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Abstract This article proposes a polarization-insensitive compact Metamaterial (MM) energy harvester that can be used seamlessly in the S-band and C-band frequencies. It is important to focus on the limitations of many current designs of harvesters, which need to be overcome. The existing devices are large and often operate in a single frequency band, while their Energy Harvesting (EH) efficiency is low. The proposed harvester solves these problems using smart technology, using a rectangle strip with two gaps, each containing 50 Ω resistors for efficient energy collecting. Also, four hexagonal ring resonators are embedded into the cross-dumbbell configuration, connecting them with strip lines. Smaller rectangular rings surround these hexagonal rings, each with gaps labelled g1–g4. Despite its sophisticated design, the size of this harvester is (10 × 10) mm2 only. This harvester operates at frequencies of 3.5 GHz and 5.5 GHz, demonstrating remarkable absorption responses across varying polarizations and incident angles in both transverse electric (TE) and transverse magnetic (TM) modes. The simulation results indicated impressive energy harvesting efficiencies of 97% at 3.5 GHz and 98% at 5.5 GHz. In addition, experiments in an anechoic chamber with a 3 × 3 array (30 × 30) mm2 were used to confirm the efficiencies empirically. The simulated and measured results showed a strong correlation, confirming the reliability of the proposed design. The proposed MM harvester is distinguished by its high efficiency, polarization-insensitive behaviour, and compactness, making it very promising for many applications in EH.

Abstract Background Cotton is one of the topmost fiber crops throughout the globe. During the last decade, abrupt changes in the climate resulted in drought, heat, and salinity. These stresses have seriously affected cotton production and significant losses all over the textile industry. The GhAGC kinase, a subfamily of AGC group and member of serine/threonine (Ser/Thr) protein kinases group and is highly conserved among eukaryotic organisms. The AGC kinases are compulsory elements of cell development, metabolic processes, and cell death in mammalian systems. The investigation of RNA editing sites within the organelle genomes of multicellular vascular plants, such as Gossypium hirsutum holds significant importance in understanding the regulation of gene expression at the post-transcriptional level. Methods In present work, we characterized twenty-eight GhAGC genes in cotton and constructed phylogenetic tree using nine different species from the most primitive to the most recent. Results In sequence logos analyses, highly conserved amino acid residues were found in G. hirsutum, G. arboretum, G. raimondii and A. thaliana. The occurrence of cis-acting growth and stress-related elements in the promoter regions of GhAGCs highlight the significance of these factors in plant development and abiotic stress tolerance. Ka/Ks levels demonstrated that purifying selection pressure resulting from segmental events was applied to GhAGC with little functional divergence. We focused on identifying RNA editing sites in G. hirsutum organelles, specifically in the chloroplast and mitochondria, across all 28 AGC genes. Conclusion The positive role of GhAGCs was explored by quantifying the expression in the plant tissues under abiotic stress. These findings help in understanding the role of GhAGC genes under abiotic stresses which may further be used in cotton breeding for the development of climate smart varieties in abruptly changing climate.

Abstract This study explores integrating blockchain technology into the Internet of Medical Things (IoMT) to address security and privacy challenges. Blockchain’s transparency, confidentiality, and decentralization offer significant potential benefits in the healthcare domain. The research examines various blockchain components, layers, and protocols, highlighting their role in IoMT. It also explores IoMT applications, security challenges, and methods for integrating blockchain to enhance security. Blockchain integration can be vital in securing and managing this data while preserving patient privacy. It also opens up new possibilities in healthcare, medical research, and data management. The results provide a practical approach to handling a large amount of data from IoMT devices. This strategy makes effective use of data resource fragmentation and encryption techniques. It is essential to have well-defined standards and norms, especially in the healthcare sector, where upholding safety and protecting the confidentiality of information are critical. These results illustrate that it is essential to follow standards like HIPAA, and blockchain technology can help ensure these criteria are met. Furthermore, the study explores the potential benefits of blockchain technology for enhancing inter-system communication in the healthcare industry while maintaining patient privacy protection. The results highlight the effectiveness of blockchain’s consistency and cryptographic techniques in combining identity management and healthcare data protection, protecting patient privacy and data integrity. Blockchain is an unchangeable distributed ledger system. In short, the paper provides important insights into how blockchain technology may transform the healthcare industry by effectively addressing significant challenges and generating legal, safe, and interoperable solutions. Researchers, doctors, and graduate students are the audience for our paper.

Abstract Background Many long noncoding RNAs (lncRNAs) with altered expression significantly influence colorectal cancer (CRC) progression and behavior. The functions of many lncRNAs in CRC are not clear yet. This study aimed to discover novel lncRNA entities and comprehensively examine and validate their roles and underlying molecular mechanisms in CRC. Methods Tissue samples, both tumourous and non-tumourous, from three CRC patients were submitted for sequencing. Following expression validation in samples from ten patients and four CRC cell lines. The lncRNA KCNMA1-AS2 was synthesized by In-vitro transcription RNA synthesis and the lncRNA was directly transfected into CRC cell lines to overexpress. Functional assays including MTT proliferation assay, Annexin-V/propidium iodide apoptosis assay, wound healing migration assay and cell cycle assays were performed to evaluate the effect of overexpression of KCNMA1-AS2. Furthermore, the binding of KCNMA1-AS2 to miR-1227-5p was confirmed using dual luciferase reporter assays and qPCR analyses. Subsequent bioinformatics analyses identified 58 potential downstream targets of miR-1227-5p across three databases. Results In this study, we identified the lncRNA KCNMA1-AS2, the expression of which was down-regulated consistently in cancer tissues and CRC cell lines compared to non-cancerous tissues. The overexpression of lncRNA KCNMA1-AS2 led to significant reduction in CRC cell proliferation and migration, increase in cell apoptosis, and more cells arrested in S phase. Additionally, the interaction between KCNMA1-AS2 and miR-1227-5p was confirmed through dual luciferase reporter assay and qPCR analysis. It is also putatively predicted that MTHFR and ST8SIA2 may be linked to CRC based on bioinformatics analyses. Conclusions LncRNA KCNMA1-AS2 exhibited distinct gene expression patterns in both CRC tissue and cell lines, impacting various cellular functions while also acting as a sponge for miR-1227-5p.The findings spotlight lncRNA KCNMA1-AS2 as a potential marker for diagnosis and treatment of CRC.

Abstract Background This study’s purposes were to evaluate the impact of biological therapies on outcomes in patients with severe asthma (SA) and chronic rhinosinusitis (CRS) and to compare these effects among those with NP (CRSwNP) versus those without NP (CRSsNP) in the “real-world” setting in Saudi Arabian patients. Methods From March to September 2022, a retrospective observational cohort study was undertaken at the severe asthma clinics of the Armed Forces Hospital—Southern Region (AFHSR) and King Khalid University Hospital, Abha, Saudi Arabia, to delineate the effects of dupilumab therapy. Outcomes were assessed, including clinical outcomes, FEV1, and laboratory findings before and one year after dupilumab. Post-therapy effects were compared between CRSwNP and CRSsNP. Results Fifty subjects were enrolled, with a mean age of 46.56. There were 27 (54%) females and 23(46%) males. Significant improvements in clinical parameters (frequency of asthma exacerbations and hospitalizations, the use of OCs, anosmia, SNOTT-22, and the ACT), FEV1, and laboratory ones (serum IgE and eosinophilic count) were observed 6 and 12 months after using dupilumab (p

Tomato yellow leaf curl virus (TYLCV) and tomato spotted wilt virus (TSWV) are well-known examples of the begomovirus and orthotospovirus genera, respectively. These viruses cause significant economic damage to tomato crops worldwide. Weeds play an important role in the ongoing presence and spread of several plant viruses, such as TYLCV and TSWV, and are recognized as reservoirs for these infections. This work applies a comprehensive approach, encompassing field surveys and molecular techniques, to acquire an in-depth understanding of the interactions between viruses and their weed hosts. A total of 60 tomato samples exhibiting typical symptoms of TYLCV and TSWV were collected from a tomato greenhouse farm in Nonsan, South Korea. In addition, 130 samples of 16 different weed species in the immediate surroundings of the greenhouse were collected for viral detection. PCR and reverse transcription-PCR methodologies and specific primers for TYLCV and TSWV were used, which showed that 15 tomato samples were coinfected by both viruses. Interestingly, both viruses were also detected in perennial weeds, such as Rumex crispus, which highlights their function as viral reservoirs. Our study provides significant insights into the co-occurrence of TYLCV and TSWV in weed reservoirs, and their subsequent transmission under tomato greenhouse conditions. This project builds long-term strategies for integrated pest management to prevent and manage simultaneous virus outbreaks, known as twindemics, in agricultural systems.

In the contemporary technological landscape, ensuring confidentiality is a paramount concern addressed through various skillsets. Cryptography stands out as a scientific methodology for safeguarding communication against unauthorized access. Within the realm of cryptography, numerous encryption algorithms have been developed to enhance data security. Recognizing the imperative for nonstandard encryption algorithms to counter traditional attacks, this paper puts forth novel encryption techniques. These methods leverage special corona graphs, star graphs, and complete bipartite graphs, incorporating certain algebraic properties to bolster the secure transmission of messages. The introduction of these proposed encryption schemes aims to elevate the level of security in confidential communication, some of the applications of these schemes are given in the later section.

This paper contributes to the understanding of media discourses surrounding the sustainability of nuclear energy, particularly within a crisis context. The study examines the implications of the adoption of the Complementary Delegated Act on EU taxonomy by the European Commission on July 6, 2022, which officially categorises nuclear energy as sustainable. Employing a critical discourse analysis (CDA) methodology, the research explores the discourse strategies –nomination, predication, and argumentation– utilised across 695 news articles. The findings reveal the intricate representation of nuclear energy’s sustainability across the media. The Russian-Ukrainian conflict amplifies favourably the nuclear energy view as a reliable and sustainable energy source. However, the media discourse on nuclear energy’s sustainability reveals a variety of viewpoints. These varying outlooks provide essential insights for shaping effective energy policies. This research contributes valuable insights into understanding how media shapes and influences public perception of sustainability energy policies, amplifying specific actors’ voices and steering discourse toward distinct trajectories. Such insights are crucial in crafting energy policies.

Abstract A substantial level of significance has been placed on renewable energy systems, especially photovoltaic (PV) systems, given the urgent global apprehensions regarding climate change and the need to cut carbon emissions. One of the main concerns in the field of PV is the ability to track power effectively over a range of factors. In the context of solar power extraction, this research paper performs a thorough comparative examination of ten controllers, including both conventional maximum power point tracking (MPPT) controllers and artificial intelligence (AI) controllers. Various factors, such as voltage, current, power, weather dependence, cost, complexity, response time, periodic tuning, stability, partial shading, and accuracy, are all intended to be evaluated by the study. It is aimed to provide insight into how well each controller performs in various circumstances by carefully examining these broad parameters. The main goal is to identify and recommend the best controller based on their performance. It is notified that, conventional techniques like INC, P&O, INC-PSO, P&O-PSO, achieved accuracies of 94.3, 97.6, 98.4, 99.6 respectively while AI based techniques Fuzzy-PSO, ANN, ANFIS, ANN-PSO, PSO, and FLC achieved accuracies of 98.6, 98, 98.6, 98.8, 98.2, 98 respectively. The results of this study add significantly to our knowledge of the applicability and effectiveness of both AI and traditional MPPT controllers, which will help the solar industry make well-informed choices when implementing solar energy systems.

Electricity demand increasing day by day with the passage of time, fossil fuels and traditional electricity sources are becoming obsolete. The possibility of dealing with a power deficit and importing oil and gas for electricity generation exists for developing nations like Pakistan. One of the most accessible and affordable sources of renewable energy in the world is solar power generating. Photovoltaic (PV) cell performance is influenced by the environment and the technology used to capture the available energy. The Islamabad region of Pakistan is blessed with an average of 300 days of sunshine a year due to its location in the solar band at 33.7215°N latitude and 73.0433°E longitude. The current study compares and contrasts how Pakistan and South Asian Association for Regional Cooperation (SAARC) countries generate electricity using solar systems. Choosing one geographical place from all SAARC nations, PVSyst software is used to develop and estimate the performance study for solar model cell Panasonic 320Wp, 48V and 1.5 kW Inverter Fronius for a 9.6 kWp load. Energy supplied into the grid is studied together with various losses that occur in the system, with the choice of PV arrays and other factors remaining the same for comparison study of all SAARC countries. The approach to minimize losses through the utilization of an adjustable tilt angle has been implemented. This study will be useful in estimating and planning the solar energy output in all SAARC nations for the same photovoltaic system, and it will be simple to trade all PV system components in the region.

The fabric quality in textile industry is characterized by the texture (weave pattern) as it plays a vital role for the production and design of best quality fabric. The earlier proposed automated weave identification methods based on image processing techniques are highly dependent on the lighting conditions. The machine learning methods have been reported to show better accuracy. However, they require very large training datasets, very high processing power and computation time. This study proposes improved accuracy with smaller dataset and reduced computation time by proposing a modification of VGG16 model by adding two additional pooling layers. Using evaluation metrics of both models, the modified model results were analysed according to accuracy, balanced accuracy, and F1-score. On the basis of investigational outcomes, a comparison has been performed with earlier work. The results show that the proposed VGG-16 model is capable to achieve state-of-the-art accuracy and avoid unnecessary activation features by freezing the main convolutional base layers. Ultimately, as evidenced by the performance of the modified VGG-16 deep learning model, the proposed method demonstrated improved accuracy. The study results show that the proposed modified VGG16 algorithm is able to recognize the features of provided database with 90% accuracy and F1-Score ranging from 0.8 to1.

Abstract The potential of solid waste as an energy source is clear, owing to its wide availability and renewable properties, which provide a critical answer for energy security. This can be especially effective in reducing the environmental impact of fossil fuels. Countries that rely heavily on coal should examine alternatives such as electricity from solid waste to provide a constant energy supply while also contributing to atmospheric restoration. In this regards, Low Emissions Analysis Platform (LEAP) is used for simulation the entire energy system in Pakistan and forecasted its capital cost and future CO2 emissions in relation to the use of renewable and fossil fuel resources under the different growth rates of solid waste projects like 20%, 30% and 40% for the study period 2023–2053. The results revealed that, 1402.97 TWh units of energy are generated to meet the total energy demand of 1193.93 TWh until 2053. The share of solid waste based electricity in total energy mix is increasing from a mere 0.81% in 2023 to around 9.44% by 2053 under the 20% growth rate, which then increase to 39.67% by 2053 under the 30% growth rate and further increases to 78.33% by 2053 under the 40% growth rate. It is suggested that 40% growth rate for solid waste based electricity projects is suitable for Pakistan until 2053 because under this condition, renewable sources contributes 95.2% and fossil fuels contributed 4.47% in the total energy mix of Pakistan. Hence, CO2 emissions are reduced from 148.26 million metric tons to 35.46 million metric tons until 2053 but capital cost is increased from 13.23 b$ in 2023 to 363.11 b$ by 2053.

Abstract The effects of inhibitor concentration on the corrosion rate and inhibition efficiency of AISI 1020 steel in an acidic and alkaline environment were investigated by means of weight loss measurement at an interval of 7 days and 14 days. To carry out this investigation, the Arachis hypogaea hull was extracted and concentrated in various weight percentages. The inhibition efficiency increased with the increased concentrations of AISI 1020 steel that were immersed in acidic and alkaline solution in the absence and presence of varying inhibitor concentrations of Arachis hypogaea hull extracts. The corrosion behavior, including the corrosion rate, is meticulously characterized through the corrosion rate analysis. The results showed that there is an increase in inhibition efficiency with an increase in inhibitor concentration and that there is a decrease in inhibition efficiency with an increase in immersion time. The organic inhibitor (Arachis hypogaea hull) produced the best inhibition efficiency of 96.4% at a 30% concentration. From the result obtained, Arachis hypogaea hull extracts revealed that it is best suited for inhibition of corrosion of mild steel in both acidic and alkaline environments. The goal of this research paper is to develop a comprehensive understanding of the corrosion inhibition and adsorption mechanisms associated with the implementation of the Arachis hypogaea hull as a natural corrosion inhibitor.

This study investigates the effect of financial inclusion (FI), considering micro and macro indicators as well as micro- and macro-FI separately, on the stability of Asian banks and examines the moderating effect of bank competition (BC) on this relationship. Using data from 2011 to 2021, this study examines the relationship between FI, BC, and bank stability (BS). The hypotheses were tested using a “two-step system-GMM framework”. The findings were also authenticated using the panel OLS approach. The results indicate that FI (considering micro- and macro-indicators) and micro- and macroFI have significant positive effects on the stability of Asian banks. However, the impact of micro-FI is greater than that of macro-FI on the BS in Asia. Furthermore, the results manifest that BC has a significant positive impact on BS and positively moderates the relationship between micro-FI and BS, whereas it negatively moderates the relationship between macro-FI and BS. The findings of this study have practical implications for regulators, bankers, and policymakers involved in formulating strategies to enhance Asian banks’ stability.

Based on enhanced thermal performances of hybrid nanomaterials, various multidisciplinary applications of such hybrid nanofluids are presented in the cooling processes, HVAC systems, energy sectors, boosting the energy sources, automotive thermal systems etc. Owing to such motivated applications in mind, different mathematical models are developed. However, the thermal analysis for hybrid nanofluid with help of fractional models is not focused properly. Therefore, the objective of current research is to develop a mathematical model for enhancement of heat transfer by using the hybrid nanofluid. The decomposition of zinc oxide (ZnO) and ferric oxide (Fe3O4) with kerosene oil base fluid is used for identifying the thermal reflection of hybrid nanofluid. The motivations to improve the thermal prospective of kerosene oil is due to its importance in the energy sources as a fuel and industrial applications like solvent, degreaser and operation of air craft. The vertical moving surface is used to initiates the flow. The natural convective flow is further perturbed with applications of mixed convection effects. The evaluation for heat transfer is inspected by incorporating the external heat source. The mathematical modelling of problem is presented via fractional expressions. The Prabhakar scheme is used to develop the analytical expressions. The accuracy of implemented scheme is inspected by comparing the numerical data computed via Zakian, Stehfest and Tzou's algorithms. The significance of problem is visualized in view of involved parameters like fractional parameters, nanoparticles volume fraction, Grashof number and Prandtl number. The results claim that the enhancement in heat transfer due to decomposition of zinc oxide and ferric oxide nanoparticles is more exclusive as compared to simple nanofluid. The heat transfer enhanced due to nanoparticles volume fraction.

Despite its ambitious “economic sustainability” objectives, the China-Pakistan Economic Corridor (CPEC) has been the subject of growing environmental anxiety. Considering the CPEC developments, it is clear that Pakistan is ready to fully embrace this new industrial chapter and take advantage of its major benefits to solve social, energy, infrastructure, and economic problems. However, it should also seriously commit to undertaking proper environmental impact assessments and upgrading system resilience. Data was collected from 400 respondents from Pakistan, and structural equation modeling was applied with the help of AMOS. Factor analysis and structural equation modeling techniques were used to estimate the results and test the study's hypothesis. The results indicate a strong socio-economic impact across perceived economic, infrastructure, social, and total impacts, but they identify a negative association between infrastructure innovation and environmental sustainability. Moreover, results revealed that infrastructure supports social and economic growth, but it might have a substantial negative impact on biodiversity. According to findings, Pakistan may be more vulnerable to climate change due to three potential environmental issues: coal-fired power plants, CO2 concentration along the CPEC route, and increased traffic on the Karakorum Highway. Furthermore, future international trade will be significantly impacted by the corridor. It may, however, also accelerate the destruction of the ecosystem over time due to the industrial revolution.

Objective: To evaluate the diagnostic efficacy and safety of fine needle aspiration cytology performed under ultrasound guidance. Study Design: Comparative prospective study. Place and Duration of Study: Pulmonology Out-Patient Department, Gulab Devi Teaching Hospital, Lahore Pakistan, from Jun 2018 to Dec 2019. Methodology: One hundred and twenty-four patients of age 10-80 years, with pulmonary, mediastinal, pleural or chest wall lesions, fulfilling the minimal fitness criteria were included. Aspiration was done by a 23-gauge needle, smear made, and fixed slides were sent to the Cytology Department for evaluation. Complications were monitored. Findings were recorded on preformed proforma. Results: 121/124(97.6%) aspirates were adequate while 03 cases (2.4%) were reported not representing the lesion. Fifty cases (41.3%) showed malignant cytology, 71/121 aspirates (58.7%) were non-malignant. A specific cell type could be characterized in 84.5% non-malignant and 68.0% malignant aspirates. Complications were a few drops bleeding at needle aspiration site in 7.5 % and minor pain in 12.0 % patients. Conclusion: Fine needle aspiration cytology under ultrasound guidance is an effective and safe diagnostic tool in pulmonary medicine.

This research provides a detailed explanation of the design, simulation, and experimental of quad-square metamaterial-based negative-index unit cells for S-band applications. The Computer Simulation Technology 2022 licensee version was utilized to design and obtain numerical results for the unit cell. The proposed unit cell for the metamaterial has dimensions of 5 × 5 × 1.57 mm3. The substrate chosen was FR-4, resulting in a substantial effective medium ratio value of 19.07. A series of systematic parametric studies were conducted to optimize the quad square metamaterial structure. Key parameters, such as substrate types, unit cell arrays, thicknesses of substrate, and split gaps, were varied to determine their impact on the structure. The validated equivalent circuit result was compared to the simulated results, showing a significant agreement. The demonstrated correlation between simulation and experimental data highlights the dependability of the proposed quad-square metamaterial, positioning it as a viable option for a range of electromagnetic applications, such as communication systems, sensors, and imaging devices.

Carbonate-substituted hydroxyapatite containing magnesium (Mg–C-HAp) was introduced through dissolution-precipitation treatment of hydroxyapatite containing magnesium (Mg-HAp) based on a black sea urchin (arbacia lixula) shells as novel biogenic materials. Based on chemical composition analysis, the Mg–C-HAp formed A- and B-type CHAp, which contained high carbonate ions. The Ca/P ratio of Mg–C-HAp was 1.707, very close to the biological bone apatite of 1.71. The Mg content in Mg–C-HAp was also relatively high, with the Mg/(Ca + Mg) ratio of 0.139, which is beneficial for antibacterial agents. The morphology of Mg–C-HAp showed particles with nanosize that provide a large surface area of ion promotion. The antibacterial test revealed that the Mg–C-HAp performed high antibacterial activity against Pseudomonas aeruginosa. The Mg–C-HAp-based porous scaffolds were then fabricated using the freeze-drying method with variations of polyvinyl alcohol (PVA) and gelatin fraction. According to the physicochemical analysis, the addition of PVA and gelatin in the scaffold structure decreased the crystallinity of the Mg–C-HAp/PVA/Gel scaffold. This lower crystallinity indicates high biodegradability, which is good for new bone growth. The macropores of the Mg–C-HAp/PVA/Gel scaffold were appropriate for new bone and blood vessel formation. The micropores of the Mg–C-HAp/PVA/Gel scaffold can be a medium for cells to grow. The microporosity of the Mg–C-HAp/PVA/Gel scaffold was also suitable for cell nutrient promotion. The compressive strength of the Mg–C-HAp/PVA/Gel scaffold was sufficient for bone regeneration. The Mg–C-HAp/PVA/Gel scaffold demonstrated high antibacterial activity against P. aeruginosa, so the Mg–C-HAp/PVA/Gel scaffold can maintain the role of Mg and carbonate content for antibacterial purposes. The good physicochemical, mechanical, and antibacterial properties of the Mg–C-HAp/PVA/Gel scaffold represented its suitable characteristics for antibacterial bone scaffolds.

Sign language is the primary communication tool used by the deaf community and people with speaking difficulties, especially during emergencies. Numerous deep learning models have been proposed to solve the sign language recognition problem. Recently. Bidirectional LSTM (BLSTM) has been proposed and used in replacement of Long Short-Term Memory (LSTM) as it may improve learning long-team dependencies as well as increase the accuracy of the model. However, there needs to be more comparison for the performance of LSTM and BLSTM in LRCN model architecture in sign language interpretation applications. Therefore, this study focused on the dense analysis of the LRCN model, including 1) training the CNN from scratch and 2) modeling with pre-trained CNN, VGG-19, and ResNet50. Other than that, the ConvLSTM model, a special variant of LSTM designed for video input, has also been modeled and compared with the LRCN in representing emergency sign language recognition. Within LRCN variants, the performance of a small CNN network was compared with pre-trained VGG-19 and ResNet50V2. A dataset of emergency Indian Sign Language with eight classes is used to train the models. The model with the best performance is the VGG-19 + LSTM model, with a testing accuracy of 96.39%. Small LRCN networks, which are 5 CNN subunits + LSTM and 4 CNN subunits + BLSTM, have 95.18% testing accuracy. This performance is on par with our best-proposed model, VGG + LSTM. By incorporating bidirectional LSTM (BLSTM) into deep learning models, the ability to understand long-term dependencies can be improved. This can enhance accuracy in reading sign language, leading to more effective communication during emergencies.

Satellite communication (SatCom) is essential in modern telecommunication infrastructure, providing global connectivity and overcoming limitations of the terrestrial network. With the emergence of vertical heterogeneous networks integrating terrestrial and non-terrestrial networks, authentication becomes crucial to address security challenges, especially in low Earth orbit SatCom. This review comprehensively explores the authentication mechanisms, challenges, and future research directions to offer a holistic view of authentication in satellite communication networks. This study adopts a systematic approach that follows five phases: research planning, search execution, study selection, data classification, and study mapping. Explores recent research on various authentication schemes in SatCom, focusing on fundamental research questions to uncover the complexities associated with authentication in this domain. Existing authentication protocols are analyzed by using a multi-criteria classification approach to highlight their strengths, weaknesses, and security implications. By investigating current authentication systems in SatCom and identifying emerging trends, this review offers a roadmap for future research, providing valuable insights for practitioners, policymakers, and researchers. Enhances understanding of SatCom authentication complexity, laying the groundwork for resilient security measures crucial to the sustainable advancement of Space-Ground network technology.

Online content availability, commercial viability, and technological advancements for English and European languages direct mainstream search engines to prioritize the search results of these high-resource languages. This makes it challenging for low-resource language users to access the search results in regional languages which is essential to promote literacy, inclusion, and digital accessibility. In this article, we create Humkinar– a Urdu language search engine using open-source tools. Our search engine is designed with five key components: computing infrastructure, data collector, search manager, web analytics engine, and user interface. First, our in-house computing infrastructure offers 160 GB RAM, 80 cores, and 30 TB memory to support the operations of the search engine. Next, we customize an open-source web crawler with a specialized Urdu language-focused URL selection algorithm, webpage parser, and content selection mechanism to collect Urdu webpages with optimized computing and Internet resources. We also employ specialized content scrapers to collect targeted and high-priority Urdu content like news articles, Wikipedia, poetry, and books. Overall, our data collector module has successfully curated a repository containing 14 million crawled webpages and 2.2 million scraped Urdu documents. Also, we design post-processing tools for tasks such as topic classification, de-duplication, profanity assessment, text summarization, and the scoring of website quality specific to the Urdu language. In addition, acknowledging the limitations of applying conventional ranking signals to Urdu language, search manager utilizes our seven derived ranking signals for search results. These signals are tuned to emphasize the richness and quality of Urdu language websites and content in search results. Moreover, we incorporate a web analytics engine into our search engine to collect and analyze user actions and metadata to enhance the overall functionality and effectiveness of the search engine. Our web analytics engine has recorded 400K user interactions from 83 countries conducted through the interactive user interface. Finally, we conduct usability testing of search engine with native Urdu language speakers to assess the strengths and weaknesses of our search engine.

This study aims to investigate the alternative model structure based on a feature selection algorithm on multiple features-framework of human activity recognition (HAR) via wearable sensor-based modality. Neighborhood component analysis (NCA) is a linear transformation that maximizes the accuracy of specified classification events used as the benchmark for the proposed algorithm. Also, the effect of different combinations of sensor configurations of two, three, and all four sensors on the performance of the developed model was studied. The effectiveness and shortcomings of best sensor configuration were highlighted. Results were compared between different sensor configurations and benchmark HAR dataset. To maximize the regularization of NCA, fine-tuning the algorithm to maximize relevance and minimize redundancy (MRMR) was proposed. Results demonstrated that RNCA-MRMR could establish an efficient algorithm that can satisfy the model validation tests with significant advantages over feature number and predictive accuracy at 93.5%, 93.7%, and 94.5% for two, three, and all four sensors respectively. Furthermore, the adaptability of RNCA-MRMR to different data characteristics has ensured an optimal and task-specific representation of the data. In essence, the combined strength of RNCA and MRMR provides a versatile and effective approach for extracting meaningful features and enhancing the overall performance of machine learning models.

Software defect prediction plays a crucial role in enhancing software quality while achieving cost savings in testing. Its primary objective is to identify and send only defective modules to the testing stage. This research introduces an intelligent ensemble-based software defect prediction model that combines diverse classifiers. The proposed model employs a two-stage prediction process to detect defective modules. In the first stage, four supervised machine learning algorithms are employed: Random Forest, Support Vector Machine, Naïve Bayes, and Artificial Neural Network. These algorithms are optimized through iterative parameter optimization to achieve the highest accuracy possible. In the second stage, the predictive accuracy of the individual classifiers is integrated into a voting ensemble to make the final predictions. This ensemble approach further improves the accuracy and reliability of the defect predictions. Seven historical defect datasets from the NASA MDP repository, namely CM1, JM1, MC2, MW1, PC1, PC3, and PC4, were utilized to implement and evaluate the proposed defect prediction system. The results demonstrate that each dataset’s proposed intelligent system achieved remarkable accuracy, outperforming twenty state-of-the-art defect prediction techniques, including base classifiers and ensemble methods.

An experiment was conducted to investigate the optimal use of high-salt water for alfalfa fodder growth and quality in Aridisol. The experiment included five treatments and was performed using a completely randomized design (CRD) as factorial design with three replications. We used a leaching fraction technique (LF), which is a mitigating technique (MT). The five treatments were T1 = MT1 as normal irrigation (control), T2 = MT2 as a leaching fraction (LF) of 15% with the same quality of water, T3 = MT3 as a LF of 30% with the same quality of water, T4 = MT4 as a LF of 15% with good-quality water (as percentage of total water), in the form of 2–3 irrigations every 3 months, and T5 = MT5 as a LF of 30% with good-quality water (as percentage of total water), in the form of 2–3 irrigations every 3 months. The duration of the experiment was three years and normal soil (non-saline, non-sodic) was used in the current study. Results showed that saline water irrigation negatively affected the growth traits, but the application of the LF technique with same-quality or good-quality water mitigated such negative effects. The fodder quality traits such as crude protein (CP), crude fiber (CF) and ashes were also affected in a negative way with the use of saline irrigation water. This negative impact was more intensified in the third year as the concentration of salts increased in saline water during the three years of the current investigation. A LF with canal water at 15 or 30% reduced the negative effects of salt stress and improved fodder biomass production and quality traits. For examples, using a LF with canal water at 30% increased the biomass production to 33.30 g and 15.87 g when plants were irrigated with W1 and W5, respectively. In addition, it improved quality traits such as crude protein content (5.54% and 3.73%) and crude fiber content (14.55% and 12.75%) when plants were irrigated with W1 and W5, respectively. It was concluded that the LF technique can be recommended for practice in the case of saline water irrigation for the optimized growth and quality of alfalfa fodder.