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Abstract Posttranslational modifications (PTMs) are essential for regulating protein localization and stability, significantly affecting gene expression, biological functions, and genome replication. Among these, sumoylation a PTM that attaches a chemical group to protein sequences—plays a critical role in protein function. Identifying sumoylation sites is particularly important due to their links to Parkinson’s and Alzheimer’s. This study introduces XGBoost-Sumo, a robust model to predict sumoylation sites by integrating protein structure and sequence data. The model utilizes a transformer-based attention mechanism to encode peptides and extract evolutionary features through the PsePSSM-DWT approach. By fusing word embeddings with evolutionary descriptors, it applies the SHapley Additive exPlanations (SHAP) algorithm for optimal feature selection and uses eXtreme Gradient Boosting (XGBoost) for classification. XGBoost-Sumo achieved an impressive accuracy of 99.68% on benchmark datasets using 10-fold cross-validation and 96.08% on independent samples. This marks a significant improvement, outperforming existing models by 10.31% on training data and 2.74% on independent tests. The model’s reliability and high performance make it a valuable resource for researchers, with strong potential for applications in pharmaceutical development.

Abstract The open nature of Wireless Sensor Networks (WSNs) renders them an easy target to malicious code propagation, posing a significant and persistent threat to their security. Various mathematical models have been studied in recent literature for understanding the dynamics and control of the propagation of malicious codes in WSNs. However, due to the inherent randomness and uncertainty present in WSNs, stochastic modeling approach is essential for a comprehensive understanding of the propagation of malicious codes in WSNs. In this paper, we formulate a general stochastic compartmental model for analyzing the dynamics of malicious code distribution in WSNs and suggest its possible control. We incorporate the stochasticity in the classical deterministic model for the inherent unpredictability in code propagation, which results in a more appropriate representation of the dynamics. A basic theoretical analysis including the stability results of the model with randomness is carried out. Moreover, a higher-order spectral collocation technique is applied for the numerical solution of the proposed stochastic model. The accuracy and numerical stability of the model is presented. Finally, a comprehensive simulation is depicted to verify theoretical results and depict the impact of parameters on the model’s dynamic behavior. This study incorporates stochasticity in a deterministic model of malicious codes spread in WSNs with the implementation of spectral numerical scheme which helps to capture these networks’ inherent uncertainties and complex nature.

Abstract Background RNA 5-methyluridine (m5U) modifications play a crucial role in biological processes, making their accurate identification a key focus in computational biology. This paper introduces Deep-m5U, a robust predictor designed to enhance the prediction of m5U modifications. The proposed method, named Deep-m5U, utilizes a hybrid pseudo-K-tuple nucleotide composition (PseKNC) for sequence formulation, a Shapley Additive exPlanations (SHAP) algorithm for discriminant feature selection, and a deep neural network (DNN) as the classifier. Results The model was evaluated using two benchmark datasets, i.e., Full Transcript and Mature mRNA. Deep-m5U achieved overall accuracies of 91.47% and 95.86% for the Full Transcript and Mature mRNA datasets with 10-fold cross-validation, and for independent samples, the model attained 92.94% and 95.17% accuracy. Conclusion Compared to existing models, Deep-m5U showed approximately 5.23% and 3.73% higher accuracy on the training data and 3.95% and 3.26% higher accuracy on independent samples for the Full Transcript and Mature mRNA datasets, respectively. The reliability and effectiveness of Deep-m5U make it a valuable tool for scientists and a potential asset in pharmaceutical design and research.

BACKGROUND: This cross-sectional pilot study aimed to evaluate awareness and management strategies among obstetricians and surgeons in the Kingdom of Saudi Arabia for round ligament varicosities (RLV) during pregnancy. MATERIALS AND METHODS: An online questionnaire was distributed through convenience sampling to surgeons and obstetricians across the Kingdom of Saudi Arabia. It assessed demographics, clinical knowledge, experience in diagnosing and managing groin lumps during pregnancy, and engagement in continuing medical education. RESULTS: Of the 39 participants, 56.41% (n = 22) were obstetricians and 43.59% (n = 17) were surgeons, with the majority having 5–10 years of practice (35.90%, n = 14). Groin hernia was reported as the most frequent diagnosis for groin lumps during pregnancy (43.59%, n = 17), whereas only 7.69% (n = 3) identified RLV, indicating a significant gap in recognizing RLV. Furthermore, 35.90% (n = 14) recognized RLV as a differential diagnosis, but only 25.64% (n = 10) were aware of its management. CONCLUSION: RLV, which is more common than hernias during pregnancy, is often misdiagnosed due to its similar presentation. Accurate differentiation, often achieved through ultrasound, is crucial as management strategies significantly differ. This study underscores the need for enhanced awareness and training among obstetricians and surgeons to accurately diagnose and manage RLV. The observed knowledge gaps highlight the critical role of continuous medical education in improving patient care. These findings will guide a larger study to assess practices across the Kingdom and develop national recommendations for the management of RLV and groin hernias during pregnancy.

To investigate the gravitational effects of massive objects on a typical observer, we studied the dynamics of a test particle following BMS3 geodesics. We constructed the BMS3 framework using the canonical phase space formalism and the corresponding Hamiltonian. We focused on analyzing these effects at fine scales of spacetime, which led us to quantization of the phase space. By deriving and studying the solutions of the quantum equations of motion for the test particle, we obtained its energy spectrum and explored the behavior of its wave function. These findings offer a fresh perspective on gravitational interactions in the context of quantum mechanics, providing an alternative approach to traditional quantum field theory analyses.

Device-to-device (D2D) communication is an essential part of the 5G system used in cellular networks, which enables devices to communicate with each other without passing the base station BS. It is likely to aid in satisfying increasing capacity and effectively offloading traffic from the BS by distributing the transmission between D2D users from one side and cellular users and the BS from the other side. It results in improved throughput, energy efficiency, and other parameters. At the same time, the introduction of D2D communication in cellular networks creates new technical challenges to be addressed. One of the major issues is the mitigation of interference between device users (DUs) and primary users (PUs). This issue leads to performance degradation if it is not managed properly. In this paper, an intelligent distributed channel selection framework (IDCSF) in D2D communication for 5G networks with hybrid selection modes of D2D is proposed to help DUs select the best channel for transmission to mitigate interference. This channel selection framework classifies the available sensed channels using self-organizing map (SOM) learning technique into four classes considering channels with sensing errors false alarm (FalsA) and miss detection (MissD) to prevent using occupied channels. Furthermore, PU activity model is adjusted to aid in recovering from bad channel selection decisions. Moreover, fuzzy logic technique is utilized to determine the reliable channels which can help the DU along with channel selection algorithm to find channel’s weight values. The channel with highest weight value is selected as the best channel. Simulation results show that the proposed framework IDCSF enhances selecting the best channel, improves throughput and spectral utilization, and reduces average delay, interference and search time compared to other approaches.

Aim: This study leveraged standard-of-care CT scans of patients receiving unilateral radiotherapy (RT) for early tonsillar cancer to detect volumetric changes in the carotid arteries, and determine whether there is a dose–response relationship. Methods: Disease-free cancer survivors (>3 months since therapy and age > 18 years) treated with intensity modulated RT for early (T1-2, N0-2b) tonsillar cancer with pre- and post-therapy contrast-enhanced CT scans available were included. Patients treated with definitive surgery, bilateral RT, or additional RT before the post-RT CT scan were excluded. Isodose lines from treatment plans were projected onto both scans, facilitating the delineation of carotid artery subvolumes in 5 Gy increments (i.e. received 50–55 Gy, 55–60 Gy, etc.). The percent-change in sub-volumes across each dose range was examined. Results: Among 46 patients, 72 % received RT alone, 24 % induction chemotherapy followed by RT, and 4 % concurrent chemoradiation. The median interval from RT completion to the latest, post-RT CT scan was 43 months (IQR 32–57). A decrease in the volume of the irradiated carotid artery was observed in 78 % of patients, while there was a statistically significant difference in mean %-change (±SD) between the total irradiated and spared carotid volumes (−7.0 ± 9.0 vs. + 3.5 ± 7.2, respectively, p

Abstract Post-translational modifications (PTMs) are fundamental to essential biological processes, exerting significant influence over gene expression, protein localization, stability, and genome replication. Sumoylation, a PTM involving the covalent addition of a chemical group to a specific protein sequence, profoundly impacts the functional diversity of proteins. Notably, identifying sumoylation sites has garnered significant attention due to their crucial roles in proteomic functions and their implications in various diseases, including Parkinson’s and Alzheimer’s. Despite the proposal of several computational models for identifying sumoylation sites, their effectiveness could be improved by the limitations associated with conventional learning methodologies. In this study, we introduce pseudo-position-specific scoring matrix (PsePSSM), a robust computational model designed for accurately predicting sumoylation sites using an optimized deep learning algorithm and efficient feature extraction techniques. Moreover, to streamline computational processes and eliminate irrelevant and noisy features, sequential forward selection using a support vector machine (SFS-SVM) is implemented to identify optimal features. The multi-layer Deep Neural Network (DNN) is a robust classifier, facilitating precise sumoylation site prediction. We meticulously assess the performance of PSSM-Sumo through a tenfold cross-validation approach, employing various statistical metrics such as the Matthews Correlation Coefficient (MCC), accuracy, sensitivity, specificity, and the Area under the ROC Curve (AUC). Comparative analyses reveal that PSSM-Sumo achieves an exceptional average prediction accuracy of 98.71%, surpassing existing models. The robustness and accuracy of the proposed model position it as a promising tool for advancing drug discovery and the diagnosis of diverse diseases linked to sumoylation sites.

Abstract This study presents a computational investigation of a stochastic Zika virus along with optimal control model using the Legendre spectral collocation method (LSCM). By accumulation of stochasticity into the model through the proposed stochastic differential equations, we appropriating the random fluctuations essential in the progression and disease transmission. The stability, convergence and accuracy properties of the LSCM are conscientiously analyzed and also demonstrating its strength for solving the complex epidemiological models. Moreover, the study evaluates the various control strategies, such as treatment, prevention and treatment pesticide control, and identifies optimal combinations that the intervention costs and also minimize the proposed infection rates. The basic properties of the given model, such as the reproduction number, were determined with and without the presence of the control strategies. For $$R_01$$ R 0 > 1 , then endemic equilibrium is satisfied, in this case the disease spread in the population at higher scale. The fundamental findings acknowledge the significant impact of stochastic phonemes on the robustness and effectiveness of control strategies that accelerating the need for cost-effective and multi-faceted approaches. In last the results provide the valuable insights for public health department to enabling more impressive mitigation of Zika virus outbreaks and management in real-world scenarios.

Background: Iron deficiency anemia (IDA) is a microcytic-hypochromic anemia prevalent in African and South Asian countries including India. Along with systemic, it presents various oral manifestations that can be diagnosed in dental setups. Literature suggests that there is a state of iron deficiency before it manifests as anemia, which eventually creates an immunodeficient body state that predisposes to various opportunistic infections such as oral candidiasis. Objectives: The present study was designed to evaluate and compare various components of the blood picture including the hemoglobin (Hb%), red blood cell (RBC) count, RBC indices, hematocrit, peripheral blood smear and serum ferritin between iron deficiency (ID), IDA, and healthy subjects. The study also attempts to compare and assess the salivary samples of ID for Candida growth. Material and Methods: A total of 252 female patients of age groups ranging from 18 to 70 years were selected. The study consisted of three study groups, namely, Group A, IDA patients (clinical manifestations with pallor); Group B, ID patients (clinical manifestations without pallor); and Group C, age-matched healthy controls, with 84 in each group. The blood samples were collected to evaluate the hematological parameters including Hb%, RBC count, RBC indices, hematocrit, peripheral blood smear, and serum ferritin. Later, a saliva sample was collected from all study groups to assess the Candida growth. The obtained data were subjected to statistical analysis using SPSS version 20.0 software. Results: The results demonstrated that ID patients had normal blood parameters as healthy controls except for serum ferritin, while IDA patients had significantly (P < 0.001) lower hemoglobin, RBC count, and blood cell indices, specifically mean corpuscular volume, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration, and hematocrit compared to ID and healthy controls. A significant finding is that like IDA patients, ID patients exhibited reduced serum ferritin levels. Contrary to the IDA group, only a small percentage of ID patients had Candida growth, although this was not statistically significant. Conclusion: Within the limitations of the current study, the results showed significantly reduced serum ferritin levels in ID, while other parameters were within the normal limits, and found tendency to develop Candida growth compared with the control subjects. Further studies are warranted to establish the association and causal relationship.

Skin cancer is a prevalent type of cancer that affects millions of people globally. However, detecting it can be a challenging task, even for specialized dermatologists. Early detection is crucial for successful treatment, and deep learning techniques, particularly deep convolutional neural networks (DCNNs), have shown tremendous potential in this area. However, achieving high accuracy results requires large volumes of data for training these DCNNs. Since medical organizations and institutions, individually, do not usually have such amounts of information available, and due to the current regulations regarding intellectual property and privacy of medical patient data, it is difficult to share data in a direct way. The primary objective of this work is to overcome this issue through a federated learning approach. We created a privacy-preserving and accurate skin cancer classification system that can assist dermatologists and specialists in making informed patient care decisions. The federated learning DCNNs architecture uses a combination of convolutional and pooling layers to extract relevant features from skin lesion images. It also includes a fully connected layer for classification. To evaluate the proposed architecture, we tested it on three datasets of varying complexity and size. The results demonstrate the applicability of the proposed solution and its efficiency for skin cancer classification.

Metering fluids is critical in various industries, and researchers have extensively explored factors affecting measurement accuracy. As a result, numerous sensors and methods are developed to precisely measure volume fractions in multi-phase fluids. A significant challenge in multi-phase fluid pipelines is the formation of scale within the pipes. This issue is particularly problematic in the petroleum industry, leading to narrowed internal diameters, corrosion, increased energy consumption, reduced equipment lifespan, and, most crucially, compromised flow measurement accuracy. This paper proposes a non-destructive metering system incorporating an artificial neural network with capacitive and photon attenuation sensors to address this challenge. The system simulates scale thicknesses from 0 mm to 10 mm using COMSOL multiphysics software and calculates counted rays through Beer Lambert equations. The simulation considers a 10% interval of volume variation in each phase, generating 726 data points. The proposed network, with two inputs—measured capacity and counted rays-and three outputs—volume fractions of gas, water, and oil—achieves mean absolute errors of 0.318, 1.531, and 1.614, respectively. These results demonstrate the system’s ability to accurately gauge volume proportions of a three-phase gas-water-oil fluid, regardless of pipeline scale thickness.

In light of fractional theory, this paper presents several new effective solitonic formulations for the Langmuir and ion sound wave equations. Prior to this study, no previous research has presented the comparision and obtained the generalized fractional soliton solutions of this kind with power law kernel and Mittag-Leffler kernel. The ion sound and Langmuir wave equations are essential in plasma physics, offering insights into the collective behavior of charged particles in plasmas and enabling diagnostics and control of these complex, ionized gas systems. The two distinct fractional order differential operators are substituted for the traditional order derivative to reshape the examined model. The Atangana-Baleanu non-singular and non-local operator and conformable fractional operator are the fractional-order operators that are used to create the fractional complex system equations for Langmuir waves and ion sound. A constructive approach new auxiliary equation method utilizes to obtain the exact analytical soliton solutions for ion sound and Langmuir wave equation. A wide range of soliton solutions is obtained, including mixed complex solitary shock solutions, singular solutions, mixed shock singular solutions, mixed trigonometric solutions, mixed singular solutions, exact solutions, mixed periodic solutions, and mixed hyperbolic solutions, dark soliton, bright soliton, trigonometric solutions, periodic results, and hyperbolic results. The solitons solution of the ion sound and Langmuir wave equations lies in their ability to maintain wave stability, their role in modeling wave propagation and nonlinear effects, their potential use as diagnostic tools, and their relevance in wave-particle interactions in plasma physics. The solitons provide a valuable framework for understanding the behavior of waves in plasmas and offer insights into the complex dynamics of these charged particle systems. A graphical comparison analysis of a few solutions is also shown here, taking into account appropriate parametric values through the use of the software package. Moreover, the results of this study have important implications for Hamilton's equations and generalized momentum, where solitons are employed in long-range interactions.

Abstract In recent years, there has been a growing interest in incorporating fractional calculus into stochastic delay systems due to its ability to model complex phenomena with uncertainties and memory effects. The fractional stochastic delay differential equations are conventional in modeling such complex dynamical systems around various applied fields. The present study addresses a novel spectral approach to demonstrate the stability behavior and numerical solution of the systems characterized by stochasticity along with fractional derivatives and time delay. By bridging the gap between fractional calculus, stochastic processes, and spectral analysis, this work contributes to the field of fractional dynamics and enriches the toolbox of analytical tools available for investigating the stability of systems with delays and uncertainties. To illustrate the practical implications and validate the theoretical findings of our approach, some numerical simulations are presented.

This article discusses the development and analysis of a broadband filter capable of suppressing specific frequencies of wireless applications at 4.7 GHz for WLAN, 8.3 GHz, and 11.5 GHz (for satellite communication) by combining a T-shaped stub loaded resonator with a central square ring to achieves high performance and compact size within the bandwidth of interest. The filter achieves low signal loss of 0.4 dB across the entire passband of 14.9 GHz bandwidth, good reflection suppression exceeding 10 dB, and independently adjustable multi stopbands. It utilizes a simple planar topology for a small footprint of 0.52 λg × 0.32 λg, where λg represents the wavelength at 4.7 GHz. The proposed filter evaluated using classical method an even-odd-mode analysis, and a 3D software tool HFSS-15 is used for simulations. The physical prototype is built on a Rogers RO-4350 substrate using LPKF S63 ProtoLaser machine. To confirm the accuracy and performance of the prototype, measurements were conducted using a ZNB20 vector network analyser. The simulations and measurement results confirm its effectiveness, paving the way for applications in various fields such as radar systems, medical imaging, and wireless communication.

Abstract The spatial movement of the human population from one region to another and the existence of super-spreaders are the main factors that enhanced the disease incidence. Super-spreaders refer to the individuals having transmitting ability to multiple pathogens. In this article, an epidemic model with spatial and temporal effects is formulated to analyze the impact of some preventing measures of COVID-19. The model is developed using six nonlinear partial differential equations. The infectious individuals are sub-divided into symptomatic, asymptomatic and super-spreader classes. In this study, we focused on the rigorous qualitative analysis of the reaction-diffusion model. The fundamental mathematical properties of the proposed COVID-19 epidemic model such as boundedness, positivity, and invariant region of the problem solution are derived, which ensure the validity of the proposed model. The model equilibria and its stability analysis for both local and global cases have been presented. The normalized sensitivity analysis of the model is carried out in order to observe the crucial factors in the transmission of infection. Furthermore, an efficient numerical scheme is applied to solve the proposed model and detailed simulation are performed. Based on the graphical observation, diffusion in the context of confined public gatherings is observed to significantly inhibit the spread of infection when compared to the absence of diffusion. This is especially important in scenarios where super-spreaders may play a major role in transmission. The impact of some non-pharmaceutical interventions are illustrated graphically with and without diffusion. We believe that the present investigation will be beneficial in understanding the complex dynamics and control of COVID-19 under various non-pharmaceutical interventions.

Cancer is a chronic disease, and recent cases of cancer cause a challenge for the whole world of medicine. Current studies have shown that T-cell antigens play a significant role in cancer treatment. Therefore, identifying tumor T cell antigens is vital for cancer detection and drug development. Several computational methods have been proposed to identify tumor T cell antigens. These models have produced promising results; however, they ignored the sequence’s physiochemical structure and correlation information during the feature transformation process. This study proposes a robust model considering sequence structure and correlation information for identifying tumor T-cell antigens. The proposed model uses three feature encoding (transformation) techniques and a multi-layer deep learning model. Additionally, the proposed model employed a feature fusion approach to consider sequence structure and correlation information during feature transformation. The fusion approach added redundant and noisy features in the feature vectors, which were removed during the feature selection phase. The performance of the proposed model is extensively evaluated by considering different measurement metrics using 10-fold cross-validation. The experimental results showed that the proposed model achieved 99.02% and 98.62% accuracies, improving an average of 14.66% and 23.75% using training and independent datasets, respectively.

Synthesis of chromophore (E)-2-(2-(2-(5-(2,5-bis(2-ethylhexyl)-3,6-dioxo-4-(thiophen-2-yl)-2,3,5,6 tetrahydropyrrolo [3,4-c] pyrrol-1-yl) thiophen-2-yl) vinyl)-4H-chromen-4 ylidene) malononitrile (DPP-CM-CN) has been designed and achieved in multistep reaction strategy. The experimental, photophysical and electrochemical properties along with theoretical calculations were explored. DPP-CM-CN dye was – chemically anchored on CdS nanowires (NWs) to investigate its influence on the dye-sensitized solar cell (DSSC) applications. Under the illumination of standard sunlight conditions, the photovoltaic performance of DPP-CM-CN based CdS nanowire device displayed a power conversion efficiency (PCE) of 0.38 %, which is 3.30 times higher as compared to a bare CdS nanowire (0.115 %) based DSSC device; ascribed to the dye loading time on CdS nanostructures. The present results suggest the potential ability of DPP-CM-CN to enhance DSSC device efficiency and reveal the importance of dye molecules in device fabrication, a key step towards the development of DSSC technology.

The impact of heat generation and activation energy on the Darcy-Forchheimer flow of magnetized fluid with nanoparticles in the condition of blood flowing arteries is being investigated by researchers from all over the world in an effort to understand the heat transmission component for the treatment of numerous diseases. The assessment of heat transmission has been made with the enclosure of influence like the viscous dissipation, heat source, Joules heating, and thermal radiation. Utilized is a tetra hybrid nanofluid made up of silver (Ag), gold (Au), titanium oxide (TiO2), coper (Cu), and Blood as the base fluid. The main justification for the use of gold, copper, and silver nanoparticles as drug delivery nanomaterials is their potential for drug transport and imaging. The purpose of the presented model is to assess the performance of the well-known tetra hybrid nanofluid models developed by Yamada-Ota and Xue. The main PDE are transformed into ODE via similarity renovations, and these ODE are then numerically solved using the MATLAB built-in bvp4c scheme. The arteries' internal temperature rises as a result of thermal radiation and viscous dissipation. The velocity and thermal distributions show an increasing behavior with greater variations in the flow parameter. The Yamada-Ota model outperforms the Xue tetra hybrid nanofluid model in terms of heat transmission effectiveness.

Abstract The fractional stochastic delay differential equation (FSDDE) is a powerful mathematical tool for modeling complex systems that exhibit both fractional order dynamics and stochasticity with time delays. The purpose of this study is to explore the stability analysis of a system of FSDDEs. Our study emphasizes the interaction between fractional calculus, stochasticity, and time delays in understanding the stability of such systems. Analyzing the moments of the system’s solutions, we investigate stochasticity’s influence on FSDDS. The article provides practical insight into solving FSDDS efficiently using various numerical techniques. Additionally, this research focuses both on asymptotic as well as Lyapunov stability of FSDDS. The local stability conditions are clearly presented and also the effects of a fractional orders with delay on the stability properties are examine. Through a comprehensive test of a stability criteria, practical examples and numerical simulations we demonstrate the complexity and challenges concern with the analyzing FSDDEs.

Hepatitis B is one of the global health issues caused by the hepatitis B virus (HBV), producing 1.1 million deaths yearly. The acute and chronic phases of HBV are significant because worldwide, approximately 250 million people are infected by chronic hepatitis B. The chronic stage is a long-term, persistent infection that can cause liver damage and increase the risk of liver cancer. In the case of multiple phases of infection, a generalized saturated incidence rate model is more reasonable than a simply saturated incidence because it captures the complex dynamics of the different infection phases. In contrast, a simple saturated incidence rate model assumes a fixed shape for the incidence rate curve, which may not accurately reflect the dynamics of multiple infection phases. Considering HBV and its various phases, we constructed a model to present the dynamics and control strategies using the generalized saturated incidence. First, we proved that the model is well-posed. We then found the reproduction quantity and model equilibria to discuss the time dynamics of the model and investigate the conditions for stabilities. We also examined a control mechanism by introducing various controls to the model with the aim to increase the population of those recovered and minimize the infected people. We performed numerical experiments to check the biological significance and control implementation.

Abstract Periodontal disease is a significant burden for oral health, causing progressive and irreversible damage to the support structure of the tooth. This complex structure, the periodontium, is composed of interconnected soft and mineralised tissues, posing a challenge for regenerative approaches. Materials combining silicon and lithium are widely studied in periodontal regeneration, as they stimulate bone repair via silicic acid release while providing regenerative stimuli through lithium activation of the Wnt/β-catenin pathway. Yet, existing materials for combined lithium and silicon release have limited control over ion release amounts and kinetics. Porous silicon can provide controlled silicic acid release, inducing osteogenesis to support bone regeneration. Prelithiation, a strategy developed for battery technology, can introduce large, controllable amounts of lithium within porous silicon, but yields a highly reactive material, unsuitable for biomedicine. This work debuts a strategy to lithiate porous silicon nanowires (LipSiNs) which generates a biocompatible and bioresorbable material. LipSiNs incorporate lithium to between 1% and 40% of silicon content, releasing lithium and silicic acid in a tailorable fashion from days to weeks. LipSiNs combine osteogenic, cementogenic and Wnt/β-catenin stimuli to regenerate bone, cementum and periodontal ligament fibres in a murine periodontal defect.

Task scheduling is a crucial challenge in cloud computing paradigm as variety of tasks with different runtime processing capacities generated from various heterogeneous devices are coming up to cloud application console which effects system performance in terms of makespan, resource utilization, resource cost. Therefore, traditional scheduling algorithms may not adapt to this paradigm efficiently. Many existing authors developed various task schedulers by using metaheuristic approaches to solve Task scheduling problem(TSP) to get near optimal solutions but still TSP is a highly dynamic challenging scenario as it is a NP hard problem. To tackle this challenge, this paper introduces a multi objective prioritized task scheduler using improved asynchronous advantage actor critic(a3c) algorithm which uses priorities of tasks based on length of tasks, runtime processing capacities and priorities of VMs based on electricity unit cost using multi cloud environment. Scheduling process carried out in two stages. In the first stage, all incoming tasks, VM priorities are calculated at the task manager level and in the second stage, Priorities are fed to (MOPTSA3C) scheduler to generate scheduling decisions to map tasks effectively onto VMs by considering priorities and schedule tasks based on cost, resource utilization, makespan in the available multi cloud environment. Extensive simulations are conducted on Cloudsim toolkit by giving input trace different fabricated data distributions and real time worklogs of HPC2N, NASA datasets to the scheduler. For evaluating the efficacy of proposed MOPTSA3C, it compared against existing techniques i.e. DQN, A2C, MOABCQ. From the results, it is evident that proposed MOPTSA3C outperforms existing algorithms for makespan, resource utilization, resource cost, reliability.

Task Scheduling is a crucial challenge in cloud computing as diversified tasks come rapidly onto cloud console dynamically from heterogeneous resources which consists of different task lengths, processing capacities. Generating schedules for these type of tasks is a challenge for Cloud Service Provider(CSP). Therefore, to generate task schedules in cloud paradigm effectively by considering type of task arising to cloud console and match it with respective Virtual Machine (VM), a task scheduler is formulated by using Deep Deterministic Policy Gradient (DDPG) algorithm which is used as methodology to design scheduler. This scheduler works in three stages. In the initial stage, tasks are classified based on length and processing capacity to identify them whether they are High Performance Computing (HPC) tasks or High Throughput Computing (HTC) tasks. After classification, in the second stage, resources are to be tracked which matches the corresponding nature of tasks. Finally, in the third stage, according to the VM priorities calculated based on electricity unit cost and tasks are mapped according to the priorities to the corresponding VMs. Simulations are conducted using Cloudsim with fabricated workload distributions and realtime worklogs. Finally, our proposed Hybrid workload Deep Deterministic Policy Gradient Task scheduler(HDDPGTS) evaluated over DQN, A2C algorithms. From results, it proved that our proposed HDDPGTS significantly improved makespan, Energy consumption, scheduling overhead, scalability over baseline approaches.

Chinese dialogue generation faces multiple challenges, such as semantic understanding, information matching, and response fluency. Generative dialogue systems for Chinese conversation are somehow difficult to construct because of the flexible word order, the great impact of word replacement on semantics, and the complex implicit context. Existing methods still have limitations in addressing these issues. To tackle these problems, this paper proposes an improved Chinese dialogue generation model based on transformer architecture. The model uses a multi-layer transformer decoder as the backbone and introduces two key techniques, namely incorporating pre-trained language model word embeddings and optimizing the sparse Softmax loss function. For word-embedding fusion, we concatenate the word vectors from the pre-trained model with character-based embeddings to enhance the semantic information of word representations. The sparse Softmax optimization effectively mitigates the overfitting issue by introducing a sparsity regularization term. Experimental results on the Chinese short text conversation (STC) dataset demonstrate that our proposed model significantly outperforms the baseline models on automatic evaluation metrics, such as BLEU and Distinct, with an average improvement of 3.5 percentage points. Human evaluations also validate the superiority of our model in generating fluent and relevant responses. This work provides new insights and solutions for building more intelligent and human-like Chinese dialogue systems.

Expanding projects to reclaim marginal land is the most effective way to reduce land use pressures in densely populated areas, such as Egypt’s Nile Valley and Delta; however, this requires careful, sustainable land use planning. This study assessed the agricultural potential of the El-Dabaa area in the northern region of the Western Desert, Egypt. It focused on assessing land capability, evaluating crop suitability, mapping soil variability, and calculating crop water requirements for twenty different crops. In this work, we evaluated land capability using the modified Storie index model and assessed soil suitability using the land use suitability evaluation tool (LUSET). We also calculated crop water requirements (CWRs) utilizing the FAO-CROPWAT 8.0 model. Additionally, we employed ArcGIS 10.8 to create spatial variability maps of soil properties, land capability classes, and suitability classes. Using a systematic sampling grid, 100 soil profiles were excavated to represent the spatial variability of the soil in the study area, and the physicochemical parameters of the soil samples were analyzed. The results indicated that the study area is primarily characterized by flat to gently sloping surfaces with deep soils. Furthermore, there are no restrictions on soil salinity or alkalinity, no sodicity hazards, and low CaCO3 levels. On the other hand, the soils in the study area are coarse textured and have low levels of CEC and organic matter (OM), which are the major soil limiting factors. As a result, the land with fair capability (Grade 3) accounted for the vast majority of the study area (87.3%), covering 30599.4 ha. Land with poor capability (Grade 4) accounted for 6.5% of the total area, while non-agricultural land (Grade 5) accounted for less than 1%. These findings revealed that S2 and S3 are the dominant soil suitability classes for all the studied crops, indicating moderate and marginal soil suitabilities. Furthermore, there were only a few soil proportions classified as unsuitable (N class) for fruit crops, maize, and groundnuts. Among the crops studied, barley, wheat, sorghum, alfalfa, olives, citrus, potatoes, onions, tomatoes, sunflowers, safflowers, and soybeans are the most suitable for cultivation in the study area. The reference evapotranspiration (ETo) varied between 2.6 and 5.9 mm day−1, with higher rates observed in the summer months and lower rates in the winter months. Therefore, the increase in summer ETo rates and the decrease in winter ones result in higher CWRs during the summer season and lower ones during the winter season. The CWRs for the crops we studied ranged from 183.9 to 1644.8 mm season−1. These research findings suggest that the study area is suitable for cultivating a variety of crops. Crop production in the study area can be improved by adding organic matter to the soil, choosing drought-resistant crop varieties, employing effective irrigation systems, and implementing proper management practices. This study also provides valuable information for land managers to identify physical constraints and management needs for sustainable crop production. Furthermore, it offers valuable insights to aid investors, farmers, and governments in making informed decisions for agricultural development in the study region and similar arid and semiarid regions worldwide.

The performance of the differential evolution algorithm (DE) is known to be highly sensitive to the values assigned to its control parameters. While numerous studies of the DE control parameters do exist, these studies have limitations, particularly in the context of setting the population size regardless of problem-specific characteristics. Moreover, the complex interrelationships between DE control parameters are frequently overlooked. This paper addresses these limitations by critically analyzing the existing guidelines for setting the population size in DE and assessing their efficacy for problems of various modalities. Moreover, the relative importance and interrelationship between DE control parameters using the functional analysis of variance (fANOVA) approach are investigated. The empirical analysis uses thirty problems of varying complexities from the IEEE Congress on Evolutionary Computation (CEC) 2014 benchmark suite. The results suggest that the conventional one-size-fits-all guidelines for setting DE population size possess the possibility of overestimating initial population sizes. The analysis further explores how varying population sizes impact DE performance across different fitness landscapes, highlighting important interactions between population size and other DE control parameters. This research lays the groundwork for subsequent research on thoughtful selection of optimal population sizes for DE algorithms, facilitating the development of more efficient adaptive DE strategies.

Abstract In this article, we introduce a novel Bayesian Max-EWMA control chart under various loss functions to concurrently monitor the mean and variance of a normally distributed process. The Bayesian Max-EWMA control chart exhibit strong overall performance in detecting shifts in both mean and dispersion across various magnitudes. To evaluate the performance of the proposed control chart, we employ Monte Carlo simulation methods to compute their run length characteristics. We conduct an extensive comparative analysis, contrasting the run length performance of our proposed charts with that of existing ones. Our findings highlight the heightened sensitivity of Bayesian Max-EWMA control chart to shifts of diverse magnitudes. Finally, to illustrate the efficacy of our Bayesian Max-EWMA control chart using various loss functions, we present a practical case study involving the hard-bake process in semiconductor manufacturing. Our results underscore the superior performance of the Bayesian Max-EWMA control chart in detecting out-of-control signals.

Abstract Quality control often employs memory-type control charts, including the exponentially weighted moving average (EWMA) and Shewhart control charts, to identify shifts in the location parameter of a process. This article pioneers a new Bayesian Adaptive EWMA (AEWMA) control chart, built on diverse loss functions (LFs) such as the square error loss function (SELF) and the Linex loss function (LLF). The proposed chart aims to enhance the process of identifying small to moderate as well as significant shifts in the mean, signifying a notable advancement in the field of quality control. These are implemented utilizing an informative prior for both posterior and posterior predictive distributions, employing various paired ranked set sampling (PRSS) schemes. The effectiveness of the suggested chart is appraised using average run length (ARL) and the standard deviation of run length (SDRL). Monte Carlo simulations are employed to contrast the recommended approach against other control charts. The outcomes demonstrate the dignitary performance of the recommended chart in identifying out-of-control signals, especially applying PRSS designs, in comparison to simple random sampling (SRS). Finally, a practical application was conducted in the semiconductor manufacturing context to appraise the efficacy of the offered chart using various paired ranked set sampling strategies. The results reveal that the suggested control chart performed well in capturing the out-of-control signals far better than the already in use control charts. Overall, this study interposes a new technique with diverse LFs and PRSS designs, improving the precision and effectiveness in detecting process mean shifts, thereby contributing to advancements in quality control and process monitoring.

Abstract An efficient finite difference approach is adopted to analyze the solution of a novel fractional-order mathematical model to control the co-circulation of double strains of dengue and COVID-19. The model is primarily built on a non-integer Caputo fractional derivative. The famous fixed-point theorem developed by Banach is employed to ensure that the solution of the formulated model exists and is ultimately unique. The model is examined for stability around the infection-free equilibrium point analysis, and it was observed that it is stable (asymptotically) when the maximum reproduction number is strictly below unity. Furthermore, global stability analysis of the disease-present equilibrium is conducted via the direct Lyapunov method. The non-standard finite difference (NSFD) approach is adopted to solve the formulated model. Furthermore, numerical experiments on the model reveal that the trajectories of the infected compartments converge to the disease-present equilibrium when the basic reproduction number ( $${\mathbb {R}}_0$$ R 0 ) is greater than one and disease-free equilibrium when the basic reproduction number is less than one respectively. This convergence is independent of the fractional orders and assumed initial conditions. The paper equally emphasized the outcome of altering the fractional orders, infection and recovery rates on the disease patterns. Similarly, we also remarked the importance of some key control measures to curtail the co-spread of double strains of dengue and COVID-19.

Abstract This paper aims to analyze the coupled nonlinear fractional Drinfel’d-Sokolov-Wilson (FDSW) model with beta derivative. The nonlinear FDSW equation plays an important role in describing dispersive water wave structures in mathematical physics and engineering, which is used to describe nonlinear surface gravity waves propagating over horizontal sea bed. We have applied the travelling wave transformation that converts the FDSW model to nonlinear ordinary differential equations. After that, we applied the generalized rational exponential function method (GERFM). Diverse types of soliton solution structures in the form of singular bright, periodic, dark, bell-shaped and trigonometric functions are attained via the proposed method. By selecting a suitable parametric value, the 3D, 2D and contour plots for some solutions are also displayed to visualize their nature in a better way. The modulation instability for the model is also discussed. The results show that the presented method is simple and powerful to get a novel soliton solution for nonlinear PDEs.

Potentially toxic elements (PTEs) in sediment can be highly hazardous to the environment and public health. This study aimed to assess the human and ecological risks of PTEs in sediments around a pharmaceutical industry in Ilorin, Nigeria. Physicochemical parameters and the concentrations of lead (Pb), chromium (Cr), cadmium (Cd), cobalt (Co), arsenic (As), and nickel (Ni) were analyzed in sediment samples collected from seven locations in the wet and dry seasons. Standard two-dimensional principal component analysis (PCA) and risk assessments were also conducted. The concentrations of Pb, Co, Ni, Cr, Cd, and As in the sediments ranged from 0.001 to 0.031 mg/kg, 0–0.005 mg/kg, 0.005–0.012 mg/kg, 0.001–0.014 mg/kg, 0.005–0.024 mg/kg, and 0.001–0.012 mg/kg, respectively. The mean concentrations of the total PTEs content were found in decreasing order of concentration: Pb > Cd > Ni > Cr > As > Co. PCA showed that some of the PTEs were highly concentrated in samples obtained at other locations as well as at the discharge point. The Hazard Index was mostly

The Featured Creatures collection provides in-depth profiles of insects, nematodes, arachnids and other organisms relevant to Florida. These profiles are intended for the use of interested laypersons with some knowledge of biology as well as academic audiences.

The use of natural ester oils as electrically insulating fluids has gained significant attention from industries and electrical utilities as they aim to replace traditional mineral oils. However, most natural ester oils are derived from edible products, which has the potential to contribute to the food crisis. Therefore, nonedible green nanofluids made from cottonseed oil (CSO) have been targeted as a keen solution to this issue. However, Al2O3, TiO2, Fe2O3, SiO2, and graphene nanoparticles at (0.025, 0.05, and 0.075 wt/vol%) were used as additives, along with surfactant Olic Ac-id and Ethanol (1:5) due to their promising impact on the dielectric and thermal properties of the nanofluid. The nanofluid synthesis process was practically conducted in HV & Chemical Laboratories using one-step and two-step methods, and their breakdown voltage results and chemical properties (e.g., fire point, flash point, cloud point, pour point, viscosity, acidity, moisture content, resistivity, and dissipation factor) were compared. The physical mechanisms underlying these properties were also analyzed and tested. For the validation of the proposed vegetable oil the results have been compared with traditional mineral oil for high-voltage equipment's. The findings suggest that the proposed nonedible green nanofluids-based cottonseed oil (CSO) has a high potential to be used as electrically insulating fluids, providing a sustainable alternative to conventional mineral oils. Overall, this study provides insights into the use of non-edible green nanofluids as a solution to the potential contribution of natural ester oils to the food crisis. The findings highlight the importance of sustainable solutions in the energy industry and the need for further research in this area.

Abstract The extended exponential weighted moving average (EEWMA) statistic is a memory type statistic that uses past observations along with the current information for the estimation of a population parameter to improve the efficiency of the estimators. This study utilized the EEWMA statistic to estimate the population mean with a suitable auxiliary variable. The ratio and product estimators are proposed for the surveys that are time-based by using current information along with that information. The approximate mean square errors are computed for the proposed memory type estimators and mathematical comparison is discussed to demonstrate the efficiency of the estimator. The simulation study was carried out to evaluate the performance of the proposed memory type estimators. It can be seen from the results that the efficiency of the estimator enhances by utilizing the current sample as well as past information. A real-life example is presented to illustrate the usage of proposed estimators.

Asking direct questions in face to face surveys about sensitive traits is an intricate issue. One of the solutions to this issue is the randomized response technique (RRT). Being the most widely used indirect questioning technique to obtain truthful data on sensitive traits in survey sampling RRT has been applied in a variety of fields including behavioral science, socio-economic, psychological, epidemiology, biomedical, criminology, data masking, public health engineering, conservation studies, ecological studies and many others. This paper aims at exploring the methods to subsidize the randomized response technique through additional information relevant to the parameter of interest. Specifically, we plan to contribute by proposing more efficient hybrid estimators compared to existing estimator based on (Kuk, 1990) [31] family of randomized response models. The proposed estimators are based on the methodology of incorporating the pertinent information, available on the basis of either historical records or expert opinion. Specifically, in case of availability of auxiliary information, the regression-cum-ratio estimator is found to be the best to further enhance the estimation through (Kuk, 1990) [31] model while the (Thompson, 1968) [49] shrinkage estimation is observed to be yielding more precise and accurate estimator of sensitive proportion. The findings in this study signify the importance of the proposed methodology. Additionally, to support the mathematical findings, a detailed numerical investigation to evaluate the comparative performances is also conducted. Based on performance analysis, overwhelming evidences are witnessed in the favor of proposed strategies.

Recent widespread connections of renewable energy resource (RESs) in place of fossil fuel supplies and the adoption of electrical vehicles in place of gasoline-powered vehicles have given birth to a number of new concerns. The control architecture of linked power networks now faces an increasingly pressing challenge: tie-line power fluctuations and reducing frequency deviations. Because of their nature and dependence on external circumstances, RESs are analogous to continually fluctuating power generators. Using a fractional order-based frequency regulator, this work presents a new method for improving the frequency regulation in a two-area interconnected power system. In order to deal with the frequency regulation difficulties of the hybrid system integrated with RES, the suggested controller utilizes the modified form of fractional order proportional integral derivative (FOPID) controller known as FOI-PDN controller. The new proposed controllers are designed using the white shark optimizer (WSO), a current powerful bioinspired meta heuristic algorithm which has been motivated by the learning abilities of white sharks when actively hunting in the environment. The suggested FOI-PDN controller's performance was compared to that of various control methodologies such as FOPID, and PID. Furthermore, the WSO findings are compared to those of other techniques such as the salp swarm algorithm, sine cosine algorithm and fitness dependent optimizer. The recommended controller and design approach have been tested and validated at different loading conditions and different circumstances, as well as their robustness against system parameter suspicions. The simulation outcomes demonstrate that the WSO-based tuned FOI-PDN controller successfully reduces peak overshoot by 73.33%, 91.03%, and 77.21% for region-2, region-1, and link power variation respectively, and delivers minimum undershoot of 89.12%, 83.11%, and 78.10% for both regions and tie-line. The obtained findings demonstrate the new proposed controller's stable function and frequency controlling performance with optimal controller parameters and without the requirement for a sophisticated design process.

Within the context of this article, we undertake a comprehensive exploration of the paraxial wave dynamical model, a modified version of Schrödinger’s equation. This equation is a well-known nonlinear partial differential equation that has garnered significant attention in modeling of several essential phenomena specifically, wave dynamics in the optical fibers. In a unified framework, our innovative method harmoniously integrates simplicity and strength, enabling us to obtain a diverse set of exact solutions. Through the implementation of this approach, our research unveils fresh perspectives on the characteristics of the model, broadening the scope of knowledge beyond previous works. Among the examined frameworks explored in this paper for predetermined structures, the utilization of the Jacobi elliptic functions is prominent. These functions assume a crucial role in solving nonlinear wave equations and analyzing soliton solutions, particularly within the realm of optics, where they find various applications. To enhance the understanding of the obtained results, we present graphical representations that illustrate the dynamic characteristics of the solutions. These visualizations demonstrate the effectiveness of our proposed method. Furthermore, the practical implications of our research extend beyond its theoretical contributions. Our findings have applications in various fields such as fluid mechanics, nonlinear optics, and plasma physics. Additionally, our approach facilitates the identification of soliton solutions for various other partial differential equations. The utilization of Mathematica software has been employed for computational purposes as well as for generating graphical representations.

This research is based on the analysis of Monkeypox transmission, from may 7, 2022 to October 11, 2022, in 30 most affected countries around the globe. The most affected countries are selected through the cut point of at least 100 reported confirmed cases of infected persons over the aforementioned time span. We novely argue the exhibition of distributional similarities between the viral flow and well known power law in context of this neglected zoonotic disease. Moreover, model-based evidence suggesting the capability of pathogen to spread far and wide around its nucleus, are collected and presented. It is estimated that 70 % of the reported confirmed cases belonged to 20 % of the top most affected countries. Also, 70 % of the reported transmission was inflicted in 34 % of the days of reporting at least one case, on average.

Fractional calculus and fractal theory remain significant tools in modeling complex real-world problems in biology and life science. In this study, we formulated a compartmental mathematical model using the Caputo fractional and fractal–fractional operators to study the dynamics and transmission of Nipah virus infection. Initially, the model is developed by a system of seven nonlinear ordinary differential equations that govern the dynamics of viral concentration, the flying fox, and the human populations. Furthermore, the model is restructured using more general modeling approaches based on fractional calculus and fractal theory to gain valuable insights into the dynamics of Nipah virus transmission. The necessary properties of the model, such as uniqueness and existence in both cases, were investigated, and possible equilibrium points with their existence were presented. The model parameters are estimated on the basis of the clinical epidemiology of the Nipah outbreak in Bangladesh, one of the most affected regions. The stability of the fractional model is studied by applying the Ulam–Hyers and Ulam–Hyers–Rassias stability conditions. Moreover, computational schemes for the model in fractional and fractal–fractional cases are developed using interpolation techniques. Finally, a detailed simulation was presented to validate the theoretical findings. We affirm that the present findings will help researchers incorporate advanced computational techniques in infectious disease modeling and control studies.

Abstract The memory-type control charts, such as cumulative sum (CUSUM) and exponentially weighted moving average control chart, are more desirable for detecting a small or moderate shift in the production process of a location parameter. In this article, a novel Bayesian adaptive EWMA (AEWMA) control chat utilizing ranked set sampling (RSS) designs is proposed under two different loss functions, i.e., square error loss function (SELF) and linex loss function (LLF), and with informative prior distribution to monitor the mean shift of the normally distributed process. The extensive Monte Carlo simulation method is used to check the performance of the suggested Bayesian-AEWMA control chart using RSS schemes. The effectiveness of the proposed AEWMA control chart is evaluated through the average run length (ARL) and standard deviation of run length (SDRL). The results indicate that the proposed Bayesian control chart applying RSS schemes is more sensitive in detecting mean shifts than the existing Bayesian AEWAM control chart based on simple random sampling (SRS). Finally, to demonstrate the effectiveness of the proposed Bayesian-AEWMA control chart under different RSS schemes, we present a numerical example involving the hard-bake process in semiconductor fabrication. Our results show that the Bayesian-AEWMA control chart using RSS schemes outperforms the EWMA and AEWMA control charts utilizing the Bayesian approach under simple random sampling in detecting out-of-control signals.

This research fundamentally aims at providing a generalized framework to assist the launch of paired comparison models while dealing with discrete binary choices. The purpose is served by exploiting the fundaments of the exponential family of distributions. The proposed generalization is proved to cater to seven paired comparison models as members of this newly developed mechanism. The legitimacy of the devised scheme is demonstrated through rigorous simulation-based investigation as well as keenly persuaded empirical evaluations. A detailed analysis, covering a wide range of parametric settings, through the launch of Gibbs Sampler—a notable extension of Markov Chain Monte Carlo methods, is conducted under the Bayesian paradigm. The outcomes of this research substantiate the legitimacy of the devised general structure by not only successfully retaining the preference ordering but also by staying consistent with the established theoretical framework of comparative models.

In the current global trend, financial organizations strive extensively towards smartening finance to derive the benefits of digitalization. In such an effort, Financial Technology (Fintech) involves usage of several contemporary disruptive technologies such as AI, 5G/6G, Blockchain, Metaverse, IoT, etc., in financial industry to add value to the customer services. By the inception of technology, several major financial services and processes such as lending, verification, fraud detection, quality maintenance, credit scoring, and many more will be simplified and augmented. However, there is a need for research and innovation of disruptive financial products and the enabling ecosystem of technologies. Consequently, several tech-giants have focused their attention on Fintech to introduce Information and Communication Technology (ICT) solutions. In this manuscript, firstly, we envision the future trends and driving applications of Fintech that would emerge during 2030. Further, we attempt to provide a high-level framework of the enablers of Fintech including IoT, 5G, Digital twins and Metaverse for certain use cases. In addition, we provide directions for future Fintech research while anticipating the challenges ahead.

With the evaluation of cellular network internet data traffic, forecasting and understanding traffic patterns become the critical objectives for managing the network-designed Quality of Service (QoS) benchmark. For this purpose, cellular network planners often use different methodologies for predicting data traffic. However, traditional traffic forecasting approaches are erroneous. As well as most of the time, traditional traffic forecasts are high-level or a generously large regional cluster level. Also, eNodeB-level utilization with concerning traffic forecasting is not readily available. As a result, user experience degradation or unnecessary network expansion is triggered based on the traditional method. This research deals with extensive 6.2 million real network time series Long-Term Evolution (LTE) data traffic and other associate parameters, including eNodeB-wise Physical Resource Block (PRB) utilization, which focuses on building a traffic forecasting model with the help of multivariate feature inputs and deep learning algorithms. A state-of-the-art Deep Learning algorithm-based fusion model is proposed. The combination of different deep learning algorithms, namely Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM) and Gated Recurrent Unit (GRU), enables traffic forecasting at a granular eNodeB-level and also provides eNodeB-wise forecasted PRB utilization. In this research $R^{2}$ score value for the proposed fusion model is 0.8034, which outperforms traditional models. Apart from the PRB utilization, QoS threshold was devised as 70% from a real network experience to trigger soft parameter tuning decisions. Based on the forecasted PRB utilization, this research proposed a unique algorithm that estimates eNodeB-level soft capacity parameter optimization for a short-term step-up solution or long-term network expansion to ensure a guaranteed QoS benchmark.

Energy Harvesting technology contributes significantly to green cellular networking by ensuring self-sustainability and extinguishing environmental hazards. Due to the imbalance between the harvested energy and traffic load of the base stations (BSs), energy cooperation has become a crucial requirement. However, the decision of optimal energy cooperation among the BSs in a multi-operator cellular network is a challenging task due to the consideration of various factors, such as cost, loss of energy, future information of traffic load, and harvested energy of the BSs, etc. The two conflicting objectives are minimizing the energy buying cost and the loss of energy while transferring through the power links. In this work, we present an optimal energy cooperation framework, formulated as a multi-objective linear programming (MOLP) problem which brings a trade-off between the two above-mentioned conflicting objectives considering the harvested energy and load of the BSs at future time slots. For the prediction of harvested energy of the BSs, we develop a Deep Q-Learning-based prediction method that intelligently increases measurement accuracy through continuous exploration and exploitation. The results of simulation experiments carried out in MATLAB depict that the proposed multi-operator energy cooperation framework outperforms state-of-the-art works in terms of cost, performance, and energy-loss reduction.

Refactoring is one of the most widely used techniques in practice to improve the quality of existing software. However, it is observed that refactoring does not continually improve all software quality attributes. Recent studies indicated that different refactoring techniques have significantly different, sometimes opposite, and conflicting effects on software quality attributes. In other words, there is contradictory evidence on the refactoring benefit. As a result, developers face challenges in selecting appropriate refactoring techniques when they use them to improve software quality. To the best of our knowledge, no study has investigated factors that may explain inconsistent or diverging results concerning the effect of refactoring techniques on software quality. Therefore, in this study, scenarios of using refactoring techniques factor have been identified, investigated, and thoroughly analyzed. Ten of the most commonly used refactoring techniques in practice have been chosen and individually applied in seven case studies of varying sizes (small, medium, and large). The Quality Model for Object-Oriented Design (QMOOD) is used to assess how refactoring techniques affect quality attributes. The findings provide strong evidence that this factor plays a significant role in producing the various effects of refactoring techniques on quality attributes. These findings can help software developers understand how to use refactoring techniques to improve software quality while taking this factor into account. The best scenario for using each refactoring technique to improve software system quality has been identified. The findings can provide guidelines for software developers to use refactoring techniques to improve the quality of software systems based on the best scenarios of using the refactoring techniques.

Speech enhancement (SE) aims to improve the intelligibility and perceptual quality of speech contaminated by noise signals through spectral or temporal changes. Deep learning models achieve speech enhancement and estimate the magnitude spectrum. This paper proposes a novel and computationally efficient deep learning model to enhance noisy speech. The model pre-processes the noisy speech magnitude by redistributing energy from high-energy voiced segments to low-energy unvoiced segments using an adaptive power law transformation while maintaining the total energy of the speech signals constant. A U-shaped fuzzy long short-term memory (UFLSTM) estimates the magnitude of a time-frequency (T-F) mask by using the pre-processed data. Residual connections to the similar-shaped layers are added to avoid gradient decay. Attention process is adopted by modifying the forget gate of UFLSTM. To make a causal speech enhancement system, the processing does not include any future audio frames. We compare the proposed speech enhancement to other deep learning models in different noisy environments with signal-to-noise ratios of 0 dB, 5 dB, and 10 dB. The experiments show that the proposed SE system outscores the competing deep learning models and considerably improves speech intelligibility and quality. In terms of STOI and PESQ, the LibriSpeech database improves results by (0.211) 21.1% and (0.95) 36.39%, respectively, over noisy speech in seen noisy conditions, and by (0.199) 19.9% and (0.94) 35.69% over noisy speech in unseen noisy conditions. Further, the cross-corpus analysis shows that proposed SE system performs better when trained with the DNS dataset as compared to the LibriSpeech, VoiceBank, and TIMIT datasets.

It is crucial to have a dependable and precise channel model in order to study the properties of millimeter wave (mmWave) propagation. The Quasi-deterministic (QD) channel model is employed in this viewpoint, which describes the propagation of mm-Wave as a group of reflected and scattered rays originating from a complex environmental setup. These rays are assumed to travel in clusters, with each cluster consisting of a deterministic ray followed by postcursor rays and preceded by precursor rays. The summation of these rays is the number of multiple path components (MPCs) of each cluster. However, this comes at the cost of higher computational complexity for the channel model, which can hinder the simulation’s scalability. To simplify the QD channel model while maintaining accuracy, one option is to decrease the number of MPCs. In this paper, we present an analysis of path gains (PGs) of specular and diffused rays to reduce the total number of MPCs. Specifically, we propose two different reduction methods namely: i) the reduced post rays (RPR) method ii) the removed surfaces post rays (RSPR) method. The computational performance of the proposed methods is investigated in terms of computational time, and complexity. Additionally, the accuracy validation compared to the original QD model is evaluated in terms of PG cumulative distribution function (CDF), signal-to-noise ratio (SNR), and intra-cluster statistics. The proposed methods’ complexity and accuracy were assessed by examining measured data from indoor and outdoor environments at 60 GHz and 28 GHz, respectively. Both first and second-order reflection orders were tested to illustrate the balance between the two variables. The simplified methods suggested can decrease computational time by approximately 16% and 11% for RSPR and RPR schemes, respectively, when compared to the original QD.