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Blockchain is a technology that has gained popularity in recent years due to its immutability and decentralized features. Various industries have started integrating this technology into their business models, resulting in the development of various types of blockchain networks. This paper reviews the latest academic advancements in sharding techniques within the various types of sharded blockchain networks implemented in the past five years to identify their strengths and weaknesses. The increasing volume of transactions in blockchain networks has caused issues in terms of scalability, transaction efficiency, storage, and security, which obstruct the global adoption of blockchain technology. Sharding in blockchain is a solution that aims to address these issues through various types of sharding techniques, such as network, transaction, or state sharding, along with the various types of networks and systems. The general definition of sharding is the partitioning of the network into subsets known as shards, which allows them to validate transactions in parallel, resulting in performance and scalability improvements of the existing network. The existing sharding techniques (before 2020) and the latest sharding techniques (2020–2024), along with their characteristics, advantages, and disadvantages, are also discussed. An analysis based on the performance evaluation of recent sharding techniques was conducted to further understand the impact of these techniques. The result of the review provides critical insights into the advancements in sharding techniques and fosters innovation while facilitating the widespread adoption of sharding-based solutions in the blockchain ecosystem.

The exponential growth of intelligent systems technologies, including Artificial Intelligence (AI), Internet of Things (IoT), Machine Learning (ML), and Smart Connected Products, has intensified the difficulties of data governance. Organizations adopting these technologies face challenges in managing the volume, variety, and velocity of data while ensuring quality, security, compliance, and ethical integrity. This study investigates the state of big data governance (BDG) programs in organizations leveraging intelligent systems technologies, using a systematic literature review guided by PRISMA. A synthesis of insights from 74 peer-reviewed articles, conferences, and industry reports reveals both the significant benefits of BDG such as enhanced decision-making, operational efficiency, and regulatory compliance and persistent challenges, including fragmented governance frameworks, limited scalability, data quality issues, and ethical concerns. To address these gaps, this study proposes the big data governance maturity assessment model (BDG MAM), a novel framework developed to assess and enhance the maturity of BDG programs. The BDG MAM evaluates governance maturity across four key dimensions: people, process, data, and technology. It provides a structured roadmap for organizations to benchmark governance practices, prioritize improvements, and implement effective strategies. The model was validated through a pilot study conducted with a public higher learning institution, demonstrating its practical applicability and effectiveness in real-world scenarios. By bridging theoretical insights with practical implementation, this study advances academic discourse and provides practitioners with a robust approach to navigate the challenges of modern data governance practices, ensuring sustainable and effective management of heterogeneous data environment.

Background: The coronary arteries, being the major vascular channels, are known to affect the complete circulatory system and hemodynamics of the body. However, as a matter of fact, the coronary arteries are known to have many anatomical and developmental variations along all the morphological aspects, which include location of orifices, various routes that the artery and its tributaries follow, and of course, the pattern of branching of the main artery. The developmental anomalies in the arterial structures result in abnormalities of arterial blood flow, hypoxia, and other conditions which may lead to atherosclerosis of the coronary artery. To the best of our knowledge, the current systematic review and meta-analysis aim to assess the association between variations in the branching pattern of the coronary artery. Moreover, we propose future studies to assess the association and preventive strategies for clinicians and various health-care workers. Methodology: We performed an electronic search of PubMed, MEDLINE, Cochrane Central Register, EMBASE, Science Citation Index, Web of Science, and Database of Abstracts of Reviews of Effects, and various clinical tribulation registries till 2022. Two independent reviewers extracted the data, and the present meta-analysis was performed with the help of RevMan version 5.4. For the present meta-analysis, we used 10-year database from 2012 to 2022. Results: Appropriate data were fetched from studies that were included for the meta-analysis, and the data were presented in tables for evaluation and drawing of accurate results. A total of 904 articles were found after searching all of the major medical databases. Thirteen more sources of reference were included in the search. After primary screening, full text articles of around 148 were analyzed, and among them, 114 were excluded because those articles were in the category of #case series, reports, or #letters to the editors, #animal studies or #irrelevant documents, reporting duplicate documents, or broadcasting a certain variant as opposed to occurrence. After the complete screening and analysis of the data were done, the final number of articles that were included in the meta-analysis was 29 (n = 29). Conclusion: The present meta-analysis is an attempt to review and analyze the recent literature published on the variation in anatomy and developmental abnormalities of the coronary arteries in the differing population across the geography of the globe. The structural aspects of the coronary arteries, and not just the lifestyle issues, are the ones which are associated with coronary artery disease (CAD), a major cause of mortality in the population. However, the anatomical factors are not attending limelight as other obesity- or age-related issues for the same. Hence, the present review aimed to deliver a thorough understanding of the most predominant branching pattern of the coronary artery seen among Asian population. This study will be beneficial to cardiologists and radiologists when performing various treatments such as coronary angiograms, coronary angioplasty, bypass grafting, and other medical procedures and can also lead to prevention of the CAD.

Abstract Adding organic soil amendments can improve the physical and hydrological properties of soil, subsequently enhancing fertility for better crop production. In this study, spent Arabica and Columbian coffee wastes and their respective biochars were evaluated as soil amendments to improve the physical and hydrological properties of loamy sand soil and enhance maize (Zea mays L.) crop growth. Spent Arabica coffee (AC) and Columbian coffee (CC) wastes were collected and transformed into biochar through pyrolysis process at 550 °C with a residence time of 3 h and pyrolysis rate of 5 °C per minute. The AC and CC derived biochar were termed as ABC and CBC, respectively. The produced soil amendments were applied to soil at 0% (control), 1%, 3%, and 5% in a column setup. The moisture characteristics, including water infiltration, evaporation, and water retention, were investigated. Thereafter, the prepared amendments were applied to loamy sand soils at 0% (control), 1%, 3%, and 5% (w/w) application rates. Maize growth was then observed for a period of 30 days under greenhouse conditions. Results of the column trials showed that ABC and CBC applied at 5% reduced the cumulative water evaporation by 57%–66% and cumulative infiltration by 124%–181% compared to control. Likewise, 5% application of ABC and CBC resulted in 101 to 130% higher water retention in loamy sand soil. Results of the greenhouse experiment showed that 5% application of ABC and CBC amendments resulted in root biomass of 2.12 and 2.38 g, respectively, as compared to 0.51 g in control treatment. Similar treatments resulted in shoot biomass of 9.70 and 9.93 g respectively, as compared to 7.37 g in control. Likewise, 5% application of CBC and ABC increase plant height from 15.71 to 30.94 cm in ABC and 33.23 cm in CBC. Overall, 5% application of coffee waste-derived biochars significantly reduced water evaporation and infiltration, while increasing soil water retention and maize plant height, root biomass, and shoot biomass. Therefore, spent coffee waste-derived biochar could effectively be employed to improve physical and hydrological properties of loamy sand soils for better crop productivity.

Reactive powder concrete is an attractive new concrete technology for structural design applications. The technology has been used for different structures with several advantages. Strengthening and retrofitting structures using FRP has grown rapidly in the last decades to upgrade structures because of the gradual material degradation over time. The technique was applied mainly for conventional normal and high-strength concrete and steel. This study is conducted to study the performance of bonding interface among reactive powder concrete substrate and CFRP sheets using different parameters, including bond length, bond width, and bond–slip relations. Concrete prisms with dimensions of (25 x 25 x 75 mm) were adopted in the experimental program. Five groups of three specimens were tested using a single-lap shear test setup, and pull-out loading was applied for testing the specimens. Test results indicated significant bonding properties compared to the normal concrete.

The use of modeling for water conservation during irrigation is still in the research stage. There are many models can be used for this purpose, including Saltmed model. The SALTMED model is a holistic model can be used for different irrigation water and soil qualities, variety of crops, and water irrigation systems. This research aimed to interpret the tomato yield using different water quantities and qualities under greenhouse experiments. The trial included deficit irrigation with 3 treatments (80 %, 60 %, 40 % along with 100 % crop water requirements (ETc), with three water qualities (ECw 1.0, 2.3 and 3.6 dSm−1). The outcomes of the SALTMED model indicate the potential of the model to interpret soil-water content, salinity and tomato yield in regions having semi-arid climates under deficit irrigation strategies. Results of the SALTMED model revealed that data was underestimated. Furthermore, the values range of the CRM coefficient of the residual mass were 0.005–0.055, -0.01–0.02, and -0.02–0.04 for 0.9, 2.3 and 3.6 dSm−1, respectively. Most salinity showed the CRM values 0.18 to -0.02, 0.2 to -0.41, and -0.05 to -0.38 respectively, for the three irrigation water qualities. The values of observed and simulated data predicted a good relationship obtained. For soil water content and salinity, R2 values ranged between 0.80–0.99 and 0.74–0.99 respectively. Model efficiently simulates tomato yield outcomes under saline and freshwater. The correlation between the simulated and observed yield were 0.92, 0.94, and 0.79 for water quality of 1.0, 2.3 and 3.6 dSm−1, respectively. Finally, the SALTMED model showed good simulations of soil water content, soil salinity, and final tomato yield.

The Fourth Industrial Revolution (4IR) has ushered in a new era of efficiency across various domains, including the management of Road Traffic Congestion (RTC) in metropolitan cities. Numerous studies have elucidated how smart urban traffic management systems (TMS), encompassing 4IR technologies like Artificial Intelligent (AI), Machine Learning (ML), Internet of Things, and even Software-Define Networks (SDN) or Blockchain can optimize traffic flow and mitigate congestion. Given these results, there is a pressing need to evaluate these approaches and innovations in terms of their performance and sustainability. This review aims to scrutinize current technologies predicated on intelligent traffic management. It will examine the methodologies employed, the benefits derived from these systems, and potential challenges that may arise. The study has identified and categorized research based on the TMS procedure from the sensing process, traffic data evaluation, and solution developments. By providing a comprehensive overview of state-of-the-art in intelligent traffic management, this review seeks to contribute to the ongoing discourse on leveraging 4IR technologies for sustainable urban development.

Intelligent reflecting surface (IRS)-assisted communications technology is currently considered a key enabler for various wireless applications. The maximum gain of IRS is achieved when the phases of the reflected signals are optimally selected to maximize signal-to-noise ratio (SNR). However, practical hurdles such as imperfect phase estimation and hardware limitations such as phase quantization can reduce the potential gain of the IRS deployment. Internet of Things applications are more vulnerable to such limitations due to restrictions on device size, energy, cost, and computational power. Therefore, this work evaluates the joint impact of quantization and imperfect phase estimation where the probability density function (PDF) of the estimated and quantized phase is derived. Then, using the sinusoidal addition theorem, the PDF of the received signal envelope is derived and used to derive exact analytic expressions for the symbol error rate and outage probability. The analytical and simulation results obtained show that the impact of the joint estimation and quantization imperfections depends on the SNR and number of IRS elements. In particular, it is shown that increasing the number of IRS elements can effectively mitigate the impact of phase estimation and quantization problems. Furthermore, the results show that the impact of phase quantization increases as the accuracy of phase estimation decreases.

Abstract Background The use of medical 3D printing (focusing on anatomical modeling) has continued to grow since the Radiological Society of North America’s (RSNA) 3D Printing Special Interest Group (3DPSIG) released its initial guideline and appropriateness rating document in 2018. The 3DPSIG formed a focused writing group to provide updated appropriateness ratings for 3D printing anatomical models across a variety of congenital heart disease. Evidence-based- (where available) and expert-consensus-driven appropriateness ratings are provided for twenty-eight congenital heart lesion categories. Methods A structured literature search was conducted to identify all relevant articles using 3D printing technology associated with pediatric congenital heart disease indications. Each study was vetted by the authors and strength of evidence was assessed according to published appropriateness ratings. Results Evidence-based recommendations for when 3D printing is appropriate are provided for pediatric congenital heart lesions. Recommendations are provided in accordance with strength of evidence of publications corresponding to each cardiac clinical scenario combined with expert opinion from members of the 3DPSIG. Conclusions This consensus appropriateness ratings document, created by the members of the RSNA 3DPSIG, provides a reference for clinical standards of 3D printing for pediatric congenital heart disease clinical scenarios.

The rapid growth of the Internet-of-things (IoT) in recent years has led to the development of new access technologies targeting low-power wide area networks (LPWANs). LPWAN is a promising solution for long-range and low-power IoT and machine-to-machine (M2M) communication applications. However, practical LPWAN deployments suffer from intense collisions due to uncoordinated transmissions, given the dense deployment of devices and wide coverage area. Therefore, this paper proposes a deep learning-based collision aware transmission priority scheduling technique (CA-PST) to mitigate high packet collision rate in ultra-dense wireless networks. The proposed CA-PST is applied to the low-range wide area network (LoRaWAN) platform where the LoRa gateway configures the nodes to a particular transmission protocol classes based on the predicted number of packet collisions. Aided by unsupervised learning clustering, the nodes located at higher transmission priority clusters will be allowed to communicate with the gateway using class C and therefore avoid packet collisions. Whereas nodes in lower transmission priority clusters communicate with the gateway using class A. Therefore, the CA-PST offers a fair trade-off between the packet delivery rate (PDR) and total energy consumption. Extensive simulation results show that using CA-PST can significantly improve the LPWAN reliability in terms of PDR in ultra-dense networks.

This article proposes a novel Direct Detection (DD) scheme for multiple-input multiple-output (MIMO) unmanned aerial vehicle (UAV) communications where tedious, slow, and computationally demanding channel state information (CSI) estimation and detection processes are not required. The proposed detection scheme is achieved by exploiting the frequency and/or temporal correlation among the transmitted signals to directly extract the information symbols from the received signals. The results obtained show that the proposed scheme offers a significant advantage of approximately 15 dB performance gain compared to conventional linear detection schemes, even when such schemes are using perfect CSI. Furthermore, the proposed detection algorithm can be efficiently implemented using the Viterbi Algorithm, or its variants, to achieve further complexity reduction.

non-orthogonal multiple access (NOMA) is a promising candidate to improve the spectral efficiency by multiplexing users in the power-domain. Most of the work in the literature considers the analysis of the single-rate (SR)-NOMA, which can only vary the power per user, limiting its flexibility and bit error rate (BER) performance. Therefore, this work considers the design and performance analysis of multirate (MR)-NOMA, which controls the symbol energy per user by varying the symbol rate and power simultaneously. Both joint-multiuser maximum likelihood sequence detector (JMLSD) based on the maximum liklihood criterion and a novel low-complexity optimal successive interference cancellation (SIC) receiver are designed. Furthermore, closed-form BER expressions are derived considering arbitrary symbol rates and modulation orders. The derived expressions are then used to optimize the power allocation at the base station (BS) to minimize the BER while strictly satisfying certain BER requirements. The presented results show that MR-NOMA offers more trade-off freedom between spectral efficiency and robustness to errors. As such, MR-NOMA can have up to two orders of magnitude improvement in BER performance in some scenarios. The derived expressions are validated via simulations.

Sketch-based image retrieval (SBIR) utilizes sketches to search for images containing similar objects or scenes. Due to the proliferation of touch-screen devices, sketching has become more accessible and therefore has received increasing attention. Deep learning has emerged as a potential tool for SBIR, allowing models to automatically extract image features and learn from large amounts of data. To the best of our knowledge, there is currently no systematic literature review (SLR) of SBIR with deep learning. Therefore, the aim of this review is to incorporate related works into a systematic study, highlighting the main contributions of individual researchers over the years, with a focus on past, present and future trends. To achieve the purpose of this study, 90 studies from 2016 to June 2023 in 4 databases were collected and analyzed using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) framework. The specific models, datasets, evaluation metrics, and applications of deep learning in SBIR are discussed in detail. This study found that Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN) are the most widely used deep learning methods for SBIR. A commonly used dataset is Sketchy, especially in the latest Zero-shot sketch-based image retrieval (ZS-SBIR) task. The results show that Mean Average Precision (mAP) is the most commonly used metric for quantitative evaluation of SBIR. Finally, we provide some future directions and guidance for researchers based on the results of this review.

Recent improvements formulated in the area of video captioning have brought rapid revolutions in its methods and the performance of its models. Machine learning and deep learning techniques are both employed in this regard. However, there is a lack of tracing the latest studies and their remarkable results. Although several studies have been proposed employing the ML and DL algorithms in different other areas, there is no systematic review utilizing the video captioning task. This study aims to examine, evaluate, and synthesize the primary studies into a thorough Systematic Literature Review (SLR) that provides a general overview of the methods used for video captioning. We performed the SLR to determine the research problems under which machine learning models were preferred over the deep learning models and vice versa. We collected a total of 1,656 studies retrieved from four electronic databases; Scopus, WoS, IEEE Xplore, and ACM, based on our search string from which 162 published studies passed the selection criteria related to one primary and two secondary research questions after a systematic process. Moreover, insufficient data collection and inefficient comparison of results are common issues identified during the review process. We conclude that the 2D/3D CNN for video feature extraction and LSTM for caption generation, METEOR and BLEU performance evaluation tools, and MSVD dataset are most frequently employed for video captioning. Our study is the pioneer in comparing the implementation of ML and DL algorithms employing the video captioning area. Thus, our study will accelerate the critical assessment of the state-of-the-art in other research fields of video analysis and human-computer interaction.

Abstract Background Medical three dimensional (3D) printing is performed for neurosurgical and otolaryngologic conditions, but without evidence-based guidance on clinical appropriateness. A writing group composed of the Radiological Society of North America (RSNA) Special Interest Group on 3D Printing (SIG) provides appropriateness recommendations for neurologic 3D printing conditions. Methods A structured literature search was conducted to identify all relevant articles using 3D printing technology associated with neurologic and otolaryngologic conditions. Each study was vetted by the authors and strength of evidence was assessed according to published guidelines. Results Evidence-based recommendations for when 3D printing is appropriate are provided for diseases of the calvaria and skull base, brain tumors and cerebrovascular disease. Recommendations are provided in accordance with strength of evidence of publications corresponding to each neurologic condition combined with expert opinion from members of the 3D printing SIG. Conclusions This consensus guidance document, created by the members of the 3D printing SIG, provides a reference for clinical standards of 3D printing for neurologic conditions.

Abstract Background Medical three-dimensional (3D) printing has demonstrated utility and value in anatomic models for vascular conditions. A writing group composed of the Radiological Society of North America (RSNA) Special Interest Group on 3D Printing (3DPSIG) provides appropriateness recommendations for vascular 3D printing indications. Methods A structured literature search was conducted to identify all relevant articles using 3D printing technology associated with vascular indications. Each study was vetted by the authors and strength of evidence was assessed according to published appropriateness ratings. Results Evidence-based recommendations for when 3D printing is appropriate are provided for the following areas: aneurysm, dissection, extremity vascular disease, other arterial diseases, acute venous thromboembolic disease, venous disorders, lymphedema, congenital vascular malformations, vascular trauma, vascular tumors, visceral vasculature for surgical planning, dialysis access, vascular research/development and modeling, and other vasculopathy. Recommendations are provided in accordance with strength of evidence of publications corresponding to each vascular condition combined with expert opinion from members of the 3DPSIG. Conclusion This consensus appropriateness ratings document, created by the members of the 3DPSIG, provides an updated reference for clinical standards of 3D printing for the care of patients with vascular conditions.

Abstract This research paper investigates the optimization of radiation performance of a plasma-based bioconvective nanofluid integrated Magneto-hydrodynamic (MHD) micropump for radiative oncology. It addresses a literature gap by analysing the radiative impact of blood-based hybrid nanofluids in MHD micropumps. Three blood-based bio-convective radiating hybrid nanofluids—blood—Pt, blood—Au and blood—MWCNT are studied to understand their radiation behaviour in MHD pump while being employed as transportation medium. The investigation employs two non-dimensional parameters, namely Rd (Radiation number) and Ha (Hartmann number), to examine the fluid dynamics, magnetic characteristics, and electrical properties of the MHD micropump. The temperature gradient, velocity distribution, and pressure drop along the flow channel are examined within the specified range of Rd and Ha. Magnetic flux density (MFD) and electric flux intensity (EFI) are evaluated to understand nanoparticle behaviour during drug delivery and blood transportation. Findings highlight that MWCNT and Pt are the most efficient bioconvective nanoparticles for plasma transportation under high radiative conditions. MWCNT-based blood flow exhibits desirable characteristics, including sufficient intake pressure of 4.5 kPa and minimal relative pressure drop of 34%. Coherence between radiation flux and electromagnetic flux reduces pumping power and ensures uniform heat dissipation for improved drug delivery. Au nanoparticles provide moderate magnetic flux density with least fluctuation within the range of Ha and Rd number (2.57 T to 4.39 T), even in highly radiative environments (such as—Rd = 4, Rd = 5), making them suitable for applications like embedded chemotherapy or cell treatment. Au nanoparticles maintain moderate electrical flux intensity with a minimal drop of 16nA, particularly at higher radiative environments influenced by the Radiation number (Rd = 4 to Rd = 5) while Ha values from Ha = 2 to Ha = 4. Conclusively, it has been identified that MWCNT and Au are superior nanofluids for advanced radiative oncological treatments. These nanofluids have the potential to enhance plasma transportation, thermal regulation, and aetilogical disease management. The present study provides significant findings on enhancing the radiation performance in MHD micropumps through utilization of blood-based hybrid nanofluids, thereby offering potential advantages to the domain of biomedical engineering.

Heat exchangers are critical components in a wide range of industries, and optimizing their thermal performance remains an important research area. To augment the thermal efficiency in a heat exchanger, several investigations have been done to increase the wetted surface area using a rib profile. Still, there exists a lack of research attempts to change the geometrical shape, such as multiple-head ribbed geometry with coil revolution in a straight heat exchanger. Therefore, the present study takes a numerical approach to examine the effect of multiple ribbed geometries, considering the variable coil revolution of the rib profile in a straight-type heat exchanger. The study was initially validated from two experimental correlations and previous numerical work. Three rib surfaces (2 ribs, 3 ribs and 4 ribs) and three different coil revolutions (10 revolutions, 20 revolutions and 30 revolutions) were taken into consideration to make nine configurations. Air was used as fluid under heating conditions. The results have been represented in terms of velocity, temperature profiles, local Nusselt number (Nu), pressure drop, friction factors and entropy generations for all the configurations. A significant enhancement in heat transfer was found when the rib head number is lesser, whereas the coil revolution number is higher. Consequently, the highest Nusselt number (Nu) was observed in the configuration of 2 ribbed 30 revolutions, and the lowest was found in 4 ribbed 10 revolutions. Furthermore, the friction factor rises because of higher coil revolution for a specific rib head number. It is found that in four rib head geometry, the friction factor rises 2 times when the revolution number is 30 than the case of revolution number 10. It is also observed that the 4 rib head 10 revolution configurations provides the lowest entropy generations among the configurations. Thus, the study suggests choosing the right configuration to design a straight heat exchanger with ribbed head profile to get the highest thermal performance. Following this study, these design modifications can be applied to other types of heat exchangers as well.

Enhancing saline water productivity in arid regions is essential for sustainable agriculture. Adding biochar can improve the quantity and quality of tomato yield under higher levels of salinity and lower levels of irrigation. The experiment aimed to evaluate the effects of biochar on enhancing tomato fruit quality and yield under salinity and drought stress. The experiment combines two treatments for irrigation water quality (0.9 and 2.3 dS m−1), four irrigation levels (40, 60, 80, and 100%) of crop evapotranspiration (ETc), and the addition of 5% of biochar to treated soil (BC5%) and untreated soil (BC0%). The results showed that the decrease in the water quality and irrigation levels negatively impacted the yield and properties of tomato fruit, while 5% of biochar application positively improved the yield. Adding biochar decreased the tomato yield by 29.33% and 42.51% under lower-saline-irrigation water than the control, negatively affecting the fruit’s physical parameters and mineral content. In contrast, adding biochar and irrigating with saline water at 60% of ETc improved the firmness and quality characteristics of the fruit by 56.60%, 67.19, 99.75, and 73.57% for vitamin C (VC), total titratable acidity (TA), total soluble solids (TSS), and total sugars (TS), respectively, compared to the control, and also reduced the sodium content of the fruits under all irrigation levels compared to untreated plants by biochar. Generally, biochar with saline water under deficit irrigation with 80 and 60% of ETc could be an excellent strategy to enhance the qualitative characteristics of tomato fruits and save approximately 20–40% of the applied water.

In this numerical study, different geometric parameters have been optimized for improved performance using a clean energy geothermal Ground to Air Tunnel Heat Exchanger (GATHE). A commercial computational fluid dynamic code ANSYS Fluent was used, and the code has been validated with the available experimental and analytical results. Three soil samples were used to analyze the thermal properties, each with a thermal conductivity of 0.65, 1.25 and 3.50 Wm−1K−1. The critical soil thickness was found to be 10 times the pipe diameter and the lowest outlet temperature of 300.1 K was achieved for a 60 m pipe with a soil thermal conductivity of 3.50 Wm−1K−1. To further enhance the performance of the system, multiple fin arrays were introduced into the ductwork at 0.5 m, 1.0 m and 2.0 m pitches. For a 10 m long pipe with fins installed every 0.5 m, an outlet temperature of 306.7 K was observed. With 4 fins for temperature variation, performance increased by 19.4 % compared to the case without fins. When the fin spacing is 1 m, the temperature drop increases by 5.6 %, and when the fin spacing is 2 m, the temperature drop increases to 7 %, with an outlet temperature of 306.1 K. Finally, a novel set of multiple blocks in diverging and converging patterns was introduced to restrict airflow and further improve heat dissipation. Compared to a single-fin block, the outlet temperature of the 4-fin block was reduced by 14.52 % due to the increased contact area, thus improving the thermal performance of the heat exchanger system.

This study introduces a numerical model to assess the thermal and frictional properties of LBE crossflow over tube bundles on the shell side of a Helical-coiled Once-Through Steam Generator (H-OTSG) considering different arrangements, including inline, obliquely staggered, triangular, and rotated square configurations. The k-ω SST turbulence model combined with Kays turbulent Prandtl number model is utilized to develop the numerical model. The simulation results have been validated against experimental data and empirical correlations. The layouts of the tube bundles significantly influence the generation of transverse flow, vortical structures and rate of heat transfer in the flow domain. The maximum Nusselt number of 8.2 is observed for the triangular layout as significant crossflow is induced, which is a 10% increase compared to the inline arrangement where the Nusselt number is 7.45. Triangle layouts also exhibit a higher friction factor of 0.45, marking a 20% increase compared to the 0.45 friction factor observed in the inline arrangement. Increasing the oblique angle usually reduces the heat transfer rate and friction factor. Yet, at a 45-degree layout, higher turbulence intensity results in a Nu of 7.05, surpassing 6.93 observed at 30° but falling short of 7.15 at 15°. Nu decreases for rotated square arrangements, but a friction factor greater than the inline arrangement is observed at higher diagonal pitches. The Thermal Enhancement Factor (THEF) is employed to assess the thermal effectiveness of the different tube bundle layouts, and the maximum THEF of 1.1 is observed for the layout with an oblique angle of 45°. Favorable THEF values of 1.06 and 1.05 are recorded for the triangular layout and 30-degree oblique angle, respectively. This numerical study will assist the design and development of LBE H-OTSG.

Both salinity and drought stand as significant abiotic stresses that adversely affect crop production worldwide. While medicinal plants offer substantial economic and social benefits, their productivity faces challenges globally due to abiotic stresses. The distinctive quality of medicinal plants stems from their active ingredients. However, these abiotic stresses can compromise the potency of active and herbal ingredients in medicinal plants. Both drought and salinity result in osmotic stress and Na+ toxicity, leading to a myriad of morphological, physiological, biochemical, and metabolic changes in plants. Such changes encompass alterations in water relationships, water use efficiency (WUE), relative growth rate (RGR), nutrient balance, transpiration rate, stomatal conductivity, CO2 uptake and diffusion, photosynthesis potential, and overall productivity. Being a pivotal physiological process crucial for plant survival, photosynthesis becomes one of the earliest processes to be affected under abiotic stress. Specifically, drought and salinity diminish the photosynthesis rate by disrupting chlorophyll metabolism, inducing oxidative stress, and prompting plastid degradation. This decline can be attributed to the adverse effects of ion absorption disturbances on chloroplast development and the protein translation machinery within plastids. Interestingly, plant responses to both salinity and drought often mirror each other. To combat these challenges, medicinal plants employ a range of protective mechanisms. These include preserving cell wall architecture, accumulating osmolytes, generating antioxidants, initiating stomatal closure, and boosting secondary metabolite levels. This review delves into the growth, development, and photosynthesis of various medicinal plants under the duress of salinity and drought.

This paper presents the design and construction of a hydroponics monitoring system that can collect parameters of hydroponic systems, such as temperature, water limit, pH level, and nutrient levels. The monitoring system was developed using an ESP32 microcontroller and several sensors, including total dissolved solids (TDS), pH, water level, and temperature sensors. The ESP32 microcontroller gathers and processes data from the sensors to automatically activate the water or salt pump and drain the necessary materials into the hydroponic system's plant basin. The user can then view the hydroponic parameters through the Blynk application on a smartphone. The user can also activate the pumps for water, nutrients, or salt using the application's interface on a smartphone, or the ESP32 microcontroller can activate them automatically if the parameter values deviate from the required values. The monitoring hydroponics system and IoT interface were successfully built and implemented. The experiments were compiled, and the data gathered and discussed. • An ESP32 microcontroller with TDS, pH, water level, and temperature sensors was used to build the hydroponic monitoring system. • The ESP32 automatically collects and evaluates sensor data in order to drain water nutrients, or salt into the plant basin of the hydroponic system as necessary. • The user can also check the parameters of the hydroponic system and, if necessary, run the pumps for water, fertilizers, or salt using his smartphone through the Blynk IoT app.

Despite the recent increasing interest in digital pathology, efficient analysis of Whole Slide Images (WSIs) faces many challenges that hinder performance, such as their massive size, high heterogeneity, and imbalance distribution. This paper proposes a Joint Multi-decoder Dual-attention U-Net (JMDU-Net) framework for tumor segmentation in WSIs to address these issues. JMDU-Net is an end-to-end framework that consists of a single encoder with a main and four supportive decoder branches. JMDU-Net incorporates the design of multiple novel blocks that adaptively acquire diversified contextual channel-wise features and progressively utilize knowledge from multi-perspective hierarchical feature fusion. Moreover, a multi-magnification majority-voting ensemble is proposed to jointly consider the multi-scale representations of different magnifications in the WSIs. The experimental results and statistical tests performed on the public PAIP 2019 dataset demonstrate the efficiency of JMDU-Net by obtaining an average of 88.0% dice score with 77.4% clipped Jaccard index value for 5-fold cross-validation on training data, while the proposed ensemble succeeded in obtaining a 86.4% dice score with 79.8% clipped Jaccard index value for the official validation set. Furthermore, the generalizability is assessed against DigestPath 2019 and PAIP 2023 datasets. JMDU-Net achieved average dice of 84.9% and 75.3% and Jaccard values of 74.4% and 63.0% on these datasets, respectively. The main aim of JMDU-Net is to provide a complementary assessment to medical professionals to enhance the level of care introduced to the patients by harnessing the powerfulness of deep learning in the cancer diagnosis task. The code will be available publicly at https://github.com/Heba-AbdeNabi/JMDU-Net.

This study aimed to examine numerically the effects of a dimpled surface over a mini-channel heat exchanger on the flow characteristics and heat transfer across a serpentine channel with a uniform rectangular cross-section. The dimples were arranged in parallel with a spanwise (y/d) distance of 3.125 and streamwise (x/d) distance of 11.25 along just one side of the serpentine channel's surface. Turbulent flow regime with Reynolds number ranging from 5 × 103 to 20 × 103 in the channel with the surface modification was studied using water and various volume concentrations (φ = 0.1%, 0.33%, 0.75%, 1%) of Al2O3-Cu/water hybrid nanofluid as the coolant to achieve a three-step passive heat transfer enhancement. Applying the Finite Volume Method (FVM), RNG k-e turbulence model, and a constant heat flux of 50 kW/m2, simulations were run assuming the mixture of Al2O3-Cu nanoparticles homogenous using ANSYS 2020 R1. The second-order upwind approach is used for approximation of solution and discretization with SIMPLE pressure–velocity coupling. Taking heat transfer increment and pressure drop penalty into consideration, the dimpled serpentine channel provides a 1.47-times improvement in thermal efficiency using water as the coolant, and the dimpled channel with 1% vol. Al2O3-Cu/water nanofluid enhanced thermal efficiency by a remarkable maximum of 2.67-times at Re 5 × 103. The study also indicates that thermal efficiency increased with an increasing volume concentration of the nanofluid and increment in thermal efficiency gradually decreased as the Re increased.

Abstract The use of medical 3D printing has expanded dramatically for breast diseases. A writing group composed of the Radiological Society of North America (RSNA) Special Interest Group on 3D Printing (SIG) provides updated appropriateness criteria for breast 3D printing in various clinical scenarios. Evidence-based appropriateness criteria are provided for the following clinical scenarios: benign breast lesions and high-risk breast lesions, breast cancer, breast reconstruction, and breast radiation (treatment planning and radiation delivery).

Physical layer security (PLS) can be adopted for efficient key generation and sharing in secured wireless systems. The inherent random nature of the wireless channel and the associated channel reciprocity (CR) are the main pillars for realizing PLS techniques. However, for applications that involve air-to-air (A2A) transmission, such as unmanned aerial vehicle (UAV) applications, the channel does not generally have sufficient randomness to enable reliable key generation. Therefore, this work proposes a novel system design to mitigate the channel randomness constraint and enable a high-rate secret key generation process. The proposed system integrates physically unclonable functions (PUFs) and CR to generate and exchange secret keys between two nodes securely. Moreover, an adaptive and controllable artificial fading (AF) level with interleaving is used to mitigate the impact of low randomness variations in the wireless channel. Moreover, we propose a novel bit extraction scheme to reduce the number of overhead bits required to share the intermediate keys. The obtained Monte Carlo simulation results show that the proposed system can operate efficiently even when the channel is nearly flat or time-invariant. Consequently, the time required for generating and sharing a key is significantly shorter than conventional techniques. Furthermore, the results show that a key agreement can be reached at the first trial for moderate and high signal-to-noise ratios (SNRs) substantially faster than other PLS techniques. Adopting the AF into static channels managed to reduce the mismatch ratio between the generated secret sequences and degrade the eavesdropper’s capability to predict the secret keys.

The usage of distributed Peer-to-Peer (P2P) networks has been growing steadily for a reasonable period. Various applications rely on the infrastructure of P2P networks, where nodes communicate to accomplish a task without the need for a central authority. One of the significant challenges in P2P networks is the ability of the network nodes to reach a consensus on a shared item; the challenge increases as time passes. Thus, this work proposes a new effective method for tweaking the Deep Reinforcement Learning (DRL) algorithm to train Deep Q Network (DQN) learning agents to reach a consensus among the P2P nodes. We propose various hierarchies of deep agents to address this crucial challenge in P2P networks. DRL is utilized to build and train agents; more precisely, DQN learning agents are constructed and trained. Two scenarios are proposed and evaluated. In the first scenario, one DQN agent is trained to find the consensus between the network nodes. In the second scenario, three hierarchies with different numbers of layers of agents are proposed and evaluated. In both scenarios, the P2P network used is a blockchain network. The best result was obtained using the third hierarchy of the second scenario; the overall accuracy of the model is 87.8%.

Activation functions are essential components in any neural network model; they play a crucial role in determining the network’s expressive power through their introduced non-linearity. Rectified Linear Unit (ReLU) has been the famous and default choice for most deep neural network models because of its simplicity and ability to tackle the vanishing gradient problem that faces backpropagation optimization. However, ReLU introduces other challenges that hinder its performance; bias shift and dying neurons in the negative region. To address these problems, this paper presents a novel composite monotonic, zero-centered, semi-saturated activation function called Hyperbolic cosine Linearized SquasHing function (HcLSH) with partial gradient-based sparsity HcLSH owns many desirable properties, such as considering the contribution of the negative values of neurons while having a smooth output landscape to enhance the gradient flow during training. Furthermore, the regularization effect resulting from the self-gating property of the positive region of HcLSH reduces the risk of model overfitting and ensures learning more robust expressive representations. An extensive set of experiments and comparisons is conducted that includes four popular image classification datasets, seven deep network architectures, and ten state-of-the-art activation functions. HcLSH exhibited the Top-1 and Top-3 testing accuracy results in 20 and 25 out of 28 conducted experiments, respectively, suppressing the widely used ReLU that achieved 2 and 5, and the reputable Mish that achieved 0 and 5 Top-1 and Top-3 testing accuracy results, respectively. HcLSH attained improvements over ReLU, ranging from 0.2% to 96.4% in different models and datasets. Statistical results demonstrate the significance of the enhanced performance achieved by our proposed HcLSH activation function compared to the competitive activation functions in various datasets and models regarding the testing loss Furthermore, the ablation study further verifies the proposed activation function’s robustness, stability, and adaptability for the different model parameter.

Non-orthogonal multiple access (NOMA) continues to receive enormous attention as a potential technique for improving the spectral efficiency (SE) of wireless networks. Although for several years most research efforts on the performance of NOMA systems focused on the ergodic sum-rate and outage probability, recent works have shifted towards error rate analysis of various NOMA configurations and designs. While the influx of publications on this topic is rich in lessons and innovations, the sheer volume of it makes it easy to get caught up in the details, so much so that one often loses sight of the overall picture. This paper serves as a survey on NOMA error rate analysis, painted in the large with bold and immediately recognizable strokes of insights to facilitate for the reader to understand and follow the up-to-date progress in this area. In addition to summarizing the principles of NOMA error rate analysis, this work aims to minimize redundancy and overlaps, identify research gaps, and outline future research directions.

Predicting the data transfer throughput of cloud networks plays an important role in several resource optimization applications, such as auto-scaling, replica selection, and load balancing. However, constant short-term variations in cloud networks make the prediction of end-to-end data transfer throughput a very challenging task. The parameters that affect the throughput can be categorized into three different areas: end-system characteristics (e.g., disk I/O bandwidth, CPU utilization), network characteristics (e.g., network bandwidth, latency, background traffic, bandwidth shaping mechanisms), and dataset characteristics (e.g., average file size, dataset size). Although there are promising results in the literature using neural networks, the datasets are collected from network layer devices and memory-to-memory data transfers where end-system and dataset characteristics are not considered as part of the problem. Also, very few studies use multivariate time series data collected from cloud networks, and the variables differ from study to study. In this project, we collected multivariate time series data from Amazon Web Services (AWS) by conducting intra- and inter-region transfers between storage systems and compute resources using monitoring services. This dataset is unique in the sense that end-system metrics in addition to network throughput are collected from both source and destination systems. Different average file size, instance type, and regionality parameters provide various settings, making the dataset applicable to various types of prediction models. Our multivariate neural network models predict the one-step-ahead network throughput with ~3.7% and disk throughput with ~6.1% error rate, outperforming multivariate models with least-correlated variables and univariate models empowered by transfer learning.

Data centers serve as the backbone for cloud computing, enterprise services, and infrastructure-based offerings. One area of ongoing research in data center networking focuses on innovating new topologies for large-scale node connectivity. These topologies must incorporate fault-tolerant and efficient routing algorithms. Consequently, the data center network topology must dynamically adapt to ever-changing application requirements. While traditional topology designs often emphasize scalability, they are typically limited by the necessity for dedicated switches to manage server connections. The development of software technologies that distinguish server and switch roles offers a unique opportunity to reconsider design priorities, paving the way for a more balanced assessment of scalability, energy efficiency, and infrastructure costs. Moreover, certain network topologies fail to be cost-effective due to their structural intricacies, often requiring far more node connections than those that are practically necessary. To address these challenges, we introduce VacoNet: a new flexible data center network topology that organizes nodes into structurally similar clusters, interconnected by a novel physical structure algorithm. Boasting high bisection bandwidth, VacoNet delivers robust network capacity, even when encountering bottlenecks. Furthermore, to connect a given set of nodes, VacoNet uses a minimal number of cables and switches, thereby drastically reducing both infrastructure costs and energy consumption. Simulation results show that VacoNet can reduce error rates by 20% and slash infrastructure costs by 70% compared to existing solutions. Additionally, it performs tasks 30% faster, underscoring its superior performance.

The digital market trend is rapidly expanding due to key characteristics like decentralization, accessibility, and market diversity enabled by blockchain technology. This study proposes a Predictive Analytics System to provide simplified reporting for the three most popular cryptocurrencies with varying digits, namely ADA Cardano, Ethereum, and Binance coin, for ten days to contribute to this emerging technology. Thus, this proposed system employs a data science-based framework and six highly advanced data-driven Machine learning and Deep learning algorithms: Support Vector Regressor, Auto-Regressive Integrated Moving Average (ARIMA), Facebook Prophet, Unidirectional LSTM, Bidirectional LSTM, Stacked LSTM. Moreover, the research experiments are repeated several times to achieve the best results by employing hyperparameter tuning of each algorithm. This involves selecting an appropriate kernel and suitable data normalization technique for SVR, determining ARIMA’s (p, d, q) values, and optimizing the loss function values, number of neurons, hidden layers, and epochs in LSTM models. For the model validation, we utilize widely used evaluation techniques: Mean Absolute Error, Root Mean Squared Error, Mean Absolute Percentage Error, and R-squared. Results demonstrate that ARIMA outperforms the other models in all cases, accurately projecting the price variability within the actual price range. Conversely, Facebook Prophet exhibits good performance to some extent. The paper suggests that the ARIMA technique offers practical implications for market analysts, enabling them to make well-informed decisions based on accurate price projections.

Consumers require high-speed data transmission for different activities, such as smartphone usage, live broadcasting of news, and video conferencing. Therefore, a reliable communication network is needed to provide this kind of service to users. Fiber to the home (FTTH) is an optical fiber architecture that uses fiber cables in the access network for direct and final connection to homes or offices of customers. Networks based on FTTH can offer high performance, speed, and quality. An optical fiber communication system based on FTTH device ingress network using gigabit passive optical networks (GPONs) with fiber Bragg grating (FBG) and optical amplifier is designed and analyzed in this study. The developed design based on the FTTH device and FBG shows a low bit error rate (BER) for downstream and upstream configurations with an optical fiber length of 20 km. Downstream and upstream configurations achieve a Q-factor of 89.5 and 181.3, respectively. Achievable sensitivity of the developed system is −28 dBm, while the received signal based on OptiSystem is −25.59 dBm. FTTH with FBG will play a major role in the future and provide effective solutions for a wide variety of applications in network communication systems and data transmission rates.

Flat Plate Solar Collectors (FPSCs) have been widely employed in the production of renewable energy for a significant period. To enhance their thermal performance, the utilization of nanofluids as heat transfer fluids has emerged as a popular approach. However, many research works were only carried out with conventional metal-based nanofluids. This numerical study investigates the effects of incorporating two carbon nanotubes, namely single-walled carbon nanotube (SWCNT)/water and multi-walled carbon nanotube (MWCNT)/water, on the fluidic and thermal performance of FPSC. The Nusselt (Nu) and Stanton (St) numbers, as well as the frictional characteristics, are measured and compared to those of metal-based nanofluid of alumina (Al2O3)/water at volume concentrations up to 1%. It is found that Nu increases with a higher Reynolds number (Re) while deteriorating at a higher volume concentration. The lowest Nu was recorded for SWCNT/water in the current investigation. The Stanton number increases with a higher volume concentration and decreases with an increase of Re. Notably, the maximum value of 0.001801 is obtained for the SWCNT nanofluid. The outlet temperature decreases maximum for SWCNT/water with the increase of Re and volume concentration while the friction factor decreases with increasing Re and is independent of the type of nanofluids. Maximum thermo-hydraulic performance parameter of 1.48, 1.32 and 1.29 is achieved for SWCNT, Al2O3 and MWCNT, respectively.

The tomato is a vital component of agriculture and is the second-most important vegetable globally. Maintaining a high tomato production requires both water quality and quantity. Water-scarce regions like Saudi Arabia still lack an understanding of the impact of deficit irrigation and the use of a blend of saline and freshwater, especially their nuanced impact across growth stages. The purpose of this study was to evaluate the effects of six different irrigation amounts: full irrigation with 100% ETc (FI), regulated deficit irrigation with 60% ETc (DI), and deficit irrigation with 60% ETc, except for the initial (DI-int), development (DI-dev), mid-season (DI-mid), and late-season (DI-lat) stages. This was performed with three different water qualities: fresh (FW), saline (SW), and fresh-saline blend (1:1) (MW) water. FW and MW enhanced the growth, physiology, morphology, yield, and quality, while SW had the lowest values. DI reduced these parameters and lowered yields by 13.7%, significantly improving water use efficiency (WUE) by 44% and fruit quality. DI-mid or DI-lat slightly improved yields while remarkably decreasing WUE and fruit quality. DI outperforms deficit irrigation in all growth stages except one, and countries with limited freshwater resources can benefit from a mix of fresh and saline water with a 60% ETc deficit irrigation, resulting in greater water savings.

The main objective of this study is to assess the use of carbon capturing and sequestration (CCS) technology in the existing plant to reduce the emissions leading to retrofitting. A typical 125 MW tangentially coal-fired Barapukuria Thermal Power Plant (BTPP), Bangladesh has been scrutitinized considering air-firing (existing) and five different oxy-firing (OF) cases have been numerically investigated. For predicting the particle flow, radiative heat transfer and turbulence effects, discrete droplet method (DDM), discrete transfer radiation method (DTRM) and k–ϵ model is considered respectively. For homogeneous reaction for the devolatilization and heterogeneous reactions for the char oxidation process, a two steps reaction mechanism is considered. Model has been validated by comparing the predicted flue gas temperature against the plant data at different positions of the furnace and reasonable agreement have been observed. This study assessed the flow characteristics, thermal behavior and species generations of the selected plant under 80% fuel loading. It is predicted that with the increase of cases from air-firing, level of oxiding elements are increasing in the main combustion zone due to enriched O2 level leading to complete combustion of the particles. It is also found that for OF 31 case, level of CO2 is close to the air-firing case. it has been found that wall temperature is comparatively higher for 100% loading and comparatively lower for 50% loading for almost all similar type of air or oxy-firing conditions. For 100% load, the wall temperature has been reached around 2900 K for OF31 case. It is predicted that air operation equivalent to oxy-firing have been found at OF 30+ % (radiative Heat transfer) respectively.

تضمن البحث دراسة الموارد المائية السطحية المتوفرة في حوض وادي مدود من حيث خصائصها الطبيعية وحساب كمية العجز المائي وتقدير كميتها وفقآ للنماذج الرياضية الهيدرولوجية المعتمدة في تقدير حجم الجريان السطحي . توصل البحث الى ان المياة السطحية تعاني من عجز مائي كبير خلال الفصل الحار بسبب ظروف التبخر العالية ، وتوصل الى امكانية انشاء سد عند الجزء الاوسط من مجرى الوادي حيث التقاء عدد من المراتب النهرية.

Abstract Background To our knowledge, the role of exogenous fluoride (F–) on aluminum (Al)-stress mitigation in plants has not been investigated yet. In this experiment, barley (Hordeum vulgaris) seedlings were exposed to excessive Al3+ concentrations (aluminum chloride, 0.5, 1.0, 2.0, 3.0, and 4.0 mM) with and without fluoride (0.025% sodium fluoride) to explore the possible roles of fluoride on the alleviation of Al-toxicity. Results Overall, Al-stress caused inhibition of growth and the production of photosynthetic pigments. Principal component analysis showed that the growth inhibitory effects were driven by increased oxidative stress and the interruption of water balance in barley under Al-stress. Fluoride priming, on the other hand, enhanced growth traits, chlorophyll a and b content, as well as invigorated the protection against oxidative damage by enhancing overall antioxidant capacity. Fluoride also improved osmotic balance by protecting the plasma membrane. Fluoride reduced endogenous Al3+ content, restored Al-induced inhibition of glutathione-S-transferase, and increased the contents of phytochelatins and metallothioneins, suggesting that fluoride reduced Al3+ uptake and improved chelation of Al3+. Conclusions Aluminum chloride-induced harmful effects are abridged by sodium fluoride on barely via enhancing antioxidative responses, the chelation mechanism causing reduction of Al uptake and accumulation of barely tissues. Advanced investigations are necessary to uncover the putative mechanisms underpinning fluoride-induced Al-stress tolerance in barley and other economically significant crops, where our results might serve as a solid reference.

Saudi Arabia faces water scarcity and inadequate sustainable sources, particularly in agriculture, necessitating efficient irrigation water management to improve productivity amidst rising demand. The study investigated the impact of irrigation levels and water salinity on tomato plants in greenhouses, covering four irrigation levels (100%, 80%, 60%, and 40% of ETc) and three water sources (FW (0.9 dS·m−1), SW (3.6 dS·m−1) and MW (2.25 dS·m−1)). Salinity impacts crop yield, physiological responses, and fruit quality. The photosynthesis, stomatal conductance, transpiration, and chlorophyll content decrease with MW and SW, negatively affecting morphological characteristics. For MW, it was recommended to apply 60% deficit irrigation with a yield of 98 kg·ha−1, and water productivity (WP) improved to 21.93 kg·m−3 compared to 13.65 kg·m−3 at full irrigation (FI). In SW, 80% irrigation was suggested, as there was no significant difference in yield compared to FI. For FW, 60% deficit irrigation produced the best water conservation (104.58 kg·ha−1 yield and 23.19 kg·m−3 WP), while FI produced the highest yield per unit area (123.48 kg·ha−1 yield and 16.51 kg·m−3 WP). Nonetheless, greater water and salinity stress was associated with increased fruit quality measures such as total acidity, vitamin C, and soluble solids. The results show that implementing deficit irrigation with salinity strategies in greenhouse tomatoes could improve crop adaptability, yield, and water productivity in the face of water scarcity and salinity variability.

In this study, canola (Brassica napus L.) seedlings were treated with individual and combined salinity and lithium (Li) stress, with and without acetic acid (AA) or nitric acid (NO), to investigate their possible roles against these stresses. Salinity intensified Li-induced damage, and the principal component analysis revealed that this was primarily driven by increased oxidative stress, deregulation of sodium and potassium accumulation, and an imbalance in tissue water content. However, pretreatment with AA and NO prompted growth, re-established sodium and potassium homeostasis, and enhanced the defense system against oxidative and nitrosative damage by triggering the antioxidant capacity. Combined stress negatively impacted phenylalanine ammonia lyase activity, affecting flavonoids, carotenoids, and anthocyanin levels, which were then restored in canola plants primed with AA and NO. Additionally, AA and NO helped to maintain osmotic balance by increasing trehalose and proline levels and upregulating signaling molecules such as hydrogen sulfide, γ-aminobutyric acid, and salicylic acid. Both AA and NO improved Li detoxification by increasing phytochelatins and metallothioneins, and reducing glutathione contents. Comparatively, AA exerted more effective protection against the detrimental effects of combined stress than NO. Our findings offer novel perspectives on the impacts of combining salt and Li stress.

Graph embedding is an important representational technique that aims to maintain the structure of a graph while learning low-dimensional representations of its vertices. Semantic relationships between vertices contain essential information regarding the meaning of the represented graph. However, most graph embedding methods do not consider the semantic relationships during the learning process. In this paper, we propose a novel semantic graph embedding approach, called SemanticGraph2Vec. SemanticGraph2Vec learns mappings of vertices into low-dimensional feature spaces that consider the most important semantic relationships between graph vertices. The proposed approach extends and enhances prior work based on a set of random walks of graph vertices by using semantic walks instead of random walks which provides more useful embeddings for text graphs. A set of experiments are conducted to evaluate the performance of SemanticGraph2Vec. SemanticGraph2Vec is employed on a part-of-speech tagging task. Experimental results demonstrate that SemanticGraph2Vec outperforms two state-of-the-art baselines methods in terms of precision and F1 score.

Salinity and waterlogging stresses have catastrophic effects on plants. Physiological and molecular mechanisms of plant responses to individual salinity and waterlogging stresses have been widely investigated. Several research studies have been conducted to understand the combined effects of salinity and waterlogging on plant growth, photosynthesis, and mineral homeostasis, but no systematic review has been performed yet to comprehend the cumulative effects of salinity and waterlogging on plants. This work employed a systematic approach to examine how plants respond to combined salinity and waterlogging stress compared to individual stresses, and to identify potential research gaps in this field. Our analysis indicates that combined salinity and waterlogging stress inhibits growth processes more strongly than salinity or waterlogging stress alone. In addition, the combined effect of salinity and waterlogging on photosynthesis and ionic homeostasis is greater than the sum of the individual effect of each. Our analysis further implies that tolerance to combined salinity and waterlogging stress is mostly determined by salinity tolerance. An examination of subgroups found that hydrophytes are more vulnerable to combined salinity and waterlogging than halophytes. We identified several research gaps that should be addressed in future research to enhance plant salinity and waterlogging stress tolerance.

Fifth generation (5G)-industrial Internet of things (IIoT) is the integration of IIoT and a private 5G network. The IIoT is a concept that involves incorporating smart objects, gadgets, and solutions into cutting-edge industrial operations to increase reliability, efficiency, and over-production costs. Furthermore, the integration of IIoT and 5G/beyond 5G (B5G) provides the potential for ubiquitous and instantaneous connectivity. The 5G architecture can handle the IIoT’s stringent ultra-low latency, realtime processing, high data rate, nearby storage, and reliability requirements. A new era of economic growth is predicted for IIoT-assisted 5G/B5G wireless networks. It should be noted that the majority of the work in IIoT is focused on the architecture, with reliability and throughput being largely ignored. This paper provides a comprehensive review of B5G assisted IIoT wireless networks, with a focus on enhanced mobile broadband (eMBB) and ultra-reliable low latency communication (URLLC) services. Furthermore, it provides insights into various applications and key enabling technologies from the perspective of URLLC, eMBB, and their tradeoff.

In reconfigurable intelligent surface (RIS)-assisted communications, the signal-to-noise ratio (SNR) is improved by optimizing the reflecting elements’ phases to make the reflected signals add coherently at the receiver. Nevertheless, the RIS cannot control the phase of the direct link if it exists. Although the RIS phase can still be controlled such that the direct and reflected signals add coherently, the SNR gain might be hindered. Therefore, this paper considers the performance analysis of such scenarios where a novel analytical framework is developed to evaluate the SNR, outage probability, and bit error rate (BER). To capture a broad range of fading conditions, the channels are modeled as independent but not identically distributed generalized $\kappa $ - $\mu $ shadowed fading channels. The Laplace transform is used to derive an accurate approximation of the probability density function (PDF) and cumulative distribution function (CDF) of the instantaneous fading, which are used to derive the PDF and CDF of the instantaneous SNR. The paper also considers deriving the asymptotic PDF, CDF, moment generating function (MGF) of the SNR, as well as the outage probability and BER. The obtained results show that a strong direct link may limit the gain obtained using the RIS.