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Reverse circulation impact drilling has the advantages of high drilling efficiency and less dust, which can effectively form holes in hard rock and gravel layer. As integral reverse circulation drill bits used in the conventional down-the-hole (DTH) hammers are only suitable for specific formations, the whole set of DTH hammer needs to be replaced when drilling different formations. In this paper, several types of split drill bits for different drilling technologies are designed. The flow field characteristics of one of the split drill bits is analyzed based on the computational fluid dynamics (CFD) method, with four technic parameters considered, which are input flow rate, number of inlet holes, angle of injection exhaust holes, and diameter of injection exhaust holes, respectively. Three parameters are selected as indicators to evaluate the rationality and performance of the split drill bit, which are injection exhaust hole outlet mass flow rate, ratio of the mass flow rate out of injection exhaust holes to the whole inlet mass flow rate, and maximum pressure at the upper end of the split drill bit. According to the CFD analysis results, the above four technic parameters influence the flow rate and pressure in different rules. Considering the injection capacity, pressure loss, and bit strength, inlet holes of 10, injection exhaust holes with an angle of 50°, and injection exhaust holes with a diameter of 12 mm are recommended to obtain ideal reverse circulation. Different types of split drill bits were manufactured, and drilling experiments were carried out in unconsolidated formations. The maximum drilling rate can reach 1.5 m/min in the drilling experiments. The split drill bit proposed in this paper exhibits excellent adaptability for reverse circulation drilling in loose formations. [ABSTRACT FROM AUTHOR]

In the realm of structural health monitoring (SHM), understanding the expected behavior of a structure is vital for the timely identification of anomalous activities. Existing methods often model only the physical quantities of monitoring data, neglecting the corresponding temporal information. To address this, this paper presents an innovative deep learning framework that synergistically combines a BiLSTM model, fortified by a temporal pattern attention (TPA) mechanism, with time-encoded temperature and traffic-induced deflection-temporal patterns. The arithmetic optimization algorithm (AOA) is employed for optimal hyperparameter tuning, and incremental learning was implemented to enable real-time updates of the model. Based on the proposed framework, an anomaly detection method was subsequently developed. This method is bidirectional: it uses quantile loss to provide expected ranges for structural behavior, identifying isolated anomalies, while the windowed normalized mutual information (WNMI) based on multivariate kernel density estimation (MKDE) helps detect trend variability caused by decreases in structural stiffness. This framework and the anomaly detection method were validated using data from an operational cable-stayed bridge. The results demonstrate that the method effectively predicts structural behavior and detects anomalies, highlighting the critical role of temporal information in SHM. [ABSTRACT FROM AUTHOR]

In this paper, the vibration suppression and trajectory tracking control of a flexible joint manipulator (FJM) based on particle swarm optimization (PSO) and fixed-time nonsingular terminal sliding mode control (NTSMC) are studied. Firstly, in order to suppress the residual vibration of the FJM, an optimal trajectory planning method based on higher-order trajectory planning (HOTP) and the PSO algorithm is proposed. Then, to ensure that the FJM can track the optimized trajectory without being affected by the initial value of the trajectory, a novel fixed-time NTSMC scheme is proposed. Compared with the cubic spline trajectory, the proposed HOTP is smoother and can more accurately suppress the residual vibration of the FJM. By combining the HOTP with the PSO algorithm, the vibration amplitude of FJM can be suppressed to around 0.002 mm. Unlike finite-time NTSMC, the rate of convergence of the proposed fixed-time NTSMC does not depend on the initial value of FJM's joint trajectory. Especially when the initial value of the trajectory is large, the FJM can still quickly track the optimal trajectory within 0 to 1 s. Finally, the effectiveness of this method is verified through simulation and comparison. [ABSTRACT FROM AUTHOR]

The security of communication networks can be compromised through both known and novel attack methods. Protection against such attacks may be achieved through the use of an intrusion detection system (IDS), which can be designed by training machine learning models to detect cyberattacks. In this paper, the KOMIG (knapsack optimization and mutual information gain) IDS was developed to detect network intrusions. The KOMIG IDS combined the strengths of optimization and machine learning together to achieve a high intrusion detection performance. Specifically, KOMIG IDS comprises a 2-stage feature selection procedure; the first was accomplished with a knapsack optimization algorithm and the second with a mutual information gain filter. In particular, we developed an optimization model for the selection of the most important features from a network intrusion dataset. Then, a new set of features was synthesized from the selected features and combined with the selected features to form a candidate features set. Next, we applied an information gain filter to the candidate features set to prune out redundant features, leaving only the features that possess the maximum information gain, which were used to train machine learning models. The proposed KOMIG IDS was applied to the UNSW-NB15 dataset, which is a well-known network intrusion evaluation dataset, and the resulting data, after optimization operation, were used to train four machine learning models, namely, logistic regression (LR), random forest (RF), decision tree (DT), and K-nearest neighbors (KNN). Simulation experiments were conducted, and the results revealed that our proposed KNN-based KOMIG IDS outperformed comparative schemes by achieving an accuracy score of 97.14%, a recall score of 99.46%, a precision score of 95.53%, and an F1 score of 97.46%. [ABSTRACT FROM AUTHOR]

The different rail transit systems, such as regional railway and urban rail transit, cooperate to form the syncretic railway network (SRN). With the rapid development of SRN, the limited transportation capacity is inadequate to meet the booming passenger flow during peak hours, where cascading failures caused by large passenger flow become a threat to SRN. This paper adopts a state equation to depict the failure-restoration process and investigates detailed restoration strategies considering the characteristics of recoverable and repeatable failures of stations. Specifically, three different restoration strategies--spontaneous restoration strategy (SRS), active restoration strategy (ARS), and hybrid restoration strategy (HRS)--are proposed, and the varying effects of restoration time, restoration probability, restoration objective, and restoration priority for SRN with ARS are compared. These restoration strategies are applied to the actual SRN in Chengdu, where it is found that HRS has a better effect than other strategies. Furthermore, stations in the metro network with higher passenger flow allocate more restoration resources to improve the robustness of SRN, while the restoration effect of SRN increases noticeably with the restoration coefficient and the reserve coefficient. The restoration strategies presented in this paper can improve the safety management of SRN., Author(s): Qingwei Zhong [1]; Su Liu (corresponding author) [1,2]; Jingwei Guo [3]; Linfeng Zhong [1]; Zhihong Yao [2] 1. Introduction Due to the rapid increase in diversified passenger travel demand, [...]

This paper focuses on the differences in crowding perception among different types of passengers in trains, aiming to optimize passenger experience and improve the level of service of urban rail transit. Based on data from a passenger survey on the Beijing subway, this paper introduces the concept of Crowding Perception Threshold (CPT) and analyzes the principle of passenger spatiotemporal crowding. Considering factors such as gender, age, travel purpose, and standing density of passengers, the paper constructs a quantitative model of crowding perception using the ordered logit model and proposes a method for classifying the level of service accordingly. The study results indicate that the CPTs for all types of passengers range from 91.8% to 101.6%, with the females, elderly individuals, and noncommuters showing greater sensitivity to crowding. In the temporal dimension, all types of passengers have higher CPTs during peak hours than during off-peak hours, influenced by passengers' crowding expectations. In the spatial dimension, the level of service for most types of passengers is considered crowded at standing densities of 6-7pax/m[sup.2] during peak hours, while the level of service for all types of passengers is deemed to be very crowded at 8pax/m[sup.2], at which point additional passengers are not recommended., Author(s): Xia Lu [1]; Baohua Mao (corresponding author) [1]; Min Wang (corresponding author) [2,3]; Yixin Zhao [1]; Peining Tian [1] 1. Introduction In some cities in China, the issue of [...]

For the coordinated development of regional logistics, there is a need to break down the administrative barriers between cities and build an efficient and smooth regional logistics network. Integrating regional logistics resources to achieve cross-regional optimal allocation is essential for efficient logistics networks. The accessibility and high capacity of the railway transportation network make it possible and necessary for the coordinated construction of railway transportation and logistics networks. This paper studies a bilevel programming model to analyse the collaborative network planning process of railway transportation and logistics. The objective is to take into account the existing layout of regional railway traffic network and the future needs of regional logistics. For the upper level of the bilevel programming model, the social, economic, and logistics service index of regional railway traffic nodes is considered to determine the numbers and locations of radiation of the cities. Meanwhile, the lower programming model uses a logistics gravity model, 0-1 integer programming model, and logistics membership degree to calculate the logistics correlation between node cities and the radiation range of the hub cities. An empirical study of the Beijing-Tianjin-Hebei region in China shows the process and effectiveness of the bilevel programming model. Four distinctive logistics radiation circles, five trunk lines, nine hub-spoke branch lines, and six branch lines are suggested. A railway traffic-oriented regional logistics network provides a reference for regional railway traffic planning, thereby optimizing the allocation of regional logistics resources, improving the level of regional logistics services, and contributing to the development of the regional logistics industry., Author(s): Taoxing Zhu (corresponding author) [1]; Yuqing Xu [1,2]; Junyi Zhang [3]; Bukang Zhao [1] 1. Introduction Logistics is a phenomenon factor flow that supports regional sustainable development. Logistics channels [...]

Internet of Vehicles can improve driving and riding experience, provide information needs, reduce environmental pollution, and improve transportation efficiency, thereby promoting the rapid development and application of intelligent transportation. Especially through the advantages of the rapidity of information exchange and the flexibility of real-time data processing, an autonomous vehicle can provide barrier-free, safe, and sustainable transportation. At present, the rapid progress of the Internet of Things has promoted the continuous development of a fully autonomous vehicle. However, the autonomous vehicle network uses wireless communication technology, and the openness of its communication channel makes the communication process vulnerable to various security attacks. Therefore, this paper proposes a practical and efficient secure communication in the autonomous vehicle. This scheme utilizes elliptic curve cryptography, which can protect the security of multiparty communication between vehicle, cloud server, and user with lower computation and communication overhead. This paper provides security verification for the scheme by using Scyther. An informal security analysis shows that this scheme can resist multiple attacks. Through a comparative analysis and performance evaluation of the schemes, we found that the scheme improves security while maintaining efficiency., Author(s): Jiongen Xiao (corresponding author) [1]; Yi Liu (corresponding author) [1,2]; Yi Zou [3]; Dacheng Li [4,5]; Tao Leng [6,7] 1. Introduction The global road traffic safety situation is severe, [...]

A second-order numerical method for two-sided tempered fractional convection-diffusion equations is studied in this paper, both convection term and diffusion term are approximated by the tempered weighted and shifted Grünwald difference operators, the first time partial derivative is discretized by the Crank–Nicolson method, and then a class of second-order numerical schemes is derived. By means of matrix method, numerical schemes are proved to be unconditionally stable and convergent with order O τ 2 + h 2 . The validity of the proposed numerical scheme is verified by numerical experiments. [ABSTRACT FROM AUTHOR]

The Korean Demilitarized Zone (DMZ) is one of the world's most preserved habitats for wild animals and migratory birds. The area also plays a major role in the spread of infectious animal diseases, in particular, African swine fever (ASF) and highly pathogenic avian influenza (HPAI). These outbreaks threaten the livelihood of local livestock farms, not infrequently. In this paper, we explore these relatively under-researched diseases by modeling and mapping ASF and HPAI risks in tandem using MaxEnt, a machine-learning algorithm. The results show robust predictive power with high area under the curve values, of 0.92 and 0.99, respectively. We found that precipitation from spring to early summer and solar radiation in winter were essential in explaining the potential distribution of ASF, but land use contributed little. Thus, understanding only wild boars' habitat preferences may not be sufficient in preventing ASF epidemics. HPAI risks were shaped by precipitation and mean temperature from winter to spring and land use. Areas with high ASF and HPAI risks were primarily found in forest and agricultural lands, respectively. The DMZ included many high-risk areas, indicating that the DMZ could lead to a broader regional spread of ASF and HPAI in the peninsula. Thus, our results highlight the essential role of cross-border collaboration and the combination of environmental and epidemiological insights in strategies to control ASF and HPAI risks within and surrounding the DMZ. [ABSTRACT FROM AUTHOR]

Coal-resource-based cities, as one of the important types of typical resource-based cities in China, have strong characteristics of instability, sensitivity, and fragility, exhibiting a series of vulnerability features of the human-land system. Previous studies have lacked a deeper investigation into the vulnerability of such cities from the perspective of coupling feedback mechanisms among various elements or subsystems. This paper adopts Bohle et al.'s "Dual Structure Model" of vulnerability to establish an indicator system for vulnerability warning in coal-resource-based cities. Using Fushun City as a sample, a dynamic simulation model of vulnerability in coal-resource-based cities is constructed based on system dynamics for warning simulation calculations, analyzing the evolution trend of vulnerability in Fushun City from 2010 to 2022. The results show the following: (1) from 2010 to 2022, the urban vulnerability of Fushun City transitioned from a medium warning level to a relatively safe level, showing a trend of annual reduction and improvement, undergoing three development stages of significant reduction, fluctuation stabilization, and slight rebound. (2) Due to the causal relationships among various elements within the vulnerability subsystems of Fushun City, differences exist in the contribution levels of vulnerability subsystems during the period 2010–2022, under the combined effects of sensitivity and adaptability factors. The contribution rate of the natural subsystem was the highest from 2010 to 2014, while, from 2016 to 2019, urban vulnerability began to shift towards an economically and socially dominant type. Influenced by the pandemic, Fushun City's overall vulnerability has shown an upward trend since 2020 compared to the previous stage. Finally, specific policy suggestions are proposed for Fushun City to cope with vulnerability changes based on the analysis results, providing a theoretical basis for achieving sustainable development in such coal-resource-based cities. [ABSTRACT FROM AUTHOR]

Nowadays, construction industries are using flyash as a potential alternative for cement. Due to its improved mechanical properties, ecofriendly nature, and low cost, geopolymer technology makes use of flyash as a promising future binder material. In this paper, 7 factors such as liquid-to-flyash ratio, silicate-to-hydroxide ratio, curing temperature, curing period, concentration of NaOH (molarity), rest period prior to curing, and dosage of superplasticizer that influence the compressive strength and temperature drop are screened using the Plackett–Burman method for optimization. Here, compressive strength and temperature drop are taken as the main indices of response to analyze the parameters. The significant variables determined from the Plackett–Burman design are further considered for the process of optimization using the response surface methodology. From the analysis, the optimum values of 0.4071 liquid-to-flyash ratio, 2.5 silicate-to-hydroxide ratio, and 6 hours curing period give maximum compressive strength and temperature drop of 28.87 MPa and 5.3°C under the optimized medium in the validation experiment which varies by only 3.93% and 1.85% from the observed value of 27.20 MPa and 5.4°C. [ABSTRACT FROM AUTHOR]

Mathematical models that represent food processing operations are characterized by the nonlinearity of their dynamic behavior with possible discrete events, the existence of several variables of interest that are usually distributed in space, and the presence of nonlinear constraints. These features require robust optimization methods to resolve these models and to identify the optimum operating conditions of the processes. Stochastic optimization methods, often referred as metaheuristics, are effective and reliable tools to perform the global and multiobjective optimization of process units and operations involved in food engineering. In this way, this paper surveys recent advances and contributions that have applied stochastic methods for solving global and multiobjective optimization problems in food engineering. The description of the most used stochastic algorithms in food engineering is provided including the application of those methods classified as random search techniques, evolutionary methods, and swarm intelligence methods. It was observed that evolutionary methods are the most applied in solving food engineering optimization problems where the genetic algorithm and differential evolution stand out. Finally, remarks on the limitations and current challenges to improving the numerical performance of stochastic optimization methods for food engineering applications are also discussed. [ABSTRACT FROM AUTHOR]

Suppose that S T 1 is a class of close-to-star functions. In this paper, we investigated the estimate of Zalcman functional on the logarithmic coefficients and the third Hankel determinant for the class S T 1 with the determinant entry of logarithmic coefficients. Also, we obtained the sharp bounds of Zalcman functional J 2 , 4 f and J 3 , 3 f for the class S T 1 . [ABSTRACT FROM AUTHOR]

In this paper, the impact of viral illnesses on the predator-prey relationship with an optimal control analysis is studied. An ecoepidemiological model of four compartments, namely, susceptible prey, susceptible predator, infected prey, and infected predator populations, in the interaction of the prey-predator system is formulated. The fundamental tenet of our ecoepidemiology model is that sick predators do not engage in predation. It is confirmed that the system's solution exists, is positive, and is bounded. The system's equilibrium points are determined and computed. Lyapunov functions and a linearizing form are used for local and global stability analysis, respectively. The next generation matrix approach is used to calculate the threshold value for diseased predators and prey at the disease-free equilibrium point. Optimal treatment options for vulnerable and infected populations are established by applying optimal control theory to the ecoepidemiology model of a prey-predator system. MATLAB software is utilized to obtain numerical simulations that validate the analytical outcomes. The optimal control problem simulations demonstrate that the number of infected populations in a given prey-predator system can be decreased by implementing control measures. [ABSTRACT FROM AUTHOR]

This paper introduces a bilevel programming model for optimizing transit network departure frequency. In the upper-level model, user satisfaction is reflected by considering congestion effects in the cost function. The lower-level assignment model simulates passenger travel behavior more realistically by incorporating congestion effects. This problem is solved by a heuristic gradient descent algorithm, where an approximation of the gradient is obtained at each iteration by using sensitivity analysis for transit equilibrium problems. The effectiveness of the proposed model and algorithm is demonstrated through two test examples, one of which involves a real-world scenario comprising over 130 transit lines. Numerical results conclusively indicate that the incorporation of congestion effects in the proposed model leads to improved transit system performance and enhanced user satisfaction. [ABSTRACT FROM AUTHOR]

Real-time train rescheduling for high-speed railway (HSR) is a pivotal technique in HSR transportation to efficiently recover train operation under disturbance scenarios. This paper aims to put forward an integrated resolving and rescheduling method considering network delay propagation. A tree-based conflict resolution mechanism is first established, with delicate considerations on the strategy's adaptability under different conflict scenarios. By inputting the scheduled arrival and departure time under different conflict resolution strategies, the timetable optimizing model aims to look for an optimal solution with minimal weighted train delay and average train adjustments under necessary technical and empirical constraints solved by a combined algorithm of Pareto optimality and Nash equilibrium, where the feasible solution space is narrowed in advance by a depth-first pruning algorithm. The performance of this coordinated train rescheduling approach is validated by a typical section disturbance in a regional HSR network administrated by the Shanghai Bureau. The results show that the proposed method can well utilizes timetable buffers and organizes train avoidance. The delay propagation characteristics are also simultaneously estimated based on the indicators of cumulative delay and instantaneous delay, which are established considering the spatio-temporal difference between the scheduled and planned timetables, in order to verify the coordination between resolution strategies and train running delays. [ABSTRACT FROM AUTHOR]

In this paper, we explore the concept of topologically dense injectivity of monoid acts. It is shown that topologically dense injective acts constitute a class strictly larger than the class of ordinary injective ones. We determine a number of acts satisfying topologically dense injectivity. Specifically, any strongly divisible as well as strongly torsion free S -act over a monoid S is topologically dense injective if and only if S is a left reversible monoid. Furthermore, we establish a counterpart of the Skornjakov criterion and also identify a class of acts satisfying the Baer criterion for topologically dense injectivity. Lastly, some homological classifications for monoids by means of this type of injectivity of monoid acts are also provided. [ABSTRACT FROM AUTHOR]

This paper is dedicated to the study of the geometric composition of a perfect fluid space-time with a conformal Ricci-Bourguignon soliton, which is the extended version of the soliton to the Ricci-Bourguignon flow. Here, we have delineated the conditions for conformal Ricci-Bourguignon soliton to be expanding, steady, or shrinking. We have studied certain curvature conditions on the spacetime that admit conformal Ricci-Bourguignon soliton. We have also discussed conformal Ricci-Bourguignon soliton on some special types of perfect fluid spacetime such as dust fluid, dark fluid, and radiation era. [ABSTRACT FROM AUTHOR]

In literature, several types of join operations of two graphs based on subdivision graph, Q -graph, R -graph, and total graph have been introduced, and their spectral properties have been studied. In this paper, we introduce a new double join operation based on H 1 , H 2 -merged subdivision graph. We compute the spectrum of a special block matrix and then use it to describe the distance spectra of some double join operations of graphs. At last, we give several families of distance equienergetic graphs of diameter 3. [ABSTRACT FROM AUTHOR]

A novel hybrid bandpass filter (BPF) with wideband and high selectivity is proposed in this paper. The hybrid BPF is composed of two film bulk acoustic resonator (FBAR) cells and three lumped component resonators realized by the integrated passive device (IPD) technology. The ABCD matrix to S-parameters matrix method is used to calculate the frequency response of the BPF. Moreover, the expressions of transmission zeros (TZs) have been extracted. In the design process, an iterative design approach is proposed to improve the circuit and layout of the hybrid filter based on the packaged acoustic-electric hybrid simulation effect. Finally, two parts of the filter are packaged based on the flip-chip method, and two prototypes for the BPF are measured. The measured results of two chips with 3 dB fractional bandwidth of 13.7% and 15.8% are designed and fabricated, which verifies the validity of the proposed design principle. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive review of the use of life cycle cost assessment (LCCA) and life cycle assessment (LCA) methods for evaluating the sustainability and costs of using recycled materials in asphalt pavement rehabilitation projects. The review begins with an introduction to pavement rehabilitation strategies and the importance of choosing techniques based on thorough engineering and economic analyses. It then explores the different types of recycled materials that can be utilized, including reclaimed asphalt pavement, recycled concrete aggregate, and recycled asphalt shingles, discussing the key characteristics and properties of these materials based on previous laboratory studies. The review also examines the various rehabilitation methods that employ recycled content, such as cold in-place recycling, hot in-place recycling, and full-depth reclamation, providing a detailed breakdown of the construction, maintenance, and rehabilitation costs considered in LCCA and analyzing the environmental benefits of recycled material usage through a review of LCA techniques and criteria like carbon footprint reduction, impacts on air and water quality, and considerations of technological factors. Software tools for conducting LCA are compared and challenges to advancing the adoption of recycled materials are reviewed along with directions for future research efforts. The unique contribution of this work is its holistic assessment of LCCA and LCA methodologies to inform the sustainable and cost-effective deployment of recycled materials in asphalt pavement rehabilitation, a topic of growing importance for transportation infrastructure management. In summary, this current work provides a valuable review of how LCCA and LCA methodologies can assess the sustainability and costs of employing recycled content in asphalt pavement rehabilitation projects. [ABSTRACT FROM AUTHOR]

Given the phenomenon of "financing is difficult and expensive" for MSEs, this paper empirically investigated the influencing mechanism of the credit demand side characteristics on the financing constraints of MSEs based on the information conveyance perspective. The conclusions show that MSEs in China are severely suffering from financing constraints and 57.17% and 50.00% of MSEs with credit demand have not applied for loans from formal and informal financing channels, respectively. In terms of enterprise characteristics, MSEs have low asset size, short establishment history, weak profitability, and lack of tools such as fixed assets, complete financial management system, professional technicians, and private brands to convey risk information to financing institutions, which are key factors resulting in their financing constraints. In terms of owner characteristics, young owners lack financing experience and convey higher risk information to financing institutions; therefore, owners' age negatively influences the financing constraints of MSEs. These findings suggest that banks can use big data credit technology as a tool to obtain risk information about MSEs, and the government should implement diversified interventions to improve the information environment in financial markets. These findings provide empirical evidence for banks and governments to address the financing constraints of MSEs. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the concept of weakly semiprime ideals and weakly n -systems in noncommutative rings. We establish the equivalence between an ideal P being a weakly semiprime ideal and R − P being a weakly n -system. We provide alternative definitions and explore the properties of weakly semiprime ideals. Additionally, we investigate scenarios where all ideals in a given ring are weakly semiprime and demonstrate that in Noetherian rings, where every ideal is weakly semiprime, the prime radical and the Jacobson radical coincide. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to examine the condition for a Finsler space with a generalized α , β -metric to be projectively flat. In addition, we establish that the Finsler space with generalized α , β -metric is a flat-parallel Minkowski space and derive the condition under which the β -change for the aforementioned metric is projective. We also explored the projective nature of β -change for various significant Finsler metrics derived from the generalized α , β -metric. [ABSTRACT FROM AUTHOR]

The misleading information during the coronavirus disease 2019 (COVID-19) pandemic's peak time is very sensitive and harmful in our community. Analyzing and detecting COVID-19 information on social media are a crucial task. Early detection of COVID-19 information is very helpful and minimizes the risk of psychological security which leads to inconvenience in daily life. In this paper, a deep ensemble transfer learning framework with an understanding of the context of Arabic text COVID-19 information is proposed. This framework is inspired to spontaneously analyze and recognize the text about COVID-19. The ArCOVID-19Vac dataset has been used to train and test our proposed model. A comprehensive experimental study for each scenario is performed. For the binary classification scenario, the proposed framework records better evaluation results with 83.0%, 84.0%, 83.0%, and 84.0% in terms of accuracy, precision, recall, and F1-score, respectively. For the second scenario (three classes), the overall performance is recorded with an accuracy of 82.0%, precision of 80.0%, recall of 82.0%, and F1-score of 80.0%, respectively. In the last scenario with ten classes, the best evaluation performance results are recorded with an accuracy of 67.0%, a precision of 58.0%, a recall of 67.0%, and F1-score of 59.0%, respectively. In addition, we have applied an ensemble transfer learning model for this scenario to get 64.0%, 66.0%, 66.0%, and 65.0% in terms of accuracy, precision, recall, and F1-score, respectively. The results show that the proposed model through transfer learning provides better results for Arabic text than all state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The integration of renewable energy sources into power systems has increased significantly in recent years. Among various types of renewable energy, the use of wind energy is growing rapidly due to its low operating cost, wide distribution worldwide, and no greenhouse gas emissions. However, power systems integrated with wind energy may face stability and reliability issues due to the intermittent nature of wind power. Therefore, in power systems connected to wind farms, it is usually required to use some compensators such as static synchronous series compensator (SSSC) to increase the system performance under abnormal conditions. On the other hand, for an SSSC to be effective in improving the system performance, it must be equipped with a suitable controller. In this paper, a fuzzy logic controller (FLC) is used for the SSSC because of its advantages over conventional controllers. Extensive research has been conducted in power systems with wind turbines in which SSSC or FLC has been used; however, their simultaneous application in such systems has received less attention. Therefore, this article aims to fill this gap. The proposed method is implemented on two power systems and the simulation results are analyzed. In both systems, the dynamic behavior of three different wind farms is examined. In the first and second wind farms, either a squirrel cage induction generator (SCIG) or doubly-fed induction generator (DFIG) are used, whereas in the third one which is a combined wind farm (CWF), an equal number of SCIG and DFIG are employed. In wind farms with SCIG or DFIG, an SSSC is also utilized. Furthermore, an FLC is employed for the SSSC to improve its efficacy. A proportional integral (PI) controller is also considered for the SSSC, and its results are compared with FLC results. The simulation results confirm the superiority of FLC over PI controller. [ABSTRACT FROM AUTHOR]

This paper investigates the threat assessment method and target assignment algorithm in multi-UAV cooperative air combat decision-making. To address the uncertainty and dynamic changes in multiple threat attributes and attribute information of UAV targets, we propose a UAV target dynamic threat assessment method based on intuitionistic fuzzy multiattribute decision-making. Firstly, we propose a mixed situation information representation method to represent interval-valued fuzzy data appropriately. Secondly, we employ the normal distribution weight assignment method to fuse the multi-time situation information. Then, by incorporating the analytic hierarchy process and entropy method, we determine the normalized threat value of the target considering both objective situation data characteristics and decision-maker preferences. Finally, a simulation example is provided to validate the rationality of our proposed algorithm. For solving the multi-weapon multi-target assignment problem, a target assignment method based on the VNS-IBPSO algorithm is introduced. This method improves upon the limitations of the BPSO algorithm, such as limited local search capability and premature convergence, by combining variable neighborhood search and an improved binary particle swarm optimization algorithm. Simulation results show that the proposed threat assessment method can obtain reasonable threat assessment results under complex dynamic environments. The proposed VNS-IBPSO algorithm can solve the target assignment model quickly and efficiently based on the assessment results, therefore ensuring that the UAV mission planning system makes the correct combat plan. [ABSTRACT FROM AUTHOR]

A capacitance multiplier is an active circuit designed specifically to increase the capacitance of a passive capacitor to a significantly higher capacitance level. In this paper, the use of a voltage differencing differential difference amplifier (VDDDA), an electronically controllable active device for designing grounded and floating capacitance multipliers, is proposed. The capacitance multipliers proposed in this study are extremely simple and consist of a VDDDA, a resistor, and a capacitor. The multiplication factor ( K c ) can be electronically controlled by adjusting the external bias current ( I B ). It offers an easy way of controlling it by utilizing a microcontroller for modern analog signal processing systems. The multiplication factor has the potential to be adjusted to a value that is either less than or greater than one, hence widening the variety of uses. The grounded capacitance multiplier can be easily transformed into a floating one by utilizing Zc-VDDDA. PSpice simulation and experimentation with a VDDDA realized from commercially available integrated circuits were used to test the performance of the proposed capacitance multipliers. The multiplication factor is electronically adjustable, ranging in approximation from 0.56 to 13.94. The operating frequency range is approximately three frequency decades. The realization of the lagging and leading phase shifters using the proposed capacitance multiplier is also examined and proven. The results reveal that the lagging and leading phase shifts are electronically tuned via the multiplication factor of the proposed capacitance multipliers. [ABSTRACT FROM AUTHOR]

The primary source of soil compaction is weight bearing down on the soil, which occurs frequently on agricultural land due to foot movement, livestock tramples, or the heavy weight of farm equipment. The primary cause of significant soil compaction is the driving of heavy machinery over moist soils. In this paper, the negative side of soil compaction and its influence on soil physical and chemical characteristics at the Awash Melkassa farm field located in Ethiopia were investigated. Compaction of soil test was taken at three different depths which are 10 cm, 20 cm, and 30 cm with the help of a hydraulically operated cone penetrometer integrated into the tractor. The three depths were expanded into 15 sample locations (designated as point A to point O) to collect 35 data points on soil compaction using a hydraulic-powered Spot-on digital soil cone penetrometer across a 0.6-hectare farmland area. A correlation analysis was conducted on the 15 sample points (A to O) to assess soil compaction in the field. In addition, soil samples were collected from specific farm locations at depths ranging from 0 to 10 cm, 10–20 cm, and 20–30 cm for subsequent physical and chemical tests in the laboratory. During the harvesting season, sample location A exhibited the highest soil compaction values, reaching 6,159 kPa, while the lowest values were observed at sample point F, measuring 327 kPa. For the seeding season, the sample point B showed the highest soil compaction values at 6,052 kPa, while the lowest values were recorded at sample point K, measuring 563 kPa. Moreover, the data indicates a consistent increase in soil compaction with depth during both experimental seasons. The laboratory test for soil texture revealed that the soil composition was classified as a clay loam, consisting of 36.7% sand, 30.3% clay, and 33% silt. The moisture content ranged from a high of 16.04% to a low of 13.97%. In addition, it was observed that total organic carbon, organic matter, and total nitrogen levels tend to rise with increased soil compaction, and conversely, decrease as soil compaction decreases. This study's insights are invaluable for agricultural mechanization. Given the substantial weight of farm machinery, comprehending soil properties and their correlation with compaction rates is imperative for optimizing farming practices. [ABSTRACT FROM AUTHOR]

This paper extends our previous method for COVID-19 diagnosis, proposing an enhanced solution for detecting COVID-19 from computed tomography (CT) images using a lean transfer learning-based model. To decrease model misclassifications, two key steps of image processing were employed. Firstly, the uppermost and lowermost slices were removed, preserving sixty percent of each patient's slices. Secondly, all slices underwent manual cropping to emphasize the lung areas. Subsequently, resized CT scans (224 × 224) were input into an Xception transfer learning model with a modified output. Both Xception's architecture and pretrained weights were leveraged in the method. A big and rigorously annotated database of CT images was used to verify the method. The number of patients/subjects in the dataset is more than 5000, and the number and shape of the slices in each CT scan varies greatly. Verification was made both on the validation partition and on the test partition of unseen images. Results on the COV19-CT database showcased not only improvement from our previous solution and the baseline but also comparable performance to the highest-achieving methods on the same dataset. Further validation studies could explore the scalability and adaptability of the developed methodologies across diverse healthcare settings and patient populations. Additionally, investigating the integration of advanced image processing techniques, such as automated region of interest detection and segmentation algorithms, could enhance the efficiency and accuracy of COVID-19 diagnosis. [ABSTRACT FROM AUTHOR]

The rapid deployment of electric vehicles (EVs) in Jordan requires massive efforts to prepare the infrastructures that serve the transportation sector. The lack of EV charging stations is the major obstacle that faces EV drivers. Utilizing renewable energy in EV charging stations contributes to the spread of these stations. Renewable energy technologies are environmentally friendly as they mitigate greenhouse gases that cause the global warming phenomenon. In this paper, an EV charging station, based on a PV-biodiesel-battery hybrid system, is investigated. The importance of this article is to discuss the hybrid system of PV and waste vegetable oil (WVO) along with storage that applies the maximum reliability supply, having an environmentally friendly supply and achieving the lowest energy cost of charging. This station is designed based on renewable and WVO utilization. In fact, WVO, coming from restaurants, is exploited to produce electricity by a diesel generator through direct burning after converting it into biodiesel. The capacity of each station is 14 cars/day with a medium-speed charger of 7 kW. The system is simulated and optimized using iHOGA software where multiobjective optimization is applied to achieve the minimum net present cost (NPC) and CO2 emissions considering three cases, PV-diesel, PV-biodiesel, and PV-WVO, all with battery-hybrid system. These values for the EV charging station that uses the PV-biodiesel-battery hybrid system are 624408 € and 15.4 tons/year, respectively. The corresponding values are 781473 € and 15.14 tons/year and 615310 € and 18.84 tons/year for the systems that work with diesel and WVO, respectively, and energy cost is achieved in best solution to be 0.11 Euro/kWh. Simulation results show that the proposed technique led to enhanced operational efficiency in terms of both NPC and annual CO2 emissions. [ABSTRACT FROM AUTHOR]

In this paper, the sequential conformable Langevin-type differential equation is studied. A representation of a solution consisting of the newly defined conformable bivariate Mittag-Leffler function to its nonhomogeneous and linear version is obtained via the conformable Laplace transforms' technique. Also, existence and uniqueness of a global solution to its nonlinear version are obtained. The existence and uniqueness of solutions are shown with respect to the weighted norm defined in compliance with (conformable) exponential function. The concept of the Ulam–Hyers stability of solutions is debated based on the fixed-point approach. The LRC electrical circuits are presented as an application to the described system. Simulated and numerical instances are offered to instantiate our abstract findings. [ABSTRACT FROM AUTHOR]

In this paper, we develop the theoretical provisions of symmetric cryptographic algorithms based on the polynomial residue number system for the first time. The main feature of the proposed approach is that when reconstructing the polynomial based on the method of undetermined coefficients, multiplication is performed not on the found base numbers but on arbitrarily selected polynomials. The latter, together with pairwise coprime residues of the residue class system, serve as the keys of the cryptographic algorithm. Schemes and examples of the implementation of the developed polynomial symmetric encryption algorithm are presented. The analytical expressions of the cryptographic strength estimation are constructed, and their graphical dependence on the number of modules and polynomial powers is presented. Our studies show that the cryptanalysis of the proposed algorithm requires combinatorial complexity, which leads to an NP-complete problem. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the atomic solution of a special type of fractional partial differential equations. Tensor product in Banach spaces, some properties of atom operators, and some properties of conformable fractional derivatives are utilized in such process. JEL Classification: 34G10, 34A55 [ABSTRACT FROM AUTHOR]

Purpose. This study aims to elucidate the gender gaps in the strategies for maintaining the social participation and interaction of older adults with people in a local community. Methods. The subjects were 130 older adults aged 65 years or older and independently living in their homes in Saitama, Saitama Prefecture, Japan. The authors conducted an anonymous questionnaire survey on paper and performed multiple regression analysis for men and women separately using the Lubben Social Network Scale-6 (LSNS-6) as the dependent variable. Results. Among the independent variables used for the analysis, those that significantly influenced LSNS-6 in men were social activities related to the daily life satisfaction of the elderly (SARDS). The independent variables that significantly influenced LSNS-6 in women were SARDS, sense of coherence-13 (SOC-13), and the number of family members living with the subjects. Discussion. Health promotion is not associated with interaction with people in men compared with women. [ABSTRACT FROM AUTHOR]

The detection of ballastless track surface (BTS) defects is a prerequisite for ensuring the safe operation of high-speed railways. Traditional convolutional neural networks fail to fully exploit contextual information and lack global pixel representations. The extensive stacking of convolutions leads deep learning models to play a black-box detection role, lacking interpretability. Due to the current lack of sufficient high-quality surface data for ballastless tracks, it is a severe constraint on the accurate identification of the substructure state in high-speed railways. This paper proposes an intelligent detection method for BTS defects named TrackNet based on self-attention and transfer learning. The method enhances the fusion ability of global features of BTS defects using multihead self-attention. The model's dependence on extensive defect data is reduced by transferring knowledge from large-scale publicly available datasets. Experimental results demonstrate that compared to advanced Swin Transformer model results, the TrackNet model achieves improvements in average accuracy and F1-score by 5.15% and 5.16%, respectively, on limited test data. The TrackNet model visualizes the decision regions of the model in identifying BTS defects, revealing the black-box recognition mechanism of deep learning models. This research performs engineering applications and provides valuable insights for the multiclass recognition of BTS defects in high-speed railways. [ABSTRACT FROM AUTHOR]

In this paper, a new simultaneous framework of distribution network operation is proposed based on the scheduling of photovoltaic/wind/battery multi-microgrids with network reconfiguration considering self-healing. The objective function is considered to minimize the energy losses, and multi-microgrids cost and improve the reliability indices including minimization of energy not-supplied (ENS) and minimizing system average interruption duration (SAIDI) and system average interruption frequency (SAIFI) indices. The optimization variables are defined as the situation of the distribution network switches to find the network's optimal configuration with the installation location and size of renewable resources and battery energy storage during 24 hours. An improved beluga whale optimization (IBWO) based on a nonlinearly diminishing inertia weight (NDIW) approach is used to find the optimal variable set of the problem. The recommended methodology is implemented on 33-bus and real 59-bus distribution networks. The results demonstrated that by obtaining an optimal state of the network switches in the event of a fault, as well as the optimal scheduling of the two microgrids, the energy losses have decreased and the reliability indices have improved. The outcomes of the proposed methodology based on the hybrid multi-microgrid allocation and network reconfiguration are stated that the losses, ENS, SAIDI, and SAIFI are reduced by 66.39%, 54.00%, 50.24%, and 33.61%, respectively, for 33-bus network and are declined by 65.29%, 57.44%, 48.63%, and 62.33%, respectively, for the real 59-bus Ahvaz network in comparison with the base network. The obtained results illustrated that in the condition of the network line outage, by simultaneously implementing the reconfiguration with the change of network switches and the optimal allocation and scheduling of HMGs in the network, self-healing is provided to prevent a significant weakening of the network performance so that the resilience is improved in addition to minimizing the energy losses and the cost of HMG energy injection compared to the base network. The findings revealed that the objectives including losses, ENS, SAIFI, and SAIDI are increased by 78.29%, 33.00%, 83.33%, and 35.11%, respectively, due to outage of line 22 of the 33-bus network and these objectives are increased by 52.32%, 10.30%, 32.29%, and 79.66%, respectively, due to outage of line 12 of the 59-bus network compared with not considering the line outage. Moreover, the superior capability of the recommended NDIW-based IBWO has been confirmed in comparison with the well-known particle swarm optimizer (PSO) and ant lion optimizer (ALO) in solving the problem to achieve better objective value. [ABSTRACT FROM AUTHOR]

Nowadays, building structures in corrosive environments requires some considerations. Being lightweight, high tensile strength, and corrosion resistance are the features that make fiber-reinforced plastic (FRP) bars an alternative component for longitudinal steel reinforcement of concrete. On the other hand, the linear elastic behavior of FRP bars, alongside the brittle behavior of concrete, makes brittle members without considerable ductility. In this paper, the effect of compression region confinement with CFRP sheets on the FRP-reinforced concrete beams was experimentally investigated. Eight GFRP reinforced beams with 2 m length, including one reference beam and seven confined beams, were constructed and tested under a four-point bending test. Based on the type of confinement, specimens are categorized into four groups. Flexural behavior improvements, including load carry capacity, energy dissipation capacity, and ductility, were observed in at least one specimen of each confined group. According to the results, the specimen that was spirally confined with a 30 mm ribbon width and angle of 10° had the best total energy absorption up to about 110% improvement in comparison to the unconfined specimen. On the other hand, vertically confined specimens with 50 mm ribbon width showed the highest improvement in ductility indices and load carrying capacity up to 60% and 11% in comparison to unconfined specimens, respectively. Due to concrete compression zone fractures in flexural failure mode, the over-reinforce method is considered the design philosophy. Results indicate that regardless of the confinement type (discrete vertical, discrete spiral, or continuous spiral confinement), there is an optimal amount for width, blank space between ribbons, and depth of confinement to achieve the best flexural behavior. [ABSTRACT FROM AUTHOR]

In this paper, under appropriate hypotheses, we have the existence of a solution semigroup of partial differential equations with delay operator. These equations are used to describe time–age-structured cell cycle model. We also prove that the solution semigroup is a frequently hypercyclic semigroup. [ABSTRACT FROM AUTHOR]

Under the condition of weakly cemented soft rock in Western China, the surrounding rock deformation of gob-side entry is obvious, especially under the influence of fault, and the problem of surrounding rock control becomes increasingly prominent. To solve this issue, this paper firstly studied and obtained the evolution law of the ground pressure appearance of the gob-side entry in the fault area, then put forward the concept of surrounding rock control in the fault area of the gob-side entry, put forward a targeted pressure relief + increase preload + passive reinforcement parallel to the roadway surrounding rock strengthening control scheme, and carried out field applications. The results show that the roof subsidence and mining side heave of the roadway in the fault area increase significantly, accompanied by the characteristics of broken cables, the length of the broken cable is 0.2 m–0.6 m, and the breaking process is divided into multiple breaks. The stress evolution of gob-side entry in fault area presents a stage characteristic of "rapid growth-stable." The surrounding rock control effect is improved under the bolt cable high pre-tightening support, and the roadway deformation is effectively controlled under the pressure relief + active + passive support, with a good application effect. [ABSTRACT FROM AUTHOR]

The magnitude of pretension force serves as an important indicator of the effectiveness of anchor bolt active support and is one of the significant factors influencing the stability of tunnel surrounding rock. Therefore, in practical engineering, it is crucial to establish the relationship between pretension torque and pretension force. However, current research in this field has some limitations. With a specific mine in western China as the engineering background, this paper first analyzed the influence factors of surrounding rock deformation and failure, established a mechanical model for pretension structure of anchor bolt support, and derived the quantitative relationship between pretension torque and pretension force based on hypothetical conditions. Finally, it proposed the loss effect of anchor bolt support and modified the pretightening loss coefficient for engineering application. The research results show that the actual pretightening force of anchor support is lower due to the influence of deep soft coal seam, which leads to failure of coal pillar under mining stress. The loss effect of surrounding rock on pretightening force was represented by a coefficient k, and the modified value of the coefficient was calculated as 0.19∼0.43. By applying the modified relationship between pretightening torque and pretightening force, it was found that the actual pretightening torque of 300 Nm applied to 11307 return air roadway effectively controlled surrounding rock deformation and failure, with coal pillar displacement less than 120 mm. [ABSTRACT FROM AUTHOR]

This paper addresses the multiorder open-dimension three-dimensional rectangular packing problem (3D-MOSB-ODRPP), which involves packing rectangular items from multiple orders into a single, size-adjustable container. We propose a novel metaheuristic approach combining a genetic algorithm with the Gurobi solver. The algorithm incorporates a lower neighborhood search strategy and is underpinned by a mathematical model representing the multiorder open-dimension packing scenario. Extensive experiments validate the effectiveness of the proposed approach. The LNSGA algorithm outperforms Gurobi and the traditional genetic algorithm in solution quality and computational efficiency. For small-scale instances, LNSGA achieves optimal values in most cases. LNSGA demonstrates significant optimization improvements over Gurobi and the genetic algorithm for large-scale instances. The superior performance is attributed to the effective integration of the lower neighborhood search mechanism and the Gurobi solver. This study offers valuable insights for optimizing the packing process in e-commerce warehousing and logistics operations. [ABSTRACT FROM AUTHOR]

The increasing global dependence on fossil fuels for energy has prompted researchers to explore alternative power generation sources that offer higher efficiency, cost-effectiveness, and low environmental impact. Developed countries have been making continuous efforts to reduce their reliance on depleting fossil fuel sources to curb carbon emissions. As a result, hydrogen has gained significant attention among researchers as a potential future fuel that meets all the necessary criteria. Many countries aiming for a hydrogen economy have invested in research on fuel cells. Among various fuel cells, the solid oxide fuel cell (SOFC) has emerged as a commercially viable power source at a small scale. This paper provides an extensive review of the components, materials, design, operation, and integration strategies of SOFCs with existing thermal-based power plants. The performance and prospects of various SOFCs in standalone and hybrid modes are summarized, along with the limitations. Additionally, the research and commercialization efforts that can enhance the potential applications of SOFCs as a sustainable and reliable system in the long term are outlined. [ABSTRACT FROM AUTHOR]

The existing watermarking algorithms make it difficult to balance the invisibility and robustness of the watermark. This paper proposes a robust image watermarking method based on discrete wavelet transform (DWT), singular value decomposition (SVD), and chaotic maps. This method is a semiblind watermarking method. First, a chaotic logistic-tent map is introduced, employing an extensive chaotic parameter domain. This map is amalgamated with Arnold's transformation to encrypt the watermark image, thereby bolstering the security of the watermark information. Subsequently, the frequency domain is obtained by applying DWT to the carrier image. Embedding watermarks in the frequency domain ensures the invisibility of the watermark, with a preference for a high-frequency subband after the DWT of the carrier image for enhanced watermark robustness. SVD is then applied to both the high-frequency subband of the carrier image after DWT and the encrypted watermark image. The final step involves embedding the singular values of the encrypted watermark image into the carrier image's singular values, thereby completing the watermark information embedding process. In simulation experiments, an invisibility test was conducted on various carrier images, yielding peak signal-to-noise ratio (PSNR) values consistently exceeding 43, and structural similarity (SSIM) close to 1. Robustness testing against various types of attacks resulted in normalized correlation (NC) values consistently surpassing 0.9, with bit error rate (BER) values approaching 0. In conclusion, the proposed algorithm satisfies imperceptibility requirements while demonstrating formidable robustness. [ABSTRACT FROM AUTHOR]

In this paper, a fractional model in the Caputo sense is used to characterize the dynamics of HPV with cervical cancer. Generalized mean value theorem has been used to examine whether the infection model has a unique positive solution. The model has two equilibrium points: the disease-free point and the endemic point. The examination of the system's local and global stability is provided in terms of the basic reproductive number R p ° . The global stability analysis has been carried out using an appropriate Lyapunov function and the LaSalle invariant principle. The results demonstrate that in the infection model, if R p ° < 1 , then the solution converges to the disease-free equilibrium, which is both locally and globally asymptotically stable. Whilst R p ° > 1 , the endemic equilibrium is considered to exist. Simulations are implemented via a finite difference method with Grünwald-Letnikov discretization approach for Caputo derivative operator to define how changes in parameters impact the dynamic behavior of the system using Matlab. [ABSTRACT FROM AUTHOR]

The flavor profile of meat and its processed products is highly volatile and subject to significant changes during storage, processing, and transportation. It is therefore crucial to monitor the flavor of meat to evaluate sensory quality and protect consumer health and safety. Gas chromatography-ion mobility spectrometry (GC-IMS) has become increasingly popular due to its advantages of being nondestructive, rapid, and capable of trace detection. The analysis of volatile organic compounds (VOCs) using this technique is continuously applied in the assessment of food freshness, origin traceability, and adulteration detection. GC-IMS has emerged as a promising tool for accurate monitoring and characterization of VOCs in food, particularly in the field of meat flavor analysis. Its applications include meat product authentication, adulteration detection, processing and storage-related flavor changes, and freshness monitoring. This paper provides a comprehensive overview of the working principle of GC-IMS and its applications in meat flavor analysis, while exploring future trends and potential limitations associated with the technique. [ABSTRACT FROM AUTHOR]

This research paper focuses on the removal of the nonsteroidal anti-inflammatory drug, ibuprofen, from an aqueous solution using a dissolved air flotation process. The comparison of different types of cationic surface-active agents such as cetyltrimethyl ammonium bromide (CTAB), tetrabutyl ammonium bromide (TBAB), and octyltrimethyl ammonium bromide (OTAB) have been employed to scrutinize the effective removal of the ecotoxic pharmaceutically active compound. The work included the influencing parameters such as pressure, contact time, surfactant dosage, pH, flow rate, and initial concentration owing to the best-optimized conditions. The maximum removal rate of 96.09% was achieved at 15 min for CTAB, TBAB had 62.36% at 45 min, and 89.6% was obtained for OTAB at 30 min, with 50 mg L−1 as the initial concentration and pH = 4. The removal rate was better with the optimized dosage of CTAB at 0.6 g, TBAB at 1.2 g, and OTAB at 1.0 g. It was observed that the geometric shape of the surface-active agents had greater impacts on the contaminants' efficiency. CTAB and OTAB were combined to find out the best possible removal rate of contaminants. The synergistic effect augments surfactant-based occurrence to be better in forming a good foaming effect and tends to have a lower critical micelle concentration (CMC). From the evaluation of kinetic models, pseudo-second-order flotation kinetics fitted the experimental data best. Furthermore, the formed metabolites that had been identified using gas chromatography-mass spectrometry were found to be less toxic than the parenting compounds. [ABSTRACT FROM AUTHOR]

Compressive sampling matching pursuit (CoSaMP), as a conventional algorithm requiring system sparsity and sensitive to step size, was improved in this paper by approximating the sparsity with adaptive variable step size. In the proposed algorithm (CoSaMP with variable step size abbreviated as Vss-CoSaMP), the idea of approximating sparsity with adaptive step size was borrowed from the sparsity adaptive matching pursuit (SAMP) algorithm to determine the sparsity for the CoSaMP algorithm. The applicability of the CoSaMP algorithm was therefore expanded considerably. On this basis, a step size reduction was added as the iteration termination condition of an orthogonal frequency division multiplexing (OFDM) system. An adaptive variable step size algorithm was then put forward to address the CoSaMP algorithm's sensitivity to step size. It could realize the required precision at different initial step sizes. A simulation was carried out to analyze the influence of pilot number and step size in an OFDM system on the algorithm. The algorithms, including SAMP, CoSaMP, and Vss-CoSaMP, were compared with two sparse channels, revealing that the Vss-CoSaMP algorithm overcame the problem of the CoSaMP algorithm, that is, the impossibility to forecast the channel sparsity. With the adaptive step size, the proposed algorithm could reach and achieve better accuracy than the CoSaMP algorithm. Additionally, the proposed algorithm was superior over the SAMP algorithm in terms of reconstruction, mean square error (MSE), and bit error ratio (BER). [ABSTRACT FROM AUTHOR]

In the past few years, there has been a notable interest in the application of machine learning methods to enhance energy efficiency in the smart building industry. The paper discusses the use of machine learning in smart buildings to improve energy efficiency by analyzing data on energy usage, occupancy patterns, and environmental conditions. The study focuses on implementing and evaluating energy consumption prediction models using algorithms like long short-term memory (LSTM), random forest, and gradient boosting regressor. Real-life case studies on educational buildings are conducted to assess the practical applicability of these models. The data is rigorously analyzed and preprocessed, and performance metrics such as root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to compare the effectiveness of the algorithms. The results highlight the importance of tailoring predictive models to the specific characteristics of each building's energy consumption. [ABSTRACT FROM AUTHOR]