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The present study was planned to evaluate the infection concerning Anaplasma platys in dogs for the first time in Nineveh Province, Iraq, using microscopic examination and conventional polymerase chain reaction (PCR) techniques. Eighty-one blood samples were randomly collected from dogs (31 household dogs and 50 stray dogs). Those samples were drained from dogs introduced to the Veterinary Teaching Hospital at the University of Mosul and private veterinary clinics in Nineveh province. The microscopic examination was used to check the blood films prepared for preliminary detection of morulae of A. platys within platelets; furthermore, the molecular technique was used to validate the presence of A. platys by targeting the gltA gene. In that, PCR yielded an amplification of 690 bp band size products, which was the expected size of the targeted gene. After that, these PCR amplicons were sent for sequencing to verify the presence of the pathogen, and then the sequence results were subsequently aligned with other strains available on the NCBI database. Finally, a phylogenetic tree for the related strains was constructed by Mega-11 software. The outcomes indicated that the overall infection rate of A. platys infection in dogs was 6/81 (7.4%) and 11/81 (13.6%) using microscopic examination and molecular techniques, respectively. Additionally, there was no significant difference within the dogs' categories in this study. In conclusion, it has been shown that A. platys in dogs were circulating in Nineveh province; therefore, plans for control measures are advised.

This study is presented to examine the performance of a newly proposed metaheuristic algorithm within discrete and continuous search spaces. Therefore, the multithresholding image segmentation problem and parameter estimation problem of both the proton exchange membrane fuel cell (PEMFC) and photovoltaic (PV) models, which have different search spaces, are used to test and verify this algorithm. The traditional techniques could not find approximate solutions for those problems in a reasonable amount of time, so researchers have used metaheuristic algorithms to overcome those shortcomings. However, the majority of metaheuristic algorithms still suffer from slow convergence speed and stagnation into local minima problems, which makes them unsuitable for tackling these optimization problems. Therefore, this study proposes an improved nutcracker optimization algorithm (INOA) for better solving those problems in an acceptable amount of time. INOA is based on improving the performance of the standard algorithm using a newly proposed convergence improvement strategy that aims to improve the convergence speed and prevent stagnation in local minima. This algorithm is first applied to estimating the unknown parameters of the single-diode, double-diode, and triple-diode models for a PV module and a solar cell. Second, four PEMFC modules are used to further observe INOA's performance for the continuous optimization challenge. Finally, the performance of INOA is investigated for solving the multi-thresholding image segmentation problem to test its effectiveness in a discrete search space. Several test images with different threshold levels were used to validate its effectiveness, stability, and scalability. Comparison to several rival optimizers using various performance indicators, such as convergence curve, standard deviation, average fitness value, and Wilcoxon rank-sum test, demonstrates that INOA is an effective alternative for solving both discrete and continuous optimization problems. Quantitively, INOA could solve those problems better than the other rival optimizers, with improvement rates for final results ranging between 0.8355 % and 3.34 % for discrete problems and 4.97 % and 99.9 % for continuous problems.

Abstract Binary optimization problems belong to the NP-hard class because their solutions are hard to find in a known time. The traditional techniques could not be applied to tackle those problems because the computational cost required by them increases exponentially with increasing the dimensions of the optimization problems. Therefore, over the last few years, researchers have paid attention to the metaheuristic algorithms for tackling those problems in an acceptable time. But unfortunately, those algorithms still suffer from not being able to avert local minima, a lack of population diversity, and low convergence speed. As a result, this paper presents a new binary optimization technique based on integrating the equilibrium optimizer (EO) with a new local search operator, which effectively integrates the single crossover, uniform crossover, mutation operator, flipping operator, and swapping operator to improve its exploration and exploitation operators. In a more general sense, this local search operator is based on two folds: the first fold borrows the single-point crossover and uniform crossover to accelerate the convergence speed, in addition to avoiding falling into local minima using the mutation strategy; the second fold is based on applying two different mutation operators on the best-so-far solution in the hope of finding a better solution: the first operator is the flip mutation operator to flip a bit selected randomly from the given solution, and the second operator is the swap mutation operator to swap two unique positions selected randomly from the given solution. This variant is called a binary hybrid equilibrium optimizer (BHEO) and is applied to three common binary optimization problems: 0–1 knapsack, feature selection, and the Merkle–Hellman knapsack cryptosystem (MHKC) to investigate its effectiveness. The experimental findings of BHEO are compared with those of the classical algorithm and six other well-established evolutionary and swarm-based optimization algorithms. From those findings, it is concluded that BHEO is a strong alternative to tackle binary optimization problems. Quantatively, BHEO could reach an average fitness of 0.090737884 for the feature section problem and an average difference from the optimal profits for some used Knapsack problems of 2.482.

This study proposed a neutrosophic set framework with TreeSoft Set for sustainable waste valorization selection. The neutrosophic set is used to overcome uncertainty and vague information in the evaluation process. The neutrosophic set has three membership degrees: truth, indeterminacy, and falsity. The multicriteria decision-making (MCDM) methodology deals with various criteria to evaluate waste valorization. The VIKOR method is an MCDM method used to rank the alternatives. The numerical example was created with 12 criteria and 10 alternatives. Three decision-makers and experts are invited to evaluate the requirements and options. We used the bipolar neutrosophic numbers to replace the opinions of experts.

This paper examines the performance of three binary metaheuristic algorithms when applied to two distinct knapsack problems (0–1 knapsack problems (KP01) and multidimensional knapsack problems (MKP)). These binary algorithms are based on the classical mantis search algorithm (MSA), the classical quadratic interpolation optimization (QIO) method, and the well-known differential evolution (DE). Because these algorithms were designed for continuous optimization problems, they could not be used directly to solve binary knapsack problems. As a result, the V-shaped and S-shaped transfer functions are used to propose binary variants of these algorithms, such as binary differential evolution (BDE), binary quadratic interpolation optimization (BQIO), and binary mantis search algorithm (BMSA). These binary variants are evaluated using various high-dimensional KP01 examples and compared to several classical metaheuristic techniques to determine their efficacy. To enhance the performance of those binary algorithms, they are combined with repair operator 2 (RO2) to offer better hybrid variants, namely HMSA, HQIO, and HDE. Those hybrid algorithms are evaluated using several medium- and large-scale KP01 and MKP instances, as well as compared to other hybrid algorithms, to demonstrate their effectiveness. This comparison is conducted using three performance metrics: average fitness value, Friedman mean rank, and computational cost. The experimental findings demonstrate that HQIO is a strong alternative for solving KP01 and MKP. In addition, the proposed algorithms are applied to the Merkle-Hellman Knapsack Cryptosystem and the resource allocation problem in adaptive multimedia systems (AMS) to illustrate their effectiveness when applied to optimize those real applications. The experimental findings illustrate that the proposed HQIO is a strong alternative for handling various knapsack-based applications.

The introduction of Industry 4.0 has brought about a significant shift in the manufacturing and supply chain management sectors, requiring supplier selection procedures to be adjusted to this rapidly changing technical environment. This study aims to improve supplier selection in Industry 4.0. This selection contains various criteria, so multi-criteria decision-making (MCDM) is used to deal with these criteria. The interval-valued neutrosophic sets (IVNSs) are used to deal with uncertainty in the evaluation process. The IVNSs are integrated with the TreeSoft Set. The TOPSIS method is an MCDM method used to rank the alternatives. The results show that the economic criterion is the most important, and supplier 7 is the best.

Abstract The parameter identification problem of photovoltaic (PV) models is classified as a complex nonlinear optimization problem that cannot be accurately solved by traditional techniques. Therefore, metaheuristic algorithms have been recently used to solve this problem due to their potential to approximate the optimal solution for several complicated optimization problems. Despite that, the existing metaheuristic algorithms still suffer from sluggish convergence rates and stagnation in local optima when applied to tackle this problem. Therefore, this study presents a new parameter estimation technique, namely HKOA, based on integrating the recently published Kepler optimization algorithm (KOA) with the ranking-based update and exploitation improvement mechanisms to accurately estimate the unknown parameters of the third-, single-, and double-diode models. The former mechanism aims at promoting the KOA’s exploration operator to diminish getting stuck in local optima, while the latter mechanism is used to strengthen its exploitation operator to faster converge to the approximate solution. Both KOA and HKOA are validated using the RTC France solar cell and five PV modules, including Photowatt-PWP201, Ultra 85-P, Ultra 85-P, STP6-120/36, and STM6-40/36, to show their efficiency and stability. In addition, they are extensively compared to several optimization techniques to show their effectiveness. According to the experimental findings, HKOA is a strong alternative method for estimating the unknown parameters of PV models because it can yield substantially different and superior findings for the third-, single-, and double-diode models.

Abstract The Internet of Things (IoT) devices are not able to execute resource-intensive tasks due to their limited storage and computing power. Therefore, Mobile edge computing (MEC) technology has recently been utilized to provide computing and storage capabilities to those devices, enabling them to execute these tasks with less energy consumption and low latency. However, the edge servers in the MEC network are located at fixed positions, which makes them unable to be adjusted according to the requirements of end users. As a result, unmanned aerial vehicles (UAVs) have recently been used to carry the load of these edge servers, making them mobile and capable of meeting the desired requirements for IoT devices. However, the trajectories of the UAVs need to be accurately planned in order to minimize energy consumption for both the IoT devices during data transmission and the UAVs during hovering time and mobility between halting points (HPs). The trajectory planning problem is a complicated optimization problem because it involves several factors that need to be taken into consideration. This problem is considered a multiobjective optimization problem since it requires simultaneous optimization of both the energy consumption of UAVs and that of IoT devices. However, existing algorithms in the literature for this problem have been based on converting it into a single objective, which may give preference to some objectives over others. Therefore, in this study, several multiobjective trajectory planning algorithms (MTPAs) based on various metaheuristic algorithms with variable population size and the Pareto optimality theory are presented. These algorithms aim to optimize both objectives simultaneously. Additionally, a novel mechanism called the cyclic selection mechanism (CSM) is proposed to manage the population size accurately, optimizing the number of HPs and the maximum function evaluations. Furthermore, the HPs estimated by each MTPA are associated with multiple UAVs using the k-means clustering algorithm. Then, a low-complexity greedy mechanism is used to generate the order of HPs assigned to each UAV, determining its trajectory. Several experiments are conducted to assess the effectiveness of the MTPAs with variable population size and cyclic selection mechanisms. The experimental findings demonstrate that the MTPAs with the cyclic selection mechanism outperform all competing algorithms, achieving better outcomes.

The advancement towards 6G networks necessitates innovative solutions for efficient deployment and management of heterogeneous networks. Although the synergy of software-defined networks (SDN) and blockchain technology brings cost and spectrum efficiencies to future network deployments, their impact on network performance is yet to be investigated. This paper investigates the integration of SDN and blockchain technology to facilitate seamless roaming across multiple mobile network operators (MNOs). We develop analytical models based on queuing theory, specifically using M/M/1 models to characterize network performance and validate them through extensive NS-3 simulations. Evaluating three consensus mechanisms—Practical Byzantine Fault Tolerance (PBFT), Raft, and Paxos—our findings demonstrate Raft’s superior performance, reducing end-to-end delays by 10.7% and 18.7% over PBFT and Paxos, respectively, with increasing packet arrival rate. Additionally, Raft decreases handover delays by 12.32% and 25.73% compared to PBFT and Paxos, with increased femtocell spacing. Our research also identifies cell residence time as a critical factor impacting handover delays, influenced by user equipment speed and femtocell spacing. This study offers valuable insights for optimizing network deployments and enabling dynamic mobility solutions in future multi-operator networks, paving the way for enhanced user experiences and verticals. The selection of consensus mechanisms and understanding of cell dynamics emerge as crucial considerations for network performance optimization in 6G and beyond.

Abstract Chest diseases, especially COVID-19, have quickly spread throughout the world and caused many deaths. Finding a rapid and accurate diagnostic tool was indispensable to combating these diseases. Therefore, scientists have thought of combining chest X-ray (CXR) images with deep learning techniques to rapidly detect people infected with COVID-19 or any other chest disease. Image segmentation as a preprocessing step has an essential role in improving the performance of these deep learning techniques, as it could separate the most relevant features to better train these techniques. Therefore, several approaches were proposed to tackle the image segmentation problem accurately. Among these methods, the multilevel thresholding-based image segmentation methods won significant interest due to their simplicity, accuracy, and relatively low storage requirements. However, with increasing threshold levels, the traditional methods have failed to achieve accurate segmented features in a reasonable amount of time. Therefore, researchers have recently used metaheuristic algorithms to tackle this problem, but the existing algorithms still suffer from slow convergence speed and stagnation into local minima as the number of threshold levels increases. Therefore, this study presents an alternative image segmentation technique based on an enhanced version of the Kepler optimization algorithm (KOA), namely IKOA, to better segment the CXR images at small, medium, and high threshold levels. Ten CXR images are used to assess the performance of IKOA at ten threshold levels (T-5, T-7, T-8, T-10, T-12, T-15, T-18, T-20, T-25, and T-30). To observe its effectiveness, it is compared to several metaheuristic algorithms in terms of several performance indicators. The experimental outcomes disclose the superiority of IKOA over all the compared algorithms. Furthermore, the IKOA-based segmented CXR images at eight different threshold levels are used to train a newly proposed CNN model called CNN-IKOA to find out the effectiveness of the segmentation step. Five performance indicators, namely overall accuracy, precision, recall, F1-score, and specificity, are used to disclose the CNN-IKOA’s effectiveness. CNN-IKOA, according to the experimental outcomes, could achieve outstanding outcomes for the images segmented at T-12, where it could reach 94.88% for overall accuracy, 96.57% for specificity, 95.40% for precision, and 95.40% for recall.

The 0–1 Knapsack problem is a non-deterministic polynomial-time-hard combinatorial optimization problem that cannot be solved in reasonable time using traditional methods. Therefore, researchers have turned to metaheuristic algorithms for their ability to solve several combinatorial problems in a reasonable amount of time. This paper adapts the Kepler optimization algorithm using eight V-shaped and S-shaped transfer functions to create a binary variant called BKOA for solving the 0–1 Knapsack problem. Several experiments were conducted to compare the efficacy of the binary Kepler optimization algorithm to several competing optimizers when solving 20 well-known knapsack instances with dimensions ranging from 4 to 75. The experimental results demonstrate the superiority of this algorithm over other metaheuristic algorithms, except for the genetic algorithm, which is marginally superior. To further improve the binary Kepler optimization algorithm, it is combined with an enhanced improvement strategy to create a new hybrid variant. This hybrid variant, termed HBKOA, has superior exploration and exploitation capabilities that make it better than genetic algorithm and other optimizers for all performance metrics considered. The enhanced improvement strategy is also integrated with several competing optimizers, and the experimental results show that HBKOA, hybrid binary manta ray foraging optimization, and hybrid binary equilibrium optimizer are competitive for small and medium-dimensional instances and superior for the higher dimensions.

Wheat has had a substantial influence on the food security of a number of different nations. In addition, governments are grappling with a number of difficulties, such as fast population expansion, a lack of available water, growing urbanization, and a restricted amount of wheat production in agricultural settings. As a result, the majority of their wheat and wheat products come from outside sources. The purpose of this research is to discover the main wheat suppliers and rate them according to certain criteria by analyzing the different ways of supplier selection that are currently available. The type-2 neutrosophic numbers-Measurement of Alternatives and Ranking according to the Compromise Solution (T2NNs-MARCOS) methodology was used in order to evaluate, choose, and rank the most reliable wheat suppliers in the African and Middle Eastern regions. According to the data, Russia is the country that provides the highest quality wheat. Because of its proximity, its robust connections via official channels, and its adaptability, this provider is often regarded as being the most reliable and cost-effective option. Because wheat is a key commodity, importers, decision-makers, and anyone involved with wheat imports may find this research helpful in identifying and selecting suppliers.

This research was conducted with the intention of determining which scenario for the construction of an intelligent reverse logistics system had the most potential for success. This selection would then be used as a reference point for decision-making throughout the process of constructing environmentally friendly closed supply chains and circular economies. The research includes the definition of four different development scenarios, each of which is then reviewed by representatives of the key stakeholders based on a comprehensive list of eight sub-indicators that are categorized under the four primary dimensions. In order to tackle the issue that was specified, a brand new multi-criteria decision-making (MCDM) framework was constructed. This model included using the Combinative Distance based Assessment (CODAS) technique in a neutrosophic environment and applying the type-2 neutrosophic numbers (T2NNs). The utilization of the developed framework led to the identification of the scenario that optimally reconciles the widespread implementation of Industry 4.0 technologies with the requisite resources.

Optimization problems are prevalent in a variety of real-world applications, including medical, engineering, chemical, and others, and must be precisely solved to enhance the performance of these applications. Unfortunately, finding near-optimal solutions to these problems is regarded as a hard challenge due to their various characteristics. In a new attempt to solve these problems, this paper presents a new variant of the artificial gorilla troops optimizer (GTO) called ranking-based GTO (RGTO). This variant uses two strategies known as the ranking-based update strategy and the convergence acceleration strategy to improve both the classical GTO's exploitation and exploration capabilities. The first strategy is proposed to enhance each gorilla's local and global search abilities, whereas the latter is intended to enhance GTO's global search abilities to reach better solutions as quickly as possible. First, a recent and challenging benchmark, namely CEC-2017, is utilized to assess the RGTO's explorative and exploitative capabilities. After that, RGTO is used to solve three engineering optimization problems, including parameter estimation problems for both photovoltaic (PV) models and proton exchange membrane fuel cells (PEMFCs), as well as some engineering design problems, to demonstrate how well it performs for real-world optimization problems. Compared to several rival optimizers, the proposed algorithm provides outstanding outcomes for the three engineering optimization benchmark problems considered.

We present a case of a man immunocompromised due to myelodysplastic syndrome with Candida krusei fungemia who had a rising cell-free DNA (cfDNA) giant magnetoresistance (GMR) signal when tested daily using plasma blood samples. With the rise in GMR signal paralleling the development of skin lesions in this patient, we conclude that cfDNA can be used to indicate uncontrolled infection and thus help monitor response to therapy. This index patient provides evidence that an invasive fungal infection requires both direct antifungal therapy and an intact immune system to control the infection. This biosensing platform has been simplified to potentially serve as a point-of-care test, setting it apart by overcoming the three common barriers of cfDNA testing: complexity, cost, and time.

In this paper, several recently published metaheuristic algorithms are adapted to optimize the NP-hard problem of jointly mining decision and resource allocation in mobile edge computing (MEC) enabled blockchain networks under two different encoding schemes. The first scheme represents individuals in a way that incorporates the mining decisions, transmission power, and computing resources of all miners for each individual, with mining decisions determined by a binary vector whose values indicate whether miners partake in mining or not. While, the second scheme makes each individual accountable for the transmission power and computing resources of each participant miner, treating all individuals as a singular solution to the problem. Then the Nutcracker optimization algorithm and gradient-based optimizer are modified to propose two robust variants, MNOA and MGBO, respectively. We then combine MNOA and MGBO to create HNOA, which further optimizes the mining decision and resource allocation in this problem. HNOA and other variants are validated using nine instances with a range of 150 to 600 miners. HNOA is also compared to several competing optimizers to demonstrate its efficacy in terms of several performance metrics. The experimental findings show the superiority of the proposed algorithm.

As a new attempt to design a precise mathematical model for the proton exchange membrane fuel cell (PEMFC), in this paper, three recently-proposed well-established optimizers: horse herding optimization algorithm (HOA), seagull optimization algorithm (SOA) and gradient-based optimizer (GBO) integrated with two newly-proposed effective strategies, namely self-adaptive strategy, and ranking-based updating strategy, have been extensively investigated to accurately estimate the unknown parameters of this model for accomplishing a better output voltage of the simulated PEMFC stacks. Those hybridized algorithms were briefly named HHOA, HSOA, and HGBO. To assess the performance of those proposed algorithms, six common PEMFC stacks were used and their outcomes were extensively compared with the standard algorithms and some of the state-of-the-arts under various performance metrics and the Wilcoxon rank-sum test. The experimental findings show the effectiveness of both HGBO and HSOA in terms of convergence speed and final accuracy; However, HSOA could be more stable. The source code of this study is publicly available at https://drive.matlab.com/sharing/d9263036-9f80-4a40-bad9-ad476ed19c69.

Brucellosis is a widespread and endemic disease in the Middle East and Mediterranean regions, and it has significant public health and livestock industry concerns. It can affect a wide range of mammalian hosts, including humans. This study aimed to determine the seroprevalence of thelatent infection of brucellosisin calves; Rose Bengal and indirect ELISA tests were conducted for this purpose. The serum samples were collected from 184 local calves of both sexes aged between 1 to 8 months (92 males and 92 females) from different regions of Mosul city. The current study revealed that all samples tested negative with the Rose Bengal test, whereas, in indirect ELISA 5.9%, the samples were against Brucella species. The seroprevalence of Brucella antibodies showed no statistical differences between males and females, which was 5.4 and 6.5%, respectively. This study concluded that the indirect ELISA test showed high diagnostic efficacy in detecting anti-Brucella antibodies in young calves.

Deep neural network models can achieve greater performance in numerous machine learning tasks by raising the depth of the model and the amount of training data samples. However, these essential procedures will proportionally raise the cost of training deep neural network models. Accelerating the training process of deep neural network models in a distributed computing environment has become the most often utilized strategy for developers in order to better cope with a huge quantity of training overhead. The current deep neural network model is the stochastic gradient descent (SGD) technique. It is one of the most widely used training techniques in network models, although it is prone to gradient obsolescence during parallelization, which impacts the overall convergence. The majority of present solutions are geared at high-performance nodes with minor performance changes. Few studies have taken into account the cluster environment in high-performance computing (HPC), where the performance of each node varies substantially. A dynamic batch size stochastic gradient descent approach based on performance-aware technology is suggested to address the aforesaid difficulties (DBS-SGD). By assessing the processing capacity of each node, this method dynamically allocates the minibatch of each node, guaranteeing that the update time of each iteration between nodes is essentially the same, lowering the average gradient of the node. The suggested approach may successfully solve the asynchronous update strategy’s gradient outdated problem. The Mnist and cifar10 are two widely used image classification benchmarks, that are employed as training data sets, and the approach is compared with the asynchronous stochastic gradient descent (ASGD) technique. The experimental findings demonstrate that the proposed algorithm has better performance as compared with existing algorithms.

Recently, Code Quality (CQ) has become critical in a wide range of organizations and in many areas from academia to industry. CQ, in terms of readability, security, and testability, is a major goal throughout the software development process because it affects overall Software Quality (SQ) in terms of subsequent releases, maintenance, and updates. It is particularly important for the development of safety critical systems. Existing studies on CQ have several shortcomings in that they are based on incomplete information about the source code, and tend to focus on only one feature, which is likely to determine the performance of the model. Moreover, these considerations often limit obtaining high accuracy because there is no strong relationship between the input data and the output data. Thus, it is necessary to design an effective and efficient SQ measurement system for measuring multiple quality factors. To that end, we propose a deep learning framework that employed a Latent Dirichlet Allocation (LDA) with Convolutional Neural Networks (CNN), called CNN-LDA, to classify input data into topics that are related to CQ features and to identify hidden patterns and correlations in programming data. Three SQ metrics (i.e., readability, security, and testability) and machine learning techniques (e.g., random forest (RF) and support vector machine (SVM)) are taken into account to validate the proposed model. The proposed CNN-LDA outperformed its peers across the vast majority of datasets examined. The average overall F-measure for readability, security, and testability are 94%,94% and 93%. The average overall accuracy for readability, security, and testability are 93%,93% and 92%. The superiority of LDA-CNN over the other classifiers was very clear based on a Wilcoxon’s non-parametric statistical test (α=0.05).

In this paper, a binary variant of a novel nature-inspired metaheuristic algorithm called the nutcracker optimization algorithm (NOA) is presented for binary optimization problems. Because of the continuous nature of the classical NOA and the discrete nature of the binary problems, two different families of transfer functions, namely S-shaped and V-shaped, are extensively investigated for converting the classical NOA into a binary variant, namely BNOA, applicable for various binary problems. Additionally, BNOA is improved using a local search strategy based on effectively integrating some genetic operators into the BNOA’s exploitation and exploration; this additional variant is called BINOA. Both BNOA and BINOA are evaluated using three common binary optimization problems, including feature selection, 0-1 knapsack, and the Merkle–Hellman knapsack cryptosystem (MHKC), and are compared to several robust binary metaheuristic optimizers in terms of statistical information, statistical tests, and convergence speed. The experiential findings show that BINOA is better than the classical BNOA and the other rival optimizers for both the 0-1 knapsack problem and attacking MHKC and is on par with some algorithms, like the genetic algorithm for feature selection.

The concept of future-proofing cities seeks to minimise environmental impacts by utilising various mitigation and adaptation techniques, specifically by improving urban fabric. Studies on urban forms and parameters have been extensively conducted in hot, arid and humid climates while they have been less investigated in temperate climates. In the UK, the current local urban design guides meet the needs of designers and urban planners, however, they are still considered descriptive and lack in-depth environmental assessment. This study aims to investigate the recommended threshold values in the National Model Design Code against real sites with different urban canyons morphologies to examine their impact on outdoor environmental conditions. The study used quantitative methods by conducting a review study, a survey on UK urban design guides and modelling using simulation via RayMan and IESVE. The study targeted pre-selected sites in Cardiff and Bristol that were identified as zones with urban heat stress. The results showed that the Aspect Ratio and Sky View Factor (SVF) in two cities have significantly impacted outdoor environmental conditions in urban areas subsequently affecting the Physiological Equivalent Temperature (PET). The results showed that the recommended values have a significant impact on the outdoor thermal performance of the pre-selected urban canyons. The findings demonstrated that modifying the width of streets and changing the Aspect Ratio and SVF resulted in achieving a positive impact during summer when both factors have a direct relationship. However, when the Aspect Ratio and SVF have an inverse relationship, the results demonstrated extreme cold stress during winter. Therefore, maintaining a direct relationship between the Aspect Ratio and SVF would help to improve outdoor conditions.

Recent advances in technology have led to a surge in interest in unmanned aerial vehicles (UAVs), which are remote-controlled aircraft that rely on cameras or sensors to gather information about their surroundings during flight. A UAV requires a path-planning technique that can swiftly recalculate a viable and quasi-optimal path in flight if a new obstacle or hazard is recognized or if the target is moved during the mission. In brief, the planning of UAV routes might optimize a specific problem determined by the application, such as the moving target problem (MTP), flight time and threats, or multiobjective navigation. The complexity of MTP ranges from NP-hard to NEXP-complete because there are so many probabilistic variables involved. Therefore, it is hard to detect a high-quality solution for this problem using traditional techniques such as differential calculus. Therefore, this paper hybridizes differential evolution (DE) with two newly proposed updating schemes to present a new evolution-based technique named hybrid differential evolution (HDE) for accurately tackling the MTP in a reasonable amount of time. Using Bayesian theory, the MTP can be transformed into an optimization problem by employing the target detection probability as the fitness function. The proposed HDE encodes the search trajectory as a sequence of UAV motion pathways that evolve with increasing the current iteration for finding the near-optimal solution, which could maximize this fitness function. The HDE is extensively compared to the classical DE and several rival optimizers in terms of several performance metrics across four different scenarios with varying degrees of difficulty. This comparison demonstrates the proposal’s superiority in terms of the majority of used performance metrics.

Escherichia coli O157:H7 is considered as an important pathogen of diarrhea in adult dogs and puppies because it contains virulence genes. The study objective was to the molecular detection of the rfbO157 encoding the O-antigen specific for E. coli O157: H7,shiga toxins and hemolysin genes of E.coli O157:H7 in feces of dogs that collected from different regions in Tikrit and Mosul cities, Iraq. One hundred fecal swabs were collected from pet and K9 dogs including (72 dogs with diarrhea, and 28 without diarrhea). All the Collected swabs were cultured in the nutrient and MacConkey agars, Then the suspected colonies were cultured in the EMB agar. Metallic sheen colonies were cultured by using the chrome agar. All bacteriological identified isolates were enrolled by using the polymerase chain reaction (PCR) technique. The results of this study showed that 7(9.7%) of 72 dogs suffered from diarrhea were positive for E. coli O157:H7 that contained the rfbO157 gene (n= 6), carry stx1 gene (n= 3), carry stx2 gene (n= 3), and hlyA gene (n= 1). On the other hand, 2 (7.1%) of 28 dogs without diarrhea were positive for E. coli O157:H7 that contained the rfbO157 gene (n= 1), stx2 gene (n= 1), and hlyA gene (n= 1). In conclusion, dogs can be a significant reservoir for pathogenic E. coli O157:H7, particularly dogs with diarrhea.

Bitcoin generates a massive amount of data every day due to its innate transparency and capacity of operating completely decentralised. In this paper, we introduce on-chain metrics derived from data on the bitcoin network that enable us to describe the state and usage of the underlying network. Based on their characteristics, we classify them into user, miner, exchange activities and run a correlation analysis with the price to understand the dynamics of bitcoin's price and its underlying mechanics. Using the correlated data, we develop a deep learning model. However, determining the best values of parameters in a deep learning model can be a very challenging and time-consuming task. Hence, we propose a self-adaptive technique using a jSO optimization algorithm to find the best values of these parameters to accurately predict the price of bitcoin. Compared to traditional LSTM model, our approach is highly accurate and optimised with a minimum error rate.

This paper proposes improvements to the binary grey-wolf optimizer (BGWO) to solve the feature selection (FS) problem associated with high data dimensionality, irrelevant, noisy, and redundant data that will then allow machine learning algorithms to attain better classification/clustering accuracy in less training time. We propose three variants of BGWO in addition to the standard variant, applying different transfer functions to tackle the FS problem. Because BGWO generates continuous values and FS needs discrete values, a number of V-shaped, S-shaped, and U-shaped transfer functions were investigated for incorporation with BGWO to convert their continuous values to binary. After investigation, we note that the performance of BGWO is affected by the selection of the transfer function. Then, in the first variant, we look to reduce the local minima problem by integrating an exploration capability to update the position of the grey wolf randomly within the search space with a certain probability; this variant was abbreviated as IBGWO. Consequently, a novel mutation strategy is proposed to select a number of the worst grey wolves in the population which are updated toward the best solution and randomly within the search space based on a certain probability to determine if the update is either toward the best or randomly. The number of the worst grey wolf selected by this strategy is linearly increased with the iteration. Finally, this strategy is combined with IBGWO to produce the second variant of BGWO that was abbreviated as LIBGWO. In the last variant, simulated annealing (SA) was integrated with LIBGWO to search around the best-so-far solution at the end of each iteration in order to identify better solutions. The performance of the proposed variants was validated on 32 datasets taken from the UCI repository and compared with six wrapper feature selection methods. The experiments show the superiority of the proposed improved variants in producing better classification accuracy than the other selected wrapper feature selection algorithms.

The Ethereum blockchain generates a significant amount of data due to its intrinsic transparency and decentralized nature. It is also referred to as on-chain data and is openly accessible to the world. Moreover, the on-chain data is timestamped, integrated, and validated into an open ledger. This important blockchain feature enables us to assess the network’s health and usage. It serves as a massive data warehouse for complex prediction algorithms that can effectively detect systemic trends and forecast future behavior. We adopt a quantitative approach using a subset of these metrics to determine the network’s true monetary value by developing a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) with the metrics most closely associated with the price as inputs. Since several hyperparameters regulate the learning process in an RNN, they are highly sensitive to their values. It is thus critical, to select optimal hyperparameters so that the training is quick and effective. Determining the optimal parameters of an RNN model is a tedious and complex process. Hence, previous studies have developed several self-adaptive approaches to determine the optimal values for various parameters effectively. However, none of the prior studies explore self-adaptive algorithms in deep learning models in conjunction with on-chain data to predict cryptocurrency prices. In this paper, we propose three self-adaptive techniques, each of which converges on a set of optimal parameters to predict the price of Ethereum accurately. We compare our results to a traditional LSTM model. Our approach exhibits 86.94% accuracy while maintaining a minimum error rate.

The goal of this research is the development of a driver assistant feature, which can warn the driver in case a pedestrian is in a potential risk due to sudden intention to cross the road. The process of crossing pedestrian is defined as the changing of pedestrian orientation on the curb toward the road. We built a Convolutional Neural Network (CNN) model combined with depth sensing camera to estimate the pedestrian orientation and distance from the vehicle. The model detects the higher human body keypoints in 2D space while the depth info make it possible to translate the points into a 3D space. These info are tracked per pedestrian and any change in the pedestrian moving pattern toward the road is translated to a warning for the driver. The CNN model is end-end trained using different datasets presenting pedestrian in different configurations and scenes.

Although several multi-operator and multi-method approaches for solving optimization problems have been proposed, their performances are not consistent for a wide range of optimization problems. Also, the task of ensuring the appropriate selection of algorithms and operators may be inefficient since their designs are undertaken mainly through trial and error. This research proposes an improved optimization framework that uses the benefits of multiple algorithms, namely, a multi-operator differential evolution algorithm and a co-variance matrix adaptation evolution strategy. In the former, reinforcement learning is used to automatically choose the best differential evolution operator. To judge the performance of the proposed framework, three benchmark sets of bound-constrained optimization problems (73 problems) with 10, 30 and 50 dimensions are solved. Further, the proposed algorithm has been tested by solving optimization problems with 100 dimensions taken from CEC2014 and CEC2017 benchmark problems. A real-world application data set has also been solved. Several experiments are designed to analyze the effects of different components of the proposed framework, with the best variant compared with a number of state-of-the-art algorithms. The experimental results show that the proposed algorithm is able to outperform all the others considered.

The study of deoxyribonucleic acid (DNA) is crucial in many fields, including medicine, biology, zoology, agriculture, and forensics. Since reading a DNA sequence is onerous because of its massive length, it is common in many DNA analysis applications to divide DNA strands into small segments or fragments which, after analysis, must be reassembled. Since this reassembly takes a non-specific polynomial time to solve, the DNA fragment assembly problem (DFAP) is NP-hard. This paper proposes a new assembler for tackling the DFAP based on the overlap-layout-consensus (OLC) approach. The proposed assembler adapts a discrete whale optimization algorithm (DWOA) using standard operators adopted from evolutionary algorithms to simulate the strategy adopted by humpback whales when searching for prey. For the first time, we formulate the behaviors of whales to be applied directly to any discrete optimization problem based on three primary operations: a swap-based best-position operator, an ordered crossover operator, and selection of a random whale operation to perform the exploitation and exploration phases of the algorithm. These operations were carefully designed to preserve the methodology of the original whale algorithm. DFAP is a multi-objective problem that seeks to reach the optimal order of segments that maximizes the overlap score and minimizes the number of contigs (set of overlapping DNA segments) to compose a one-contig DNA strand. Existing local search methods, such as problem aware local search (PALS) many non-conflicting movements (PALS2-many), suffer from being trapped in local optima. Hence, the integration of DWOA with PALS2-many improves the search capability for finding the optimal order of fragments. In addition, we propose a new variation of PALS2-many that achieves simultaneously the two objectives of DFAP. Our proposed DWOA was compared with a number of the most recent robust assemblers: a hybrid crow search algorithm for solving the DFAP (CSA-P2M*Fit), P2M*Fit, and a hybrid genetic algorithm (GA-P2M*Fit). The experimental results and statistical analyses of the proposed DWOA on thirty benchmark instances show that DWOA significantly outperforms those algorithms in reaching fewer contigs, in addition to being competitive with CSA-P2M*Fit and superior to P2M*Fit and GA-P2M*Fit for the overlap score.

Lung ultrasound images have shown great promise to be an operative point-of-care test for the diagnosis of COVID-19 because of the ease of procedure with negligible individual protection equipment, together with relaxed disinfection. Deep learning (DL) is a robust tool for modeling infection patterns from medical images; however, the existing COVID-19 detection models are complex and thereby are hard to deploy in frequently used mobile platforms in point-of-care testing. Moreover, most of the COVID-19 detection models in the existing literature on DL are implemented as a black box, hence, they are hard to be interpreted or trusted by the healthcare community. This paper presents a novel interpretable DL framework discriminating COVID-19 infection from other cases of pneumonia and normal cases using ultrasound data of patients. In the proposed framework, novel transformer modules are introduced to model the pathological information from ultrasound frames using an improved window-based multi-head self-attention layer. A convolutional patching module is introduced to transform input frames into latent space rather than partitioning input into patches. A weighted pooling module is presented to score the embeddings of the disease representations obtained from the transformer modules to attend to information that is most valuable for the screening decision. Experimental analysis of the public three-class lung ultrasound dataset (PCUS dataset) demonstrates the discriminative power (Accuracy: 93.4%, F1-score: 93.1%, AUC: 97.5%) of the proposed solution overcoming the competing approaches while maintaining low complexity. The proposed model obtained very promising results in comparison with the rival models. More importantly, it gives explainable outputs therefore, it can serve as a candidate tool for empowering the sustainable diagnosis of COVID-19-like diseases in smart healthcare.

Saving energy and cutting costs without compromising indoor comfort conditions are challenging, especially in hot and humid regions such as Malaysia. This study explores a new approach to reducing energy consumption without compromising staff comfort in office buildings. This study aims to develop a method for lowering Building Energy Index (BEI) and maintaining acceptable indoor conditions while increasing productivity in office buildings. A developed triangulation method using Building Use Studies (BUS) for evaluating occupant satisfaction, physical measurements, and simulation modelling was implemented to measure indoor performance in an office building. The results indicated that enhancing six variables of building conditions managed to improve occupant satisfaction by 44%. Hence, the productivity of staff in the building increased by 16%. The findings demonstrated that a reduction of 3 h in the operating times of chillers while an increase in chillers’ temperature by 1.5 °C maintained an acceptable indoor environment and reduced the building’s BEI to 89.48 kWh/m2/year, with an energy saving of 21.51%, turning the case study into a low energy building.

Task scheduling is one of the most significant challenges in the cloud computing environment and has attracted the attention of various researchers over the last decades, in order to achieve cost-effective execution and improve resource utilization. The challenge of task scheduling is categorized as a nondeterministic polynomial time (NP)-hard problem, which cannot be tackled with the classical methods, due to their inability to find a near-optimal solution within a reasonable time. Therefore, metaheuristic algorithms have recently been employed to overcome this problem, but these algorithms still suffer from falling into a local minima and from a low convergence speed. Therefore, in this study, a new task scheduler, known as hybrid differential evolution (HDE), is presented as a solution to the challenge of task scheduling in the cloud computing environment. This scheduler is based on two proposed enhancements to the traditional differential evolution. The first improvement is based on improving the scaling factor, to include numerical values generated dynamically and based on the current iteration, in order to improve both the exploration and exploitation operators; the second improvement is intended to improve the exploitation operator of the classical DE, in order to achieve better results in fewer iterations. Multiple tests utilizing randomly generated datasets and the CloudSim simulator were conducted, to demonstrate the efficacy of HDE. In addition, HDE was compared to a variety of heuristic and metaheuristic algorithms, including the slime mold algorithm (SMA), equilibrium optimizer (EO), sine cosine algorithm (SCA), whale optimization algorithm (WOA), grey wolf optimizer (GWO), classical DE, first come first served (FCFS), round robin (RR) algorithm, and shortest job first (SJF) scheduler. During trials, makespan and total execution time values were acquired for various task sizes, ranging from 100 to 3000. Compared to the other metaheuristic and heuristic algorithms considered, the results of the studies indicated that HDE generated superior outcomes. Consequently, HDE was found to be the most efficient metaheuristic scheduling algorithm among the numerous methods researched.

This paper contains two main parts, Part I and Part II, which discuss the local and global minimization problems, respectively. In Part I, a fresh conjugate gradient (CG) technique is suggested and then combined with a line-search technique to obtain a globally convergent algorithm. The finite difference approximations approach is used to compute the approximate values of the first derivative of the function f. The convergence analysis of the suggested method is established. The comparisons between the performance of the new CG method and the performance of four other CG methods demonstrate that the proposed CG method is promising and competitive for finding a local optimum point. In Part II, three formulas are designed by which a group of solutions are generated. This set of random formulas is hybridized with the globally convergent CG algorithm to obtain a hybrid stochastic conjugate gradient algorithm denoted by HSSZH. The HSSZH algorithm finds the approximate value of the global solution of a global optimization problem. Five combined stochastic conjugate gradient algorithms are constructed. The performance profiles are used to assess and compare the rendition of the family of hybrid stochastic conjugate gradient algorithms. The comparison results between our proposed HSSZH algorithm and four other hybrid stochastic conjugate gradient techniques demonstrate that the suggested HSSZH method is competitive with, and in all cases superior to, the four algorithms in terms of the efficiency, reliability and effectiveness to find the approximate solution of the global optimization problem that contains a non-convex function.

This paper introduces a novel physical-inspired metaheuristic algorithm called “Light Spectrum Optimizer (LSO)” for continuous optimization problems. The inspiration for the proposed algorithm is the light dispersions with different angles while passing through rain droplets, causing the meteorological phenomenon of the colorful rainbow spectrum. In order to validate the proposed algorithm, three different experiments are conducted. First, LSO is tested on solving CEC 2005, and the obtained results are compared with a wide range of well-regarded metaheuristics. In the second experiment, LSO is used for solving four CEC competitions in single objective optimization benchmarks (CEC2014, CEC2017, CEC2020, and CEC2022), and its results are compared with eleven well-established and recently-published optimizers, named grey wolf optimizer (GWO), whale optimization algorithm (WOA), and salp swarm algorithm (SSA), evolutionary algorithms like differential evolution (DE), and recently-published optimizers including gradient-based optimizer (GBO), artificial gorilla troops optimizer (GTO), Runge–Kutta method (RUN) beyond the metaphor, African vultures optimization algorithm (AVOA), equilibrium optimizer (EO), grey wolf optimizer (GWO), Reptile Search Algorithm (RSA), and slime mold algorithm (SMA). In addition, several engineering design problems are solved, and the results are compared with many algorithms from the literature. The experimental results with the statistical analysis demonstrate the merits and highly superior performance of the proposed LSO algorithm.

Summary: Introduction: Familial hypercholesterolaemia (FH) is a common, heritable and preventable cause of premature coronary artery disease, with significant potential for positive impact on public health and healthcare savings. New clinical practice recommendations are presented in an abridged guidance to assist practitioners in enhancing the care of all patients with FH. Main recommendations: Core recommendations are made on the detection, diagnosis, assessment and management of adults, children and adolescents with FH. There is a key role for general practitioners (GPs) working in collaboration with specialists with expertise in lipidology. Advice is given on genetic and cholesterol testing and risk notification of biological relatives undergoing cascade testing for FH; all healthcare professionals should develop skills in genomic medicine. Management is under-pinned by the precepts of risk stratification, adherence to healthy lifestyles, treatment of non-cholesterol risk factors, and appropriate use of low-density lipoprotein (LDL)-cholesterol lowering therapies, including statins, ezetimibe and proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors. Recommendations on service design are provided in the full guidance. Potential impact on care of FH: These recommendations need to be utilised using judicious clinical judgement and shared decision making with patients and families. Models of care need to be adapted to both local and regional needs and resources. In Australia new government funded schemes for genetic testing and use of PCSK9 inhibitors, as well as the National Health Genomics Policy Framework, will enable adoption of these recommendations. A broad implementation science strategy is, however, required to ensure that the guidance translates into benefit for all families with FH.

Model Predictive Control (MPC) has its reputation since it can handle multiple inputs and outputs with consideration to constraints. However, this comes at the cost of high computational complexity, which limits MPC to slow dynamic systems. This paper provides an overview of the available methods to accelerate the MPC process. Various parallel computing approaches using different technologies were proposed to speed up the execution of MPC, some of these approaches are focused on building dedicated hardware for MPC using field programmable arrays (FPGA), and others are focused on parallelizing MPC computation using multi-core processors (CPUs) and many-core processors (GPUs). The focus of this survey is to review the available methods for accelerating MPC process. A brief introduction to the theory of MPC is provided first followed by a description of each approach. A comparison between the different methods is presented in terms of complexity and performance followed by a valid application for each approach. Finally, this paper discusses the challenges and requirements of MPC for future applications.

Background: In mitral valve prolapse is associated with severe mitral regurgitation, mitral valve repair or surgical replacement may be necessary. In Mitral valve replacement, some surgeon use continuous suture, others use interrupted suture technique. Aim of the work: to compare efficacy of semi-continuous technique & interrupted suture of prosthestic valve replacement in mitral valve replacement. Patients and Methods: it was a prospective randomized comparative study done at the Cardiothoracic Surgery Department, Qena University Hospitals, South Valley University, Egypt between April 2016 to April 2018. It included 31 patients with rheumatic mitral valve disease whatever the cause. They were divided into group1: patients underwent semi-continuous technique for MVR (18 cases) and group2: patients underwent interrupted technique (13 cases).All of patients were subjected to preoperative data: complete history, general examination including general condition of the patient and vital signs, investigations including routine laboratory investigations, especially complete blood count, chest X ray (CXR), 12 lead Electrocardiography (ECG), echocardiography and coronary angiography if patient more than 40 years old, operative data including total bypass time, aortic CCT, number of sutures, and postoperative outcome including ICU stage and inotrope use, postoperative complications, pulmonary hypertension and echo findings as immediate postoperative and echo after 1 and 3 months postoperative. Results: We found that the mean total bypass time was significantly higher among interrupted group < br />compared to semicontinuous one. Conclusion: Semicontinuous sutures showed significantly better results than interrupted ones, especially regarding the total bypass time

Applications of the Internet of Things (IoT) are rapidly utilized in smart buildings and smart cities to reduce energy consumption. This advancement has caused a knowledge gap in applying IoT effectively by experts in the built environment to achieve energy efficiency. The study aims to provide an extensive review of IoT applications for energy savings in buildings and cities. This study contributes to the field of IoT by guiding and supporting built environment experts to utilize IoT technologies. This paper performed a thorough study using a systematic review that covered an overview of IoT concepts, models, applications, trends and challenges that can be encountered in the built environment. The findings indicated limitations in developing IoT strategies in buildings and cities by professionals in this field due to insufficient comprehension of technologies and their applied methods. Additionally, the study found an indefinite implementation and constraints on using IoT when integrated into the built environment. Finally, the study provides critical arguments and the next steps to effectively utilize IoT in terms of energy efficiency.

Metaheuristics are proven solutions for complex optimization problems. Recently, bio-inspired metaheuristics have shown their capabilities for solving complex engineering problems. The Whale Optimization Algorithm is a popular metaheuristic, which is based on the hunting behavior of whale. For some problems, this algorithm suffers from local minima entrapment. To make WOA compatible with a number of challenging problems, two major modifications are proposed in this paper: the first one is opposition-based learning in the initialization phase, while the second is inculcation of Cauchy mutation operator in the position updating phase. The proposed variant is named the Augmented Whale Optimization Algorithm (AWOA) and tested over two benchmark suits, i.e., classical benchmark functions and the latest CEC-2017 benchmark functions for 10 dimension and 30 dimension problems. Various analyses, including convergence property analysis, boxplot analysis and Wilcoxon rank sum test analysis, show that the proposed variant possesses better exploration and exploitation capabilities. Along with this, the application of AWOA has been reported for three real-world problems of various disciplines. The results revealed that the proposed variant exhibits better optimization performance.

Cyber-attacks are getting increasingly complex, and as a result, the functional concerns of intrusion-detection systems (IDSs) are becoming increasingly difficult to resolve. The credibility of security services, such as privacy preservation, authenticity, and accessibility, may be jeopardized if breaches are not detected. Different organizations currently utilize a variety of tactics, strategies, and technology to protect the systems’ credibility in order to combat these dangers. Safeguarding approaches include establishing rules and procedures, developing user awareness, deploying firewall and verification systems, regulating system access, and forming computer-issue management groups. The effectiveness of intrusion-detection systems is not sufficiently recognized. IDS is used in businesses to examine possibly harmful tendencies occurring in technological environments. Determining an effective IDS is a complex task for organizations that require consideration of many key criteria and their sub-aspects. To deal with these multiple and interrelated criteria and their sub-aspects, a multi-criteria decision-making (MCMD) approach was applied. These criteria and their sub-aspects can also include some ambiguity and uncertainty, and thus they were treated using q-rung orthopair fuzzy sets (q-ROFS) and q-rung orthopair fuzzy numbers (q-ROFNs). Additionally, the problem of combining expert and specialist opinions was dealt with using the q-rung orthopair fuzzy weighted geometric (q-ROFWG). Initially, the entropy method was applied to assess the priorities of the key criteria and their sub-aspects. Then, the combined compromised solution (CoCoSo) method was applied to evaluate six IDSs according to their effectiveness and reliability. Afterward, comparative and sensitivity analyses were performed to confirm the stability, reliability, and performance of the proposed approach. The findings indicate that most of the IDSs appear to be systems with high potential. According to the results, Suricata is the best IDS that relies on multi-threading performance.

Data mining applications are growing with the availability of large data; sometimes, handling large data is also a typical task. Segregation of the data for extracting useful information is inevitable for designing modern technologies. Considering this fact, the work proposes a chaos embed marine predator algorithm (CMPA) for feature selection. The optimization routine is designed with the aim of maximizing the classification accuracy with the optimal number of features selected. The well-known benchmark data sets have been chosen for validating the performance of the proposed algorithm. A comparative analysis of the performance with some well-known algorithms advocates the applicability of the proposed algorithm. Further, the analysis has been extended to some of the well-known chaotic algorithms; first, the binary versions of these algorithms are developed and then the comparative analysis of the performance has been conducted on the basis of mean features selected, classification accuracy obtained and fitness function values. Statistical significance tests have also been conducted to establish the significance of the proposed algorithm.

Water scarcity is a global issue, and its severity is expected to worsen in the near future, prompting further efforts to find new sources of freshwater. Solar-Powered Atmospheric Water Harvesting (SPAWH) is a promising passive approach for atmospheric water generation. This study aims to examine the thermal performance of different glazing materials and water production in SPAWH. The research consists of two phases: a laboratory test of various glazing materials and an experimental study to assess system efficiency in producing water in the tropics. The preliminary results indicated that glass demonstrated better thermal performance than acrylic in the lab, with higher thermal conductivity and less heat loss. The experimental findings showed that the maximum water produced by the proposed SPAWH (60 cm length, 60 cm width and 30 cm height) placed on a 30° tilt angle using glass (3 mm) and acrylic (3 mm) was 0.61 L/m2/day and 0.44 L/m2/day, respectively. The cost analysis revealed that produced water costs $0.18/kg for glass and $0.40/kg for acrylic, respectively. Atmospheric water could be harvested using SPAWH in the tropics, which would help to provide new opportunities for sustainable water supplies and development in these regions.

This study investigated an indirect evaporative cooling system (IECS) to control latent heat loss on roof ponds by increasing the evaporation rates on wet fabric membranes. The cooling potential of the proposed system was experimentally tested in a real environment and it was compared against a roof pond and a floating fiber (gunny bags) to provide an efficient model for buildings in hot and humid climates. Dry bulb temperatures (DBT) are presented for four experimental models. Solar irradiance, ambient and indoor dry bulb temperatures, and relative humidity (RH) were measured for seven days in each of the following climate conditions: hot sub-humid (mean DBT 27.3 °C and mean RH 72%), hot humid (mean DBT 27.1 °C and mean RH 81%), and warm sub-humid (mean DBT 25.2 °C and mean RH 68%). There were no significant variations in thermal performance between the examined devices under hot humid conditions; however, the wet fabric device had superior thermal performance under sub-humid conditions when compared to the other IECSs. In the three climatic scenarios where the proposed system was tested, the wet fabric managed to reduce the indoor air temperature by 6.6 °C, 5.3 °C, and 5.1 °C, respectively, as compared to the outdoor air temperatures.

Multiple doses of alemtuzumab for immunosuppressive therapy of patients with hematologic malignancies and hematopoietic stem cell transplant have been associated with a high rate of infection. In transplantation, limited alemtuzumab dosing has been successfully used as induction immunosuppression. The effect of multiple doses of alemtuzumab, used as maintenance therapy to minimize steroid and/or calcineurin inhibitor toxicity in solid organ transplant recipients, is unknown. We evaluated the infectious and noninfectious outcomes of 179 pancreas transplant recipients treated with alemtuzumab for induction and maintenance therapy (extended alemtuzumab exposure (EAE)) from 2/28/2003 through 8/31/2005, compared with 159 pancreas transplant recipients with standard induction and maintenance (SIM) therapy performed before (1/1/2002 until 12/31/2002) and after (1/1/2006 until 12/31/2006) the implementation of EAE. EAE was associated with higher risk of overall infections (hazard ratio (HR) 1.33 (1.06–1.66), P=0.01), bacterial infections (HR 1.33 (1.05–1.67), P=0.02), fungal infections (HR 1.86 (1.28–2.71), P

Initially, lipoprotein (a) [Lp(a)] was believed to be a genetic variant of lipoprotein (Lp)-B. Because its lipid moiety is almost identical to LDL, Lp(a) has been deliberately considered to be highly atherogenic. Lp(a) was detected in 1963 by Kare Berg, and individuals who were positive for this factor were called Lpa+. Lpa+ individuals were found more frequently in patients with coronary heart disease than in controls. After the introduction of quantitative methods for monitoring of Lp(a), it became apparent that Lp(a), in fact, is present in all individuals, yet to a greatly variable extent. The genetics of Lp(a) had been a mystery for a long time until Gerd Utermann discovered that apo(a) is expressed by a variety of alleles, giving rise to a unique size heterogeneity. This size heterogeneity, as well as countless mutations, is responsible for the great variability in plasma Lp(a) concentrations. Initially, we proposed to evaluate the risk of myocardial infarction at a cut-off for Lp(a) of 30–50 mg/dl, a value that still is adopted in numerous epidemiological studies. Due to new therapies that lower Lp(a) levels, there is renewed interest and still rising research activity in Lp(a). Despite all these activities, numerous gaps exist in our knowledge, especially as far as the function and metabolism of this fascinating Lp are concerned.

We report the case of a sixty-eight-year-old female patient who presented for left ankle pain; X-rays and MRI showed a benign calcaneal cyst, found to be delayed distant metastasis from primary papillary thyroid carcinoma. Patient required surgical excision of the cyst. Results of histological examination showed metastatic papillary thyroid carcinoma. The patient underwent total thyroidectomy 12 years prior to presentation, with the same pathology. Postoperatively, she was treated with radioactive iodine. At 10-year follow-up post calcaneal mass excision, she was found to have a right proximal tibial mass and found to be recurrent with the same pathology. This case reports a rare condition that will be taken into consideration in bone metastasis with thyroid cancer.