developing countries, including Egypt, and is associated with substantial morbidity, mortality, and increased healthcare expenses.

This paper presents a novel linear hybrid excited flux switching permanent magnet machine (LHEFSPMM) with a crooked tooth modular stator. Conventional stators are made up of a pure iron core, which results in high manufacturing costs and increased iron core losses. Using a modular stator lowers the iron volume by up to 18% compared to a conventional stator, which minimizes the core losses and reduces the machine’s overall cost. A crooked angle is introduced to improve the flux linkage between the stator pole and the mover slot. Ferrite magnets are used with parallel magnetization to reduce the cost of the machine. Two-dimensional FEA is performed to analyze and evaluate various performance parameters of the proposed machine. Geometric optimization is used to optimize the split ratio (S.R) and winding slot area (Slotarea). Genetic algorithm (GA) is applied and is used to optimize stator tooth width (STW), space between the modules (SS), crooked angle (α), and starting angle (θ). The proposed model has a high thrust density (306.61 kN/m3), lower detent force (8.4 N), and a simpler design with higher efficiency (86%). The linear modular structure makes it a good candidate for railway transportation and electric trains. Thermal analysis of the machine is performed by FEA and then the results are validated by an LPMEC model. Overall, a very good agreement is observed between both the analyses, and relative percentage error of less than 3% is achieved, which is considerable since the FEA is in 3D while 2D temperature flow is considered in the LPMEC model.

The increasing incidence of distributed denial-of-service (DDoS) attacks has made software-defined networking (SDN) more vulnerable to the depletion of controller resources. DDoS attacks prevent the SDN controller from processing all incoming data efficiently, potentially disrupting a network or denying legitimate users access to network services. Thus, the protection of the SDN controller is crucial, especially from the ones that exploit the SDN characteristics. In this paper, the authors propose an efficient detection approach for low- and high-rate DDoS attacks on the controller with a high detection rate and a low false positive rate by adapting a dynamic threshold algorithm rather than a static one and proposing a new rule-based detection mechanism. In addition, the proposed approach was evaluated using eight simulation scenarios representing all potential attacks against the SDN controller in terms of attack traffic rates (low or high), sources (either single or multiple hosts), and targets (single or multiple victims). The experiment results show that the proposed approach is more effective than the existing approaches based on attack detection and false positive rates.

Internet Protocol version six (IPv6) is more secure than its forerunner, Internet Protocol version four (IPv4). IPv6 introduces several new protocols, such as the Internet Control Message Protocol version six (ICMPv6), an essential protocol to the IPv6 networks. However, it exposes IPv6 networks to some security threats since ICMPv6 messages are not verified or authenticated, and they are mandatory messages that cannot be blocked or disabled. One of the threats currently facing IPv6 networks is the exploitation of ICMPv6 messages by malicious actors to execute distributed denial of service (DDoS) attacks. Therefore, this paper proposes a deep-learning-based approach to detect ICMPv6 flooding DDoS attacks on IPv6 networks by introducing an ensemble feature selection technique that utilizes chi-square and information gain ratio methods to select significant features for attack detection with high accuracy. In addition, a long short-term memory (LSTM) is employed to train the detection model on the selected features. The proposed approach was evaluated using a synthetic dataset for false-positive rate (FPR), detection accuracy, F-measure, recall, and precision, achieving 0.55%, 98.41%, 98.39%, 97.3%, and 99.4%, respectively. Additionally, the results reveal that the proposed approach outperforms the existing approaches.

Nitrogen fertilization and irrigation patterns have been extensively studied for common maize (Zea mays L.), but there is limited published work for Zhengda 619, especially in subtropical areas. Nitrogen (N) fertilizer and irrigation play an important role in crop growth and yield improvements. The study aimed to investigate the yield, growth, chlorophyll content, reactive oxygen species (ROS) and enzyme activities of hybrid maize (Zhengda 619) under greenhouse conditions. Individual plants grown in plastic pots were subjected to two irrigation types—low irrigation (LW; 60% field capacity) and high irrigation water (HW; 80% field capacity)—and five N rates. Our results demonstrate that the LW irrigation increased dry matter, kernel yield, leaf chlorophyll, total root length, root diameter, root volume, and root surface area, as well as soil enzymes and plant antioxidant enzymes, while it lowered malondialdehyde (MDA), proline, and ROS. Moreover, most of the above parameters increased with increasing N application rates up to N3 under LW irrigation due to the increased N availability to the plant and soil enzymes. It is concluded that increasing N rates could improve soil enzyme activities as well as plant antioxidant enzymes and decrease ROS, ultimately resulting in a higher kernel yield under LW irrigation.

Melatonin is effective in modulating metabolism and regulating growth and development in many plants under biotic and abiotic stress. However, there is no systematic quantification of melatonin effects on maize growth, gas exchange, chlorophyll content, and the antioxidant defense system. A meta-analysis was conducted on thirty-two currently available published articles to evaluate the effect of stress types, study types, and maize varieties on response ratio (lnRR++) of “melatonin” to “control (no melatonin)” on plant growth, enzyme activities, gas exchange parameters, and photosynthetic pigments. Our findings revealed that melatonin application overall increased plant height, leaf area, root length, fresh and dry root weight and shoot weight, superoxide dismutase (SOD), peroxide (POD), catalase (CAT), ascorbate peroxidase (APX), soluble sugar and protein, photosynthetic rate, stomatal conductance, transpiration rate, chlorophyll, and carotenoid in maize leaf under stress conditions. In contrast, melatonin application decreased the levels of hydrogen peroxide (H2O2), superoxide anion (O2−), malondialdehyde (MDA), and electrolyte leakage. The categorical meta-analysis demonstrated that melatonin application to chilling stress resulted in higher SOD activity followed by salt stress. Melatonin application to all stress types resulted in higher POD, CAT and APX activities, except Cd stress, which had no effect on POD and decreased CAT by 38% compared to control. Compared to control, melatonin resulted in lower reactive oxygen species (ROS) and electrolyte leakage under no stress, Cd, drought, salt, lead, heat, and chilling stress in all study types (pot, growth chamber, hydroponic, and field), except O2 content which was not affected in pot and growth chamber studies. It was concluded that melatonin alleviates oxidative damage by improving stress tolerance, regulating the antioxidant defense system, and increasing leaf chlorophyll content compared to control.

Traditional diets exist in all cultures and geographic regions, and they often represent healthy eating options. Traditional culinary preparations have, however, often undergone profound change, even in the isolated Himalayan region. Therefore, we adapted methods to identify traditional plant foraging activities to better understand their significance in food system sustainability, as well as to promote innovative local gastronomies. Information on wild food and foraging practices was gathered from varied ethnic groups such as Kashmiri, Gujjars, Pahari, Dogra, Bakarwal, Balti, Beda and Brokpa through interviews (n = 716) and group discussions (n = 67) in four bio-geographic regions of the Jammu and Kashmir Himalayas (J&KH). The data were subjected to ordination techniques (Principal Component Analysis) via R software Ver. 4.0.0. We documented 209 food species, of which 73% were plants and 27% animals, used by the inhabitants of four bio-geographic regions of J&KH. The highest number of food plant species was recorded in Indian Kashmir, followed by Jammu, Azad Kashmir and Ladakh (81, 65, 60 and 27 species, respectively), and the maximum number of animal species was reported in Indian Kashmir, followed by Azad Kashmir, Ladakh and Jammu (33, 21, 19 and 17 species, respectively). The Azad Kashmir and Indian Kashmir regions showed greater similarity, whereas the least overlap was observed between Kashmir and Ladakh. The PCA showed considerable variation between different regions, and specific groups of species were more related to one specific region than others. The reported uses of Abies pindrow, Acacia modesta, Bergenia ciliata, Bergenia stracheyi and Juglans regia among plants, and Jynx torquilla, Streptopelia orientalis and Tadorna ferruginea among animals, are novel for the gastronomy of this part of the Himalayan region. We also recorded for the first time from this region seven unique food preparations of wild animals. This study documented extensive traditional knowledge on the usage of wild species, and is the first scientific description of wild food species and their vernacular names in the Western Himalayas, Jammu and Kashmir. Our findings can contribute significantly to combating food insecurity by revitalizing and reconsidering the rich bio-cultural food heritage around which local traditional communities have developed their food systems.

In hot and dry regions such as the Gulf Cooperation Council (GCC) countries, the cooling demand is often responsible for more than 70% of electricity consumption, which places a massive strain on the electricity grid and leads to significant emissions. Solar thermal driven Silica-Gel/Water adsorption chillers, used for space cooling, could provide low carbon emission cooling and reduce the reliance on grid electricity. However, a meticulous design is required to make this both economically and environmentally beneficial. This paper aims to evaluate the solar thermal adsorption chiller performance based on large-scale cooling demand through a TRNSYS simulation for 1 year of operation. The proposed system consists of two main parts: first, the solar loop with evacuated tube solar collectors; and second, the adsorption cooling system with a silica-gel/water adsorption chiller. A neighbourhood of 80 typical 197 m2 villas in Riyadh, the capital city of the Kingdom of Saudi Arabia (KSA), was taken as a case study. The solar adsorption cycle’s performance has been compared to the conventional vapour compression cycle in terms of energy, economic and environmental performance. In addition, a parametric study has been performed for the main design parameters. Results reveal that the system can reach a solar fraction of 96% with solar collector area of 5500 m2 and a storage tank volume between 350 and 400 m3. Furthermore, the annual energy cost can be reduced by 74% for the solar adsorption system compared to the conventional vapour compression cycle. Meanwhile, the CO2 saving percentage for the solar adsorption cycle was approximately 75% compared to the conventional vapour compression cycle. Carefully designed solar thermal cooling systems can reduce greenhouse gas emissions while covering a large scale of cooling demands. This can reduce the strain on the electricity grid as well as greenhouse gas emissions.

COVID-19 has proved to be a serious, and consequential disease that has affected millions of people globally. Previously, the adverse effects of proton pump inhibitors (PPI) have been observed with increasing the risk of pneumonia and COVID-19. This meta-analysis aims to address the relationship between the use of PPI and the severity of COVID-19 infection. We conducted a systemic literature search from PUBMED, Science Direct, and Cinahl from December 2019 to January 2022. Published and unpublished randomized control trials and cohort studies were included. Review Manager was used for all statistical analyses. In total, 14 studies were included in this systemic review and meta-analysis. Outcomes of interest include: (1) susceptibility of COVID-19 infection and (2) severity of COVID-19 (defined as the composite of poor outcomes: ICU admission, need for oxygen therapy, need for a ventilator, or death), and (3) mortality due to COVID-19. PPI use was marginally associated with a nominal but statistically significant increase in the risk of COVID-19 infection (OR 1.05 [1.01, 1.09]; I2 97%, p = 0.007). PPI use also increased the risk of the composite poor outcome (OR 1.84 [1.71, 1.99]; I2 98%, p < 0.00001) and mortality (OR 1.12 [1.00, 1.25]; I2 84%, p = 0.05) in patients with COVID-19.

Population growth, food shortages, climate change and water scarcity are some of the frightening challenges being confronted in today’s world. Water deficit or drought stress has been considered a severe limitation for the productivity of rice, a widely popular nutritive cereal crop and the staple food of a large portion of the population. A key stage in crop growth is seed emergence, which is mostly constrained by abiotic elements such as high temperatures, soil crusting and low water potential, which are responsible for poor stand establishment. Seed priming is a pre-sowing treatment of seeds that primes them to a physiological state that allows them to emerge more proficiently. The purpose of this study was to investigate the potential of leaf extracts from local and exotic moringa landraces as seed priming agents in rice cultivated under water deficit (75% field capacity) and control conditions (100% field capacity). Rice seeds were placed in an aerated solution of moringa leaf extract (MLE) at 3% from three obtained landraces (Faisalabad, Multan and an exotic landrace of India). The results obtained from the experimentation show that the water deficit regime adversely affected the studied indicators including emergence and growth attributes as well as physiological parameters. Among the priming agents, MLE from the Faisalabad landrace significantly improved the speed and spread of emergence of rice seedlings (time to start emergence at 23%, emergence index at 75%, mean emergence time at 3.58% and final emergence percentage at 46%). All the priming agents enhanced the growth, photosynthetic pigments, gas exchange parameters and antioxidant activities, particularly under the water deficit regime, but the maximum improvement was recorded by the MLE from the Faisalabad landrace. Therefore, the MLE of the Faisalabad landrace can be productively used to boost the seedling establishment and growth of rice grown under normal and water deficit conditions.

Quinoa is a climate-resilient food grain crop that has gained significant importance in the last few years due to its nutritional composition, phytochemical properties and associated health benefits. Quinoa grain is enriched in amino acids, fiber, minerals, phenolics, saponins, phytosterols and vitamins. Quinoa possesses different human-health promoting biological substances and nutraceutical molecules. This review synthesizes and summarizes recent findings regarding the nutrition and phytochemical properties of quinoa grains and discusses the associated biological mechanisms. Quinoa grains and grain-based supplements are useful in treating different biological disorders of the human body. Quinoa is being promoted as an exceptionally healthy food and a gluten-free super grain. Quinoa could be used as a biomedicine due to the presence of functional compounds that may help to prevent various chronic diseases. Future research needs to explore the nutraceutical and pharmaceutical aspects of quinoa that might help to control different chronic diseases and to promote human health.

The present research investigated the chemical characterization and insecticidal activity of n-Hexane extracts of Epaltes divaricata (NH-EDx) along with their chief derivatives n-Hexadecanoic acid (n-HDa) and n-Octadecanoic acid (n-ODa) against the dengue vector Aedes aegypti and lepidopteran pest Spodoptera litura. Chemical screening of NH-EDx through GC–MS analysis delivered nine major derivatives, and the maximum peak area percentage was observed in n-Hexadecanoic acid (14.63%) followed by n-Octadecadienoic acid (6.73%). The larvicidal activity of NH-EDx (1000 ppm), n-HDa (5 ppm), and n-ODa (5 ppm) against the A. aegypti and S. litura larvae showed significant mortality rate in a dose-dependent way across all the instars. The larvicidal activity was profound in the A. aegypti as compared to the S. litura across all the larval instars. The sublethal dosages of NH-EDx (500 ppm), n-HDa (2.5 ppm), and n-ODa (2.5 ppm) also showed alterations in the larval/pupal durations and adult longevity in both the insect pests. The enzyme activity revealed that the α- and β-carboxylesterase levels were decreased significantly in both the insect pests, whereas the levels of GST and CYP450 uplifted in a dose-dependent manner of NH-EDx, n-HDa, and n-ODa. Correspondingly, midgut tissues such as the epithelial layer (EL), gut lumen (GL), peritrophic matrix (Pm), and brush border membrane (BBM) were significantly altered in their morphology across both A. aegypti and S. litura against the NH-EDx and their bioactive metabolites. NH-EDx and their bioactive metabolites n-HDa and n-ODa showed significant larvicidal, growth retardant, enzyme inhibition, and midgut toxicity effects against two crucial agriculturally and medically challenging insect pest of ecological importance.

Whitefly (Bemisia tabaci)-transmitted Geminiviruses cause serious diseases of crop plants in tropical and sub-tropical regions. Plants, animals, and their microbial symbionts have evolved complex ways to interact with each other that impact their life cycles. Blocking virus transmission by altering the biology of vector species, such as the whitefly, can be a potential approach to manage these devastating diseases. Virus transmission by insect vectors to plant hosts often involves bacterial endosymbionts. Molecular chaperonins of bacterial endosymbionts bind with virus particles and have a key role in the transmission of Geminiviruses. Hence, devising new approaches to obstruct virus transmission by manipulating bacterial endosymbionts before infection opens new avenues for viral disease control. The exploitation of bacterial endosymbiont within the insect vector would disrupt interactions among viruses, insects, and their bacterial endosymbionts. The study of this cooperating web could potentially decrease virus transmission and possibly represent an effective solution to control viral diseases in crop plants.

The stabilisation of G-quadruplexes (G4s) by small-molecule compounds is an effective approach for causing cell growth arrest, followed by cell death. Some of these compounds are currently being developed for the treatment of human cancers. We have previously developed a substituted naphthalene diimide G4-binding molecule (CM03) with selective potency for pancreatic cancer cells, including gemcitabine-resistant cells. We report here that CM03 and the histone deacetylase (HDAC) inhibitor SAHA (suberanilohydroxamic acid) have synergistic effects at concentrations close to and below their individual GI50 values, in both gemcitabine-sensitive and resistant pancreatic cancer cell lines. Immunoblot analysis showed elevated levels of γ-H2AX and cleaved PARP proteins upon drug combination treatment, indicating increased levels of DNA damage (double-strand break events: DSBs) and apoptosis induction, respectively. We propose that the mechanism of synergy involves SAHA relaxing condensed chromatin, resulting in higher levels of G4 formation. In turn, CM03 can stabilise a greater number of G4s, leading to the downregulation of more G4-containing genes as well as a higher incidence of DSBs due to torsional strain on DNA and chromatin structure.