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Abstract Background Posterior reversible encephalopathy syndrome is a rare neurological syndrome that refers to reversible subcortical vasogenic brain edema disorder in patients with acute neurological symptoms. Case presentation Whether there is a direct causal relationship between pancreatitis and posterior reversible encephalopathy syndrome needs further study. We here report a 39-year-old Chinese woman who was diagnosed with pancreatitis followed by vision disturbance. The patient was finally diagnosed with posterior reversible encephalopathy syndrome. On the basis of this rare case, we analyzed the causes of visual disturbance and proposed diagnostic ideas. Conclusions For posterior reversible encephalopathy syndrome, early identification and treatment of the primary disease are particularly important. Imaging and clinical characteristics in posterior reversible encephalopathy syndrome are usually reversible.

Abstract Suspensions containing microencapsulated phase change materials (MPCMs) play a crucial role in thermal energy storage (TES) systems and have applications in building materials, textiles, and cooling systems. This study focuses on accurately predicting the dynamic viscosity, a critical thermophysical property, of suspensions containing MPCMs and MXene particles using Gaussian process regression (GPR). Twelve hyperparameters (HPs) of GPR are analyzed separately and classified into three groups based on their importance. Three metaheuristic algorithms, namely genetic algorithm (GA), particle swarm optimization (PSO), and marine predators algorithm (MPA), are employed to optimize HPs. Optimizing the four most significant hyperparameters (covariance function, basis function, standardization, and sigma) within the first group using any of the three metaheuristic algorithms resulted in excellent outcomes. All algorithms achieved a reasonable R-value (0.9983), demonstrating their effectiveness in this context. The second group explored the impact of including additional, moderate-significant HPs, such as the fit method, predict method and optimizer. While the resulting models showed some improvement over the first group, the PSO-based model within this group exhibited the most noteworthy enhancement, achieving a higher R-value (0.99834). Finally, the third group was analyzed to examine the potential interactions between all twelve HPs. This comprehensive approach, employing the GA, yielded an optimized GPR model with the highest level of target compliance, reflected by an impressive R-value of 0.999224. The developed models are a cost-effective and efficient solution to reduce laboratory costs for various systems, from TES to thermal management.

This study investigated the impact of sweeteners on the release of heavy metals during the heating and atomization processes in electronic cigarettes. Based on a PG/VG base e-liquid with the addition of 2% and 5% neotame or sucralose, we quantitatively analyzed the impact of sweetener content on the levels of heavy metals such as Ni, Cr, and Fe in the e-liquid and aerosol after heating and atomization. Additionally, the heated e-liquid samples were used to culture SH-SY-5Y and Beas-2B cells, and their cytotoxic effects were assessed using the CCK-8 assay. The results indicated that the e-liquid with 5% sucralose had the highest average levels of heavy metals after heating and atomization, particularly nickel (13.36 ± 2.50 mg/kg in the e-liquid and 12,109 ± 3,229 ng/200 puffs in the aerosol), whereas the e-liquid with neotame had significantly lower average heavy metal content in comparison. Additionally, it was measured that the chloride ion concentration in the e-liquid with 5% sucralose reached 191 mg/kg after heating at 200°C for 1 h, indicating that heating sucralose generated chloride ions, Which might corrode metal parts components leading to heavy metal release. Cytotoxicity tests revealed that the base e-liquid without sweeteners exhibited the highest average cell viability after heating, at 64.80% ± 2.84% in SH-SY-5Y cells and 63.24% ± 0.86% in Beas-2B cells. Conversely, the e-liquid variant with 5% sucralose showed a significant reduction in average cell viability, reducing it to 50.74% ± 0.88% in SH-SY-5Y cells and 53.03% ± 0.76% in Beas-2B cells, highlighting its more pronounced cytotoxic effects compared to other tested e-liquids. In conclusion, sucralose in e-liquids should be limited preferably less than 2%, or replaced with neotame, a safer alternative, to minimize health risks.

There are limited treatment options for recurrent advanced esophageal squamous cell carcinoma. A good response with a possible abscopal effect was observed in a patient with programmed death-ligand 1 (PD-L1)-negative recurrent advanced esophageal squamous cell carcinoma treated with an anti-PD-1 monoclonal antibody plus stereotactic body radiotherapy (SBRT). A 66-year-old male patient was diagnosed with recurrent advanced esophageal squamous cell carcinoma with multiple lung metastases (13 metastatic nodules in total) four months after completing radical radiotherapy plus concurrent and consolidated chemotherapy, and PD-L1 expression in the primary esophageal tumor was negative. This patient received 25 cycles of camrelizumab (an anti-PD-1 monoclonal antibody) in total plus upfront SBRT for two metastatic nodules, which was administered after the first cycle of camrelizumab. After this combined treatment, for most nontarget nodules, an obvious volume decrease and fuzzy change were observed, including two nodules that completely vanished. At the end of follow-up, the progression-free survival and duration of response of this patient were 34 months and 32 months, respectively. This case report indicated that an anti-PD-1 monoclonal antibody combined with SBRT was a promising therapeutic strategy for recurrent esophageal squamous cell carcinoma even in patients with negative PD-L1 expression.

The fumigant has been approved by the World Health Organization for use in grain storage, targeting the eradication of pests in areas where grain is stored or transported to reduce insect and mold activities, thereby minimizing storage losses. Phosphine is featured by its economic viability, application readibility, rapid diffusion, and lack of residue, making it advantageous for comprehensive pest control. It demonstrates high efficacy against various harmful organisms in storage, making it one of major fumigants worldwide. However, the elimination of some fumigants has increased dependence on phosphine. The lack of standardized guidance and inappropriate use of phosphine have led to severe resistance among stored grain pests, posing a threat to the effectiveness and sustainability of phosphine treatment. This paper provides a comprehensive review of the current status and trends of phosphine resistance in stored grain pests on the global. It analyzes the genetic basis of resistance, focusing on respiratory and cuticle-related gene expression and detoxification enzyme mechanisms. A comparison of four resistance detection methods—FAO, discriminating does bioassay, rapid knockdown, and molecular identification—is rendered. The study explores key strategies for resistance management, including optimization of fumigation protocols, laboratory and field validations, and application recommendations. The aim is to offer reference information for the scientific and rational use of phosphine and the development of alternative pest control methods in the future.

In the context of modern power systems, the reliance on a single-time-of-use electricity pricing model presents challenges in managing electric vehicle (EV) charging in a way that can effectively accommodate the variable supply and demand patterns, particularly in the presence of wind power generation. This often results in undesirable peak–valley differences in microgrid load profiles. To address this challenge, this paper introduces an innovative approach that combines time-of-use electricity pricing with the flexible energy storage capabilities of electric vehicles. By dynamically adjusting the time-of-use electricity prices and implementing a tiered carbon pricing system, this paper presents a comprehensive strategy for formulating optimized charging and discharging plans that leverage the inherent flexibility of electric vehicles. This approach aims to mitigate the fluctuations in the microgrid load and enhance the overall grid stability. The proposed strategy was simulated and compared with the no-incentive and single-incentive strategies. The results indicate that the load peak-to-trough difference was reduced by 30.1% and 18.6%, respectively, verifying its effectiveness and superiority. Additionally, the increase in user income and the reduction in carbon emissions verify the need for the development of EVs in tandem with clean energy for environmental benefits.

Abstract To lower operational costs as well as emissions when wind and solar resources are available in a microgrid (MG), this study discusses the scheduling of electric vehicles (EVs) and responsive demands simultaneously. To mitigate the effects associated with undispatchable energy sources such as wind and solar, the proposed system makes use of EVs for peak shaving and load curve changes, while responsive demands provide the reserves required to do so. In addition, a two‐stage model is provided to evaluate MG's planned running costs (energy and reserve). Costs related to generating and reserving electricity are minimized in Stage 1, while costs related to adjusting unit scheduling to account for fluctuations in wind and photovoltaic output are minimized in Stage 2. Converged barnacles mating optimizer (CBMO) is a highly effective and powerful optimization tool that is used to handle the resultant objective optimization issue. An MG consisting of multiple dispersed generations is used to implement the proposed model. It is worth mentioning that three scenarios have been defined to analyze the impact of joint scheduling of EVs and controllable loads on the MG's day‐ahead operation. The three cost terms, that is, the generation cost, the reserve cost, and the startup cost of units in this scenario, are derived as $745.6913, $10.5278, and $6.35, respectively, remarkably less than the values reported in Scenarios 1 and 2. In Scenario 1, the CBMO algorithm yielded a lower MG operational cost than methods by a margin of 843.2 $/day. Costs per day of operation in Scenario 2 are derived to be $819.3 using the CBMO technique, whereas in Scenario 3, they are determined to be $743.1.

Abstract Load balancing is a serious problem in cloud computing that makes it challenging to ensure the proper functioning of services contiguous to the Quality of Service, performance assessment, and compliance to the service contract as demanded from cloud service providers (CSP) to organizations. The primary objective of load balancing is to map workloads to use computing resources that significantly improve performance. Load balancing in cloud computing falls under the class of concerns defined as "NP-hard" issues due to vast solution space. Therefore it requires more time to predict the best possible solution. Few techniques can perhaps generate an ideal solution under a polynomial period to fix these issues. In previous research, Metaheuristic based strategies have been confirmed to accomplish accurate solutions under a decent period for those kinds of issues. This paper provides a comparative analysis of various metaheuristic load balancing algorithms for cloud computing based on performance factors i.e., Makespan time, degree of imbalance, response time, data center processing time, flow time, and resource utilization. The simulation results show the performance of various Meta-heuristic Load balancing methods, based on performance factors. The Particle swarm optimization method performs better in improving makespan, flow time, throughput time, response time, and degree of imbalance.

Due to the well-known limitations of renewable energy resources, nuclear power is considered as another alternative for carbon-free electricity generation. A great deal of interest has been paid to developing gas-cooled reactors due to their efficient and cost effective electricity generation ability. Among these systems, the GT-MHR is a promising one which employs the Brayton closed cycle for power generation with an energy efficiency of around 47% and a waste heat of around 300 MWth. The present work aims at utilizing this waste heat to produce additional power (via an ORC) and inlet gas cooling of compressor via employing absorption chiller. In this respect a combined GT-MHR/ORC/ARC structure is designed and assessed thermodynamically and economically and is compared with the standalone GT-MHR performance. A sensitivity analysis is implemented to inspect the influences of design variables, and then optimizations are conducted based on cost and exergy. Results revealed better performance for combined system in comparison to the GT-MHR. Under the optimal working conditions obtained for cost optimal design case, the combined cycle yields 12.4% higher efficiency and 9.7% lower LCOE compered to GT-MHR power plant. Another interesting outcome is obtained, as under the optimal operation, the total levelized investment cost for the novel combined cycle (9065 $/h) is less than the GT-MHR system (9285 $/h). This is due to the larger optimal pressure ratio for compressor in combined cycle which causes less helium flow rate, as a result of which the components sizes and costs are decreased.

A hybrid machine learning method is proposed for wildfire susceptibility mapping. For modeling a geographical information system (GIS) database including 11 influencing factors and 262 fire locations from 2013 to 2018 is used for developing an integrated multivariate adaptive regression splines (MARS). The cat swarm optimization (CSO) algorithm tunes the parameters of the MARS in order to generate accurate susceptibility maps. From the Pearson correlation results, it is observed that land use, temperature, and slope angle have strong correlation with the fire severity. The results demonstrate that the prediction capability of the MARS-CSO model outperforms model tree, reduced error pruning tree and MARS. The resulting wildfire risk map using MARS-CSO reveals that 20% of the study areas is categorized in the very low wildfire risk class, whereas 40% is under the very high class of fire hazard.

Abstract Numerous users are experiencing unsafe communications due to the growth of big network mediums, where no node communication is detected in emergency scenarios. Many people find it difficult to communicate in emergency situations as a result of such communications. In this paper, a mobile cloud computing procedure is implemented in the suggested technique in order to prevent such circumstances, and to make the data transmission process more effective. An analytical framework that addresses five significant minimization and maximization objective functions is used to develop the projected model. Additionally, all mobile cloud computing nodes are designed with strong security, ensuring that all the resources are allocated appropriately. In order to isolate all the active functions, the analytical framework is coupled with a machine learning method known as Decision Tree. The suggested approach benefits society because all cloud nodes can extend their assistance in times of need at an affordable operating and maintenance cost. The efficacy of the proposed approach is tested in five scenarios, and the results of each scenario show that it is significantly more effective than current case studies on an average of 86%.

Abstract Background Nasal‐type extranodal natural killer (NK)/T cell lymphoma (ENKTL) is a rare and aggressive type of lymphoma. The optimal chemotherapy regimen for ENKTL has not yet been established. In this study, we compared the LVDP (L‐asparaginase, etoposide, dexamethasone, and cisplatin) and GLIDE (gemcitabine, L‐asparaginase, ifosfamide, dexamethasone, and etoposide) chemotherapy regimens for the treatment of ENKTL. Methods A total of 267 patients with newly diagnosed ENKTL were included in this retrospective study. Propensity score matching (PSM) was used to adjust for confounders between the LVDP and GLIDE groups. Treatment responses, survival outcomes, and toxicities between the two groups were compared before and after PSM. Results At the end of therapy, the objective response rate (ORR) and complete response (CR) were 83.5% and 62.2%, respectively, for all patients. The ORR and CR were 85.5% and 62.2% for the LVDP group compared with 79.3% and 62.2% for the GLIDE group, respectively, and no differences between the two groups were found (ORR, p = 0.212; CR, p = 0.996). With a median 71 months follow‐up, the 5‐year progression‐free survival (PFS) and overall survival (OS) rates were 64.3% and 68.5%, respectively. The 5‐year PFS and OS were 65.6% and 70.1% for the LVDP group compared with 61.6% and 64.6% for the GLIDE group, respectively (PFS, p = 0.478; OS, p = 0162). After PSM, no significant differences in short‐term efficacy (ORR, p = 0.696; CR, p = 0.264) or long‐term efficacy (PFS, p = 0.794; OS, p = 0.867) between the two groups were identified. However, treatment‐related toxicities were milder in the LVDP group compared to the GLIDE group, even after adjusting for confounders via PSM. Conclusion In conclusion, both LVDP and GLIDE regimens are effective for the treatment of ENKTL. However, the LVDP regimen is safer than the GLIDE regimen, with milder treatment‐related toxicities. Therefore, the LVDP regimen could be a preferable option for patients with ENKTL.

Abstract Cloud Mobile Ad-hoc Networks (Cloud-MANETs) is a framework that can access and deliver cloud services to MANET users through their smart devices. MANETs is a pool of self-organized mobile gadgets that can communicate with each other with no support from a central authority or infrastructure. The main advantage of MANETs is its ability to manage mobility while data communication between different users in the system occurs. In MANETs, clustering is an active technique used to manage mobile nodes. The security of MANETs is a key aspect for the fundamental functionality of the network. Addressing the security-related problems ensures that the confidentiality and integrity of the data transmission is secure. MANETs are highly prone to attacks because of their properties.In clustering schemes, the network is broken down to sub-networks called clusters. These clusters can have overlapping nodes or be disjointed. An enhanced node referred to asthe Cluster Head (CH) is chosen from each set to overseetasks related to routing. It decreases the member nodes’ overhead and improvesthe performance of the system. The relationship between the nodes and CH may vary randomly, leading to re-associations and re-clustering in a MANET that is clustered. An efficient and effective routing protocol is required to allow networking and to find the most suitable paths between the nodes. The networking must be spontaneous, infrastructure-less, and provide end-to-end interactions. The aim of routing is the provision of maximum network load distribution and robust networks. This study focused on the creation of a maximal route between a pair of nodes, and to ensure the appropriate and accurate delivery of the packet. The proposed solution ensured that routing can be carried out with the lowest bandwidth consumption. Compared to existing protocols, the proposed solution had a control overhead of 24, packet delivery ratio of 81, the lowest average end-to-end delay of 6, and an improved throughput of 80,000, thereby enhancing the output of the network. Our result shows that multipath routing enables the network to identify alternate paths connecting the destination and source. Routing is required to conserve energy and for optimum bandwidth utilization.

Abstract Broadcasting more educating and language-reviving contents are ways radio stations can help revitalize the use of the English language in the Hunan province of China. The challenges faced in communicating in English in Chinese radio stations are majorly caused by the lack of language professionals and linguists in the broadcast stations. The absence of these professionals is a major constraint to the development of the community. The broadcast media can help manage multilingualism through the introduction of new words which would give little or no room for lexicon dearth but would expand the language lexicon. Using the English language during broadcast reduces language dearth, and helps reach a much larger audience, even those not in China. Programmes anchored in English in places where the language is barely spoken enhances the vocabulary, comprehension and language vitality of the listeners. This study examined the impact of the English language used in radio broadcasting using a descriptive Big Data survey research design. The study’s population comprises of the inhabitants of the Hunan province in China, from which a sample of 50 broadcast staff and 150 regular inhabitants was drawn using a stratified random sampling technique. The instrument of data collection was a structured questionnaire with closed questions and a self-structured interview. The sample employed frequency distribution tables, percentages, and charts in the presentation and analysis of data. The results revealed that majority of the respondents in Hunan listened to radio broadcast indicating that the use of English language can have massive impact on the people. The study also found that majority of the respondents use their indigenous languages in their day-to-day activities as well as their schools with English being used majorly only in schools with only English-speaking students. The study recommends, amongst others, that the Broadcasting Corporation of China (BCC) review their policy on the allocated time of broadcast in English languages, and that more English language experts and linguists should be incorporated into the broadcast system.

BackgroundThe systemic immune-inflammation index (SII) is based on the neutrophil, platelet, and lymphocyte counts, and has been identified as a prognostic marker in multiple types of cancer. However, the potential value of the SII for predicting survival outcomes in patients with extranodal natural killer/T-cell lymphoma (ENKTCL) has not been investigated thus far.MethodThis study included 382 patients with ENKTCL treated with asparaginase-base regimens from 2021 to 2017 in West China Hospital (Chengdu, China). Clinical and demographic variables, as well as the prognostic value of the SII, were analyzed using Cox proportional hazards regression analysis.ResultsThe complete and objective response rates were 55.8% and 74.9%, respectively. Patients with high SII were associated with a lower rate of complete response, higher rate of B symptoms, and serum lactate dehydrogenase levels above or equal to the upper limits of normal (p < 0.01). Patients with low SII were linked to better overall survival and progression-free survival than those with high SII (p < 0.01). Patients with early-stage disease or prognostic model for natural killer lymphoma with Epstein–Barr virus, defined as the low-risk group, could be further stratified according to the SII (p < 0.01). Negative prognostic factors were determined using the Cox proportional hazards regression analysis, which identified four variables: Eastern Cooperative Oncology Group performance status score ≥2, Stage III/IV disease, positivity for Epstein–Barr virus DNA in plasma, and high SII. Predictive nomograms for the prediction of 3- and 5-year overall survival, as well as progression-free survival, were constructed based on those four variables. The nomograms demonstrated favorable discriminating power.ConclusionThe SII is a novel prognostic marker for ENKTCL, which may be used for the prediction of poorer survival in low-risk patients.

Abstract Cloud computing is an extremely important infrastructure used to perform tasks over processing units. Despite its numerous benefits, a cloud platform has several challenges preventing it from carrying out an efficient workflow submission. One of these is linked to task scheduling. An optimization problem related to this is the maximal determination of cloud computing scheduling criteria. Existing methods have been unable to find the quality of service (QoS) limits of users- like meeting the economic restrictions and reduction of the makespan. Of all these methods, the Heterogeneous Earliest Finish Time (HEFT) algorithm produces the maximum outcomes for scheduling tasks in a heterogeneous environment in a reduced time. Reviewed literature proves that HEFT is efficient in terms of execution time and quality of schedule. The HEFT algorithm makes use of average communication and computation costs as weights in the DAG. In some cases, however, the average cost of computation and selecting the first empty slot may not be enough for a good solution to be produced. In this paper, we propose different HEFT algorithm versions altered to produce improved results. In the first stage (rank generation), we execute several methodologies to calculate the ranks, and in the second stage, we alter how the empty slots are selected for the task scheduling. These alterations do not add any cost to the primary HEFT algorithm, and reduce the makespan of the virtual machines’ workflow submissions. Our findings suggest that the altered versions of the HEFT algorithm have a better performance than the basic HEFT algorithm regarding decreased schedule length of the workflow problems.

Accurate estimation of the longitudinal dispersion coefficient (LDC) is essential for modeling the pollution status in rivers. This research investigates the capabilities of machine-learning methods such as multi-layer perceptron (MLP), multi-layer perceptron trained with particle swarm optimization (MLP-PSO), multi-layer perceptron trained with Stochastic gradient descent deep learning (MLP-SGD) and different regressions including linear and non-linear regressions (LR and NLR) methods for determining the LDC of pollution in natural rivers and evaluates the accuracy of these methods in comparison with real measured data. Furthermore, the correlation coefficient (CC), root mean squared error (RMSE) and Willmott’s Index (WI) were implemented to evaluate the accuracies of the mentioned methods. Comparison of the results showed the superiority of the MLP-SGD model with CC of 0.923, RMSE of 281.4 and WI of 0.954, which indicates the undeniable accuracy and quality of the deep-learning model that can be used as a powerful model for LDC simulation. Also due to the acceptable performance of the PSO algorithm in the hybridization of the MLP model, the use of PSO algorithms is recommended to train machine-learning techniques for LDC estimation.

River streamflow is an essential hydrological parameters for optimal water resource management. This study investigates models used to estimate monthly time-series river streamflow data at two hydrological stations in the USA (Heise and Irwin on Snake River, Idaho). Five diverse types of machine learning (ML) model were tested, support vector machine-radial basis function (SVM-RBF), SVM-Polynomial (SVM-Poly), decision tree (DT), gradient boosting (GB), random forest (RF), and long short-term memory (LSTM). These were trained and tested alongside a conventional multiple linear regression (MLR). To improve the estimation and model performance, hybrid models were designed by coupling the models with wavelet theory (W). The models performance was assessed using root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), and Willmott’s index (WI). A side-by-side performance assessment of the stand-alone and hybrid models revealed that the coupled models exhibit better estimates of monthly river streamflow relative to the stand-alone ones. The statistical parameter values for the best model (W-LSTM4) during the test phase was RMSE = 36.533 m3/s, MAE = 26.912 m3/s, R2 = 0.947, NSE = 0.946, WI = 0.986 (Heise station), and RMSE = 33.378 m3/s, MAE = 24.562 m3/s, R2 = 0.952, NSE = 0.951, WI = 0.987 (Irwin station).

As part of city planning and urban studies, it is customary to use the phrase urban density to describe people and other human activity and development throughout a certain area of land. Urban regions in affluent nations are not only more densely populated (DPUA), but they also have a greater population density per unit of land area than rural areas do as well. In densely populated urban areas, public transportation is more plentiful, reducing travel times making them more efficient. Greater affluent residents in metropolitan areas can also benefit from modern high-rise structures, which offer more breathing room. An energy-efficient design and features provide great living standards and comfort while using little energy and releasing minimal carbon dioxide in this sort of housing. In the absence of or rare usage of conventional active and passive heating and cooling systems, Smart sensors and other IoT devices make up the Internet of Things (IoT). IoT presents cities with new potential for using data from sensors in their infrastructure to improve traffic flow and reduce pollution. The more crowded and congested a city is, the more dissatisfied its residents are. There are numerous negatives to urban life, including high living costs, congestion, pollution, crime, and traffic, yet the benefits exceed the risks. Clean up the air by reducing the amount of energy used and using alternate modes of transportation. Provide services like garbage disposal and housing through private–public partnerships. Urban planning should include planting trees and the maintenance of municipal green areas. There are many advantages to living in densely populated places, such as better healthcare, better amenities, and faster emergency response times. The innovative DPUA-IoT approach proposed in this study is only one example of these advantages. Rapid population expansion is harmful to achieving economic and social progress and sustainably managing the natural resource base. However, there is still a large disparity between the interests of the commercial and public sectors when it comes to reducing fertility.

Abstract In our everyday lives, we communicate with each other using several means and channels of communication, as communication is crucial in the lives of humans. Listening and speaking are the primary forms of communication. For listening and speaking, the human voice is indispensable. Voice communication is the simplest type of communication. The Automatic Speaker Verification (ASV) system verifies users with their voices. These systems are susceptible to voice spoofing attacks - logical and physical access attacks. Recently, there has been a notable development in the detection of these attacks. Attackers use enhanced gadgets to record users’ voices, replay them for the ASV system, and be granted access for harmful purposes. In this work, we propose a secure voice spoofing countermeasure to detect voice replay attacks. We enhanced the ASV system security by building a spoofing countermeasure dependent on the decomposed signals that consist of prominent information. We used two main features— the Gammatone Cepstral Coefficients and Mel-Frequency Cepstral Coefficients— for the audio representation. For the classification of the features, we used Bi-directional Long-Short Term Memory Network in the cloud, a deep learning classifier. We investigated numerous audio features and examined each feature’s capability to obtain the most vital details from the audio for it to be labelled genuine or a spoof speech. Furthermore, we use various machine learning algorithms to illustrate the superiority of our system compared to the traditional classifiers. The results of the experiments were classified according to the parameters of accuracy, precision rate, recall, F1-score, and Equal Error Rate (EER). The results were 97%, 100%, 90.19% and 94.84%, and 2.95%, respectively.

Abstract The quality assurance of higher education in China is an issue of vital international interest. To improve the international reputation of the nation’s universities, steps must be taken to ensure a sustained focus on the quality assurance within its ranks. This paper is primarily focused on the quality assurance models operational in Chinese universities, the Big data challenges and the legal framework backing them. The paper also discusses the implementation of the models, the extent to which they meet international standards, and how they adhere to prevailing laws. The degree of success in attaining and maintaining quality and evaluation of quality improvement opportunities are also discussed. Some of the solutions recommended in this study are the participation of more teachers and students in quality management, more emphasis of Higher Education Institution (HEI) quality assurance on self-regulation and a learning-oriented approach and conducting sessions to collect anonymous feedback from students to reward staff with best practices. Some of the Quality Assurance practices/models adopted in Chinese Universities are the Ministry of Education (MOE) reviews; the Academic Degree Committee oversight; Higher Education Evaluation Center (HEEC) overview, University self-evaluation according to HEEC Indicators, and the Webometric Ranking Model.

Abstract Cloud technology is not immune to bugs and issue tracking. A dedicated system is required that will extremely error prone and less cumbersome and must command a high degree of collaboration, flexibility of operations and smart decision making. One of the primary goals of software engineering is to provide high-quality software within a specified budget and period for cloud-based technology. However, defects found in Cloud-Based Bug Tracking software’s can result in quality reduction as well as delay in the delivery process. Therefore, software testing plays a vital role in ensuring the quality of software in the cloud, but software testing requires higher time and cost with the increase of complexity of user requirements. This issue is even cumbersome in the embedded software design. Early detection of defect-prone components in general and embedded software helps to recognize which components require higher attention during testing and thereby allocate the available resources effectively and efficiently. This research was motivated by the demand of minimizing the time and cost required for Cloud-Based Bug Tracking Software testing for both embedded and general-purpose software while ensuring the delivery of high-quality software products without any delays emanating from the cloud. Not withstanding that several machine learning techniques have been widely applied for building software defect prediction models in general, achieving higher prediction accuracy is still a challenging task. Thus, the primary aim of this research is to investigate how deep learning methods can be used for Cloud-Based Bug Tracking Software defect detection with a higher accuracy. The research conducted an experiment with four different configurations of Multi-Layer Perceptron neural network using five publicly available software defect datasets. Results of the experiments show that the best possible network configuration for software defect detection model using Multi-Layer Perceptron can be the prediction model with two hidden layers having 25 neurons in the first hidden layer and 5 neurons in the second hidden layer.

Abstract Blockchain is the latest boon in the world which handles mainly banking and finance. The blockchain is also used in the healthcare management system for effective maintenance of electronic health and medical records. The technology ensures security, privacy, and immutability. Federated Learning is a revolutionary learning technique in deep learning, which supports learning from the distributed environment. This work proposes a framework by integrating the blockchain and Federated Deep Learning in order to provide a tailored recommendation system. The work focuses on two modules of blockchain-based storage for electronic health records, where the blockchain uses a Hyperledger fabric and is capable of continuously monitoring and tracking the updates in the Electronic Health Records in the cloud server. In the second module, LightGBM and N-Gram models are used in the collaborative learning module to recommend a tailored treatment for the patient’s cloud-based database after analyzing the EHR. The work shows good accuracy. Several metrics like precision, recall, and F1 scores are measured showing its effective utilization in the cloud database security.

This work investigates the performance enhancement of a solar air heater using inclined impinging jets on the heater surface. Two main sets of arrangements are considered for the jet nozzles including the parallel and crossing orientation of nozzles in consecutive rows. The RNG k − ɛ model is employed to simulate the turbulent flow in the channel. The influence of different geometrical and operational parameters such as jet diameter ratio (Dj/Dh), inclination angle (α,β), stream wise pitch ratio (L/Dh), span wise pitch ratio (S/Dh), Re number and velocity ratio (Vr) on the hydraulic and thermal behavior of jet solar air heater was studied. Results showed that the proposed jet solar air heater improved the Nu number and thermohydraulic performance parameter compared to the conventional unimproved solar air heater. For the studied range of parameters, up to 4.26 times higher Nu number was obtained using the jet impingement compared to the smooth duct solar air heater. Moreover, it was shown that the amount of velocity ratio (Vr) is a significant factor when comparing the parallel and crossing orientations. Additionally, results indicated that the increment of stream wise and span wise pitch led to the increment of the Nu number while having a minor effect on the friction factor. For example, the increment of L/dh from 0.3 to 0.6 leads to the reduction of f/fs of case 1 and case 4 by 6.5% and 1.09%, respectively Also, the increment of the jet diameter first raises the thermohydraulic performance parameter, and then with a further increment of diameter, the amount of thermohydraulic performance parameter decreases.

Nowadays, under the pressure of numerous research publications, researchers increasingly pay attention to writing survey papers to track and understand one research topic they are interested in, and then begin to conduct more in-depth research. Until this moment, there are two types of survey papers: traditional review analysis and bibliometric statistical analysis. Compared with traditional review analysis, due to the analysis of various bibliometric information that can be quickly summarized to assess and predict one research field’s development, the bibliometric statistical analysis is progressively proposed. However, no research relied on the bibliometric approach to explore fuzzy inference system (FIS) -based classifiers. More importantly, since the current open-ended bibliometric analysis approaches have different assessment focuses, choosing a suitable approach is problematic. Therefore, based on the extraction, integration, and expansion of previous bibliometric analysis theories, this research proposes a new systematic and time-saving bibliometric statistical analysis approach. It is worth noting that the proposed approach eliminates the need to read the internal content of all publications. Two core parts (Publication Information and TOP 20 SETs) are generated by the proposed analysis approach. Among them, analyzing Author Keywords and TOP 20 SETs are unprecedented guiding features to assist researchers in exploring research topic. Significantly, this research relies on the proposed approach to explore FIS-based classifiers. Various assessments cover the bibliometric information of the entire related publications. In addition, these results may need to be considered to increase the citation rate of future publications.

Sustainable utilization of the freely available solar radiation as renewable energy source requires accurate predictive models to quantitatively evaluate future energy potentials. In this research, an evaluation of the preciseness of extreme learning machine (ELM) model as a fast and efficient framework for estimating global incident solar radiation (G) is undertaken. Daily meteorological datasets suitable for G estimation belongs to the northern parts of the Cheliff Basin in Northwest Algeria, is used to construct the estimation model. Cross-correlation functions are applied between the inputs and the target variable (i.e., G) where several climatological information's are used as the predictors for surface level G estimation. The most significant model inputs are determined in accordance with highest cross-correlations considering the covariance of the predictors with the G dataset. Subsequently, seven ELM models with unique neuronal architectures in terms of their input-hidden-output neurons are developed with appropriate input combinations. The prescribed ELM model's estimation performance over the testing phase is evaluated against multiple linear regressions (MLR), autoregressive integrated moving average (ARIMA) models and several well-established literature studies. This is done in accordance with several statistical score metrics. In quantitative terms, the root mean square error (RMSE) and mean absolute error (MAE) are dramatically lower for the optimal ELM model with RMSE and MAE = 3.28 and 2.32 Wm-2 compared to 4.24 and 3.24 Wm-2 (MLR) and 8.33 and 5.37 Wm-2 (ARIMA).

Multi-object tracking has been a key research subject in many computer vision applications. We propose a novel approach based on multi-agent deep reinforcement learning (MADRL) for multi-object tracking to solve the problems in the existing tracking methods, such as a varying number of targets, non-causal, and non-realtime. At first, we choose YOLO V3 to detect the objects included in each frame. Unsuitable candidates were screened out and the rest of detection results are regarded as multiple agents and forming a multi-agent system. Independent Q-Learners (IQL) is used to learn the agents' policy, in which, each agent treats other agents as part of the environment. Then, we conducted offline learning in the training and online learning during the tracking. Our experiments demonstrate that the use of MADRL achieves better performance than the other state-of-art methods in precision, accuracy, and robustness.

Nanotechnology has been extensively explored for the potential applications in developing materials for sustained drug release. In this study, a modified coaxial electrospinning, characterized by a detachable concentric spinneret, was developed for the fabrication of a new kind of hybrid structural nanomaterials. The hybrid nanofibers consisted of a drug-free thin layer of glycerol monostearate as the shell and a drug-loaded nanocomposite containing berberine hydrochloride and ethylcellulose as the core, which were confirmed by scanning electron microscopic and transmission electron microscopic results. X-ray diffraction analyses suggested that the drug berberine hydrochloride was converted into an amorphous state with the carrier ethylcellulose in the core section. Fourier transform infrared spectra suggested that all the components were compatible. In vitro dissolution tests verified that the structural hybrids can provide an improved drug sustained-release profile than monolithic medicated nanocomposites in terms of initial burst release, sustained-release time and tailing-off time. The mechanism involving the influences of sheath layer on the drug release behaviour was suggested. The reported protocols pave a new way for fabricating lipid–polymer-based functional nanostructures with accurate structure–property–performance relationship and are useful for developing a series of new functional nanomaterials. Keywords: Lipid–polymer hybrid, Core–shell nanostructure, Coaxial electrospinning, Lipid layer, Sustained release

The oxidation of veratryl alcohol can produce veratryl aldehyde or veratryl acid, which are industrially important chemicals widely used in the food and pharmaceutical industries. Gold nanoparticles (AuNPs) functionalized with graphene quantum dots (GQDs) have shown high activity and selectivity as a catalyst for veratryl alcohol oxidation. However, the AuNP/GQD catalyst has excellent dispersibility in aqueous solutions, which makes it difficult to recycle all the catalyst after reaction. In this work, AuNP/GQD catalysts were further immobilized onto a silicon dioxide support by different methods, including physical adsorption, one-pot synthesis, and hydrothermal synthesis to produce AuNP/GQD/SiO2 composites. Extensive characterization of the microstructure, crystallinity, chemical structure, and catalytic performance of the catalysts was performed to evaluate the processing methods. It was demonstrated that the obtained composites are suitable heterogeneous catalysts for the oxidation of veratryl alcohol.

The relationship between phase transformation and time of Ti5Mo5V3Al-Cr (Cr content is 1.0%,3.0%,5.0%,7.0% and 9.0%,mass fraction) alloys at 420,520,620℃was studied by in-situ thermal expansion and XRD method. According to the Johnson-Mehl-Avrami equation and experimental results,the kinetic parameters of Ti5Mo5V3Al-Cr alloys were obtained by fitting, and the dynamic equation of the system was established. Furthermore,based on the experimental results and theoretical calculation results, the time temperature transformation (TTT) contrast curve of Ti5Mo5V3Al-Cr alloys was plotted. The results show that the calculated results are in good agreement with the experimental results. Besides, 1Cr and 3Cr alloys have a fast phase change speed in the test temperature range of 420,520,620℃; 5Cr and 7Cr alloys have the fastest alpha phase transition velocity at the "nose temperature zone" of 550℃. The "nose temperature zone" of 9Cr alloy is not obvious, which means that the phase transition rate is slow.

Visual object tracking is a fundamental component in many computer vision applications. Extracting robust features of object is one of the most important steps in tracking. As trackers, only formulated on RGB data, are usually affected by occlusions, appearance, or illumination variations, we propose a novel RGB-D tracking method based on genetic feature learning in this paper. Our approach addresses feature learning as an optimization problem. As owning the advantage of parallel computing, genetic algorithm (GA) has fast speed of convergence and excellent global optimization performance. At the same time, unlike handcrafted feature and deep learning methods, GA can be employed to solve the problem of feature representation without prior knowledge, and it has no use for a large number of parameters to be learned. The candidate solution in RGB or depth modality is represented as an encoding of an image in GA, and genetic feature is learned through population initialization, fitness evaluation, selection, crossover, and mutation. The proposed RGB-D tracker is evaluated on popular benchmark dataset, and experimental results indicate that our method achieves higher accuracy and faster tracking speed.

Cultivation of students' higher-order thinking ability has become the main agenda of the education curriculum. The transfer of knowledge pertaining to higher-order thinking by teachers to the students can prepare the latter with the necessary attributes for the 21st century. The present study is aimed at exploring Malaysian secondary ESL (English as a Second Language) school teachers' self-assessment of, and perceptions on the higher-order thinking skills practices for teaching writing. Using a mixed-method research design, the validated 30-item five-point Likert scale questionnaire with an open-ended question was administered to a group of respondents consisting of 72 ESL teachers. The findings indicated that the overall mean score of ESL teachers' self-assessment of using higher-order thinking skills practices for teaching writing was at a high level. Meanwhile, their perceptions on the integration of higher-order thinking skills in the teaching of writing include the concern of students' low proficiency, difficulties in implementing HOTS in writing, poor participation by passive students, and teachers' attitude towards using HOTs for teaching writing. This study suggests that fostering and creating awareness of mastering the elements of HOTS can benefit both the teachers and the students. Teachers have to be creative and innovative in their teaching so that the students can be given the opportunity to showcase their knowledge, skills and abilities in the quest to imbue them with the 21st century life skills.

Riedel's thyroiditis is a rare type of chronic thyroiditis, associated with fibroinflammatory process and invasion into surrounding tissues, leading to compressive symptoms. A 45-year-old man had a left thyroid mass, presenting with hypotension and bradycardia many times. He was diagnosed with vasovagal reflex caused by cervical vessel compression due to a thyroid lesion. We performed the emergency operation, and most of the left thyroid was removed to relieve the compression on cervical vessels. The result of pathology proved to be Riedel's thyroiditis. The vasovagal reflex did not occur any more during the 28-month follow up, except on the 3rd day after the surgery. Six months after the thyroidectomy, the patient was found to have retroperitoneal fibrosis, diagnosed by biopsy during a laparotomy for biliary disease. Riedel's thyroiditis can lead to a vasovagal episode and might not be a primary thyroid disease but rather a manifestation of the systemic disorder, multifocal fibrosclerosis.

This paper proposes a novel rotating interferometer-based algorithm for parameter estimation of nearfield source. The algorithm exploits the rotation of a single long baseline interferometer and the integration operation of the phase to unwrap the phase ambiguity. Thus the contradiction between the maximal unambiguous angle and the direction finding accuracy in the single baseline interferometer can be efficiently eliminated in the near-field source scenario. The algorithm can obtain the closed form solutions of the elevation and azimuth angles as well as range estimations for near-field source with two receiving sensors. It avoids the construction of high-order cumulant matrices and multi-dimension search. Simultaneously, it alleviates the requirment for multi-baseline channel consistency. Compared with the conventional double long baselines interferometer method, the proposed algorithm achieves higher parameter estimation accuracy and better performance in ambiguity resolution. Moreover, the proposed algorithm enjoys a simple structure, which is easy to be implemented in engineering application. Simulation results demonstrate the effectiveness and validity of the proposed algorithm.

In order to improve adaptability, the image enlargement method was proposed using improved anisotropic complex diffusion and adaptive Shock filter. For the enlargement of the noiseless image, an improved anisotropic com-plex diffusion was coupled to shock filter, which adaptively changes the diffusion threshold according to local variance, dividing diffusion time by the diffusion parameter of the image's imaginary part to remove the diffusion character dependence on the diffusion time, especially the linear diffusion character at the beginning of the diffusion. The noisy image was enlarged by using the adaptive threshold anisotropic complex diffusion filters according to pixel's local variance coupling with shock filters constrained by pixel's local variance to enhance the blurred edges. The adaptive threshold anisotropic complex diffusion detects the edge and noise through the combination of local variance and smoothed second derivative of the image, and the proposed method enhances the edge and smoothed the noise, containing the advantage of complex diffusion, as well as preserving the ramp structure and avoiding the staircase effective. The simulations prove prominent performance of the proposed method is not only for noiseless image but also for the certain gauss noisy image.

The accurate prediction and manipulation of nanoscale product sizes is a major challenge in material processing. In this investigation, two process characteristics were explored during the modified coaxial electrospinning of zein, with the aim of understanding how this impacts the products formed. The characteristics studied were the spreading angle at the unstable region (θ) and the length of the straight fluid jet (L). An electrospinnable zein core solution was prepared and processed with a sheath comprising ethanolic solutions of LiCl. The width of the zein nanoribbons formed (W) was found to be more closely correlated with the spreading angle and straight fluid jet length than with the experimental parameters (the electrolyte concentrations and conductivity of the shell fluids). Linear equations W = 546.44L − 666.04 and W = 2255.3θ − 22.7 could be developed with correlation coefficients of Rwl2 = 0.9845 and Rwθ2 = 0.9924, respectively. These highly linear relationships reveal that the process characteristics can be very useful tools for both predicting the quality of the electrospun products, and manipulating their sizes for functional applications. This arises because any changes in the experimental parameters would have an influence on both the process characteristics and the solid products’ properties.

In view of problem that self-carried AD conversion module of DSP chip TMS320F2812 cannot meet requirement of collecting parameters of current and voltage synchronously, a data collection system with multi-channel based on AD7606 was designed. Design of voltage/current input circuit, input filtering circuit, interface circuit between AD7606 and TMS320F2812, AD conversion program of the system were introduced in details. Test result shows that AD conversion result by using AD7606 has high precision and small error compared with AD conversion result by using self-carried AD conversion module of TMS320F2812, and it is suitable for AD conversion circuit with high precision.

Liquid tank systems play important role in industrial application such as in food processing, beverage, dairy, filtration, effluent treatment, pharmaceutical industry, water purification system, industrial chemical processing and spray coating. A typical situation is one that requires fluid to be supplied to a tank at a constant rate which has Two-Input Two-Output (TITO). The accuracy and stability of TITO is one of the key factors of process which have cross coupling between process input and output. Unlike traditional neural network weight adaptation using gradient descent method, Fuzzy Adaptive PID control technique was utilized for adaptive tuning and fine tuning the controller’s parameters. Design approach for controlling liquid levels of Coupled Tank TITO system by using Fuzzy Adaptive PID controllers. Tuning method for parameters of improved Fuzzy Adaptive, Particle Swamp Optimization and Neural network controllers ware also discussed. Experimental results demonstrate that the performances of proposed method perform well compared with convention Fuzzy and Tuned Fuzzy controllers; it was shown that Fuzzy Adaptive controller exhibited better performance in terms of transient response analysis.

Recently, emerging evidence has suggested that carcinoma-associated fibroblasts (CAFs) could contribute to chemotherapy resistances in breast cancer treatment. The aim of this study is to compare the gene expression profiling of CAFs before and after chemotherapy and pick up candidate genes that might associate with chemotherapy resistance and could be used as predictors of treatment response. CAFs were cultured from surgically resected primary breast cancers and identified with immunohistochemistry (IHC) and Flow cytometry (FCM). MDA-MB-231 cells were cultured as the breast cancer cell line. Cell adhesion assay, invasion assay, and proliferation assay (MTT) were performed to compare the function of MDA-MB-231 cells co-cultured with CAFs and MDA-MB-231 cells without co-culture, after chemotherapy. Totally 6 pairs of CAFs were prepared for microarray analysis. Each pair of CAFs were obtained from the same patient and classified into two groups. One group was treated with Taxotere (regarded as after chemotherapy) while the other group was not processed with Taxotere (regarded as before chemotherapy). According to our study, the primary-cultured CAFs exhibited characteristic phenotype. After chemotherapy, MDA-MB-231 cells co-cultured with CAFs displayed increasing adhesion, invasiveness and proliferation abilities, compared with MDA-MB-231 cells without CAFs. Moreover, 35 differentially expressed genes (absolute fold change >2) were identified between CAFs after chemotherapy and before chemotherapy, including 17 up-regulated genes and 18 down-regulated genes. CXCL2, MMP1, IL8, RARRES1, FGF1, and CXCR7 were picked up as the candidate markers, of which the differential expression in CAFs before and after chemotherapy was confirmed. The results indicate the changes of gene expression in CAFs induced by Taxotere treatment and propose the candidate markers that possibly associate with chemotherapy resistance in breast cancer.