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The Master Surgery Scheduling Problem (MSSP) can be described as a timetabling problem involving assigning surgery groups to operating theatre (OT) time slots. Previous MSSP optimization models considered throughput, waiting measures, resource utilization, costs, and schedule assignment objectives, but have overlooked consecutive days assignment preferences and surgical equipment-sharing limitations. Furthermore, previous works utilize greedy constructive heuristics to produce solutions, which increases quality but decreases feasibility. Our prior study demonstrated that the saturation degree heuristic enhances feasibility by considering assignment difficulty during event selection. However, its impact on solution quality remained unexplored. Therefore, this study proposes an improved saturation degree-based constructive heuristic that integrates objective function value for event selection to increase both quality and feasibility. The algorithm sorts surgery groups by unit scores, prioritizing higher assignment difficulty and objective value. The highest-scoring group is assigned to its feasible slot with the highest slot score. If no feasible slots exist, the repair mechanism vacates the slot with the highest swap score, which prioritizes lower assignment difficulty and objective value. A new mathematical model is also formulated, incorporating novel objectives regarding consecutive days assignment preference and surgical equipment-sharing limitations. Using real-world data from Hospital Canselor Tuanku Muhriz, the proposed algorithm is evaluated considering repair mechanism usage for feasibility and objective function value for quality. The algorithm is benchmarked against greedy, random, regret-based, and saturation degree-based constructive heuristics. Our algorithm achieved a 14.63% improvement in feasibility compared to the original variant. Its objective function value is over two times better than the closest competitor and 2.6 times superior to the original variant. Comparison with the hospital’s actual plan demonstrates competitive objective function value and a more balanced waiting time distribution among surgical groups. Our study showcases that a saturation degree-based constructive heuristic considering objective function value has increased solution quality while maintaining feasibility.

One of the hardest processes encountered by petroleum refining is sulfur elimination from fuel oil. There are many traditional methods executed but they caused drawbacks such as poor selectivity of sulfur compounds and toxic raw materials. Extractive oxidative desulfurization (EODS) caught the interest of researchers due to high selectivity of sulfur compounds and great desulfurization. Currently, researchers are investigating the use of ionic liquids (ILs) as green extractant, unfortunately they are expensive. This research is proposing and comparing the use of cheap biodegradable solvents called deep eutectic solvents (DESs), as extractants in removing sulfur from fuel oil. The DESs are synthesized through a combination of choline chloride - orcinol and choline chloride - ethylene glycol, and their structure is confirmed through FTIR. Their thermal properties are characterized by DSC and TGA. Their desulfurization performance is evaluated by type of DESs, different ratios of DES and model oil, different ratios of oxidant and sulfone and various temperatures which these factors are found to influence the result. The optimum conditions are found to be at 1:1 for DES and model oil ratio, 4 for O/S ratio and temperature at 85 °C with the extraction efficiency of 99.98%. In conclusion, this DES has high potential to be the cheap green alternative to the conventional extractant for extractive oxidative desulfurization process.

The use of machine learning (ML) and data mining algorithms in the diagnosis of breast cancer (BC) has recently received a lot of attention. The majority of these efforts, however, still require improvement since either they were not statistically evaluated or they were evaluated using insufficient assessment metrics, or both. One of the most recent and effective ML algorithms, fast learning network (FLN), may be seen as a reputable and efficient approach for classifying data; however, it has not been applied to the problem of BC diagnosis. Therefore, this study proposes the FLN algorithm in order to improve the accuracy of the BC diagnosis. The FLN algorithm has the capability to a) eliminate overfitting, b) solve the issues of both binary and multiclass classification, and c) perform like a kernel-based support vector machine with a structure of the neural network. In this study, two BC databases (Wisconsin Breast Cancer Database (WBCD) and Wisconsin Diagnostic Breast Cancer (WDBC)) were used to assess the performance of the FLN algorithm. The results of the experiment demonstrated the great performance of the suggested FLN method, which achieved an average of accuracy 98.37%, precision 95.94%, recall 99.40%, F-measure 97.64%, G-mean 97.65%, MCC 96.44%, and specificity 97.85% using the WBCD, as well as achieved an average of accuracy 96.88%, precision 94.84%, recall 96.81%, F-measure 95.80%, G-mean 95.81%, MCC 93.35%, and specificity 96.96% using the WDBC database. This suggests that the FLN algorithm is a reliable classifier for diagnosing BC and may be useful for resolving other application-related problems in the healthcare sector.

Information retrieval is a difficult process due to the overabundance of information on the web. Nowadays, search result responds to user queries with too many results although only a few are relevant. Therefore, the existing clustering methods that fail in clustering snippets (short texts) of web documents due to the low frequencies of document terms should be deeply investigated. One of the approaches that can be used to solve this problem is the expansion of document terms with semantically similar terms. Hence, a list of terms with their closest and accurate semantically similar words (word representation) must be built. This study aims to design and develop a new framework to enhance the performance of web search result clustering (WSRC). The research also presents a new unsupervised distributed word representation scheme where each word is represented by a vector of its semantically related words; such as scheme expands snippets and user queries. The proposed framework consists of several activities, such as (1) various standard datasets (Open Directory Project [ODP]-239 and MORESQUE) that are used for evaluating search result clustering algorithms for most cited dataset works, (2) text pre-processing, (3) document representation based on a new wiki-based k-nearest neighbors (KNN) representation method, (4) effect of the proposed model on the performance of traditional clustering methods (k-means, k-medoids, single-linkage, and complete-linkage) for WSRC, and (5) evaluation stage of the proposed method. Results indicate that enhanced clustering methods, according to the new wiki-KNN based representation method in comparison with the baseline methods, show a significant improvement in WSRC. Furthermore, the new data representation scheme has enhanced the overall performance of clustering methods.

Deep learning models have shown great promise in diagnosing skeletal fractures from X-ray images. However, challenges remain that hinder progress in this field. Firstly, a lack of clear definitions for recognition, classification, detection, and localization tasks hampers the consistent development and comparison of methodologies. The existing reviews often lack technical depth or have limited scope. Additionally, the absence of explainable facilities undermines the clinical application and expert confidence in results. To address these issues, this comprehensive review analyzes and evaluates 40 out of 337 recent papers identified in prestigious databases, including WOS, Scopus, and EI. The objectives of this review are threefold. Firstly, precise definitions are established for the bone fracture recognition, classification, detection, and localization tasks within deep learning. Secondly, each study is summarized based on key aspects such as the bones involved, research objectives, dataset sizes, methods employed, results obtained, and concluding remarks. This process distills the diverse approaches into a generalized processing framework or workflow. Moreover, this review identifies the crucial areas for future research in deep learning models for bone fracture diagnosis. These include enhancing the network interpretability, integrating multimodal clinical information, providing therapeutic schedule recommendations, and developing advanced visualization methods for clinical application. By addressing these challenges, deep learning models can be made more intelligent and specialized in this domain. In conclusion, this review fills the gap in precise task definitions within deep learning for bone fracture diagnosis and provides a comprehensive analysis of the recent research. The findings serve as a foundation for future advancements, enabling improved interpretability, multimodal integration, clinical decision support, and advanced visualization techniques.

The Master Surgery Scheduling Problem (MSSP) allocates operating theatre time to surgery groups such as medical specialities or surgeons, which is essential for daily operational planners. Many researchers have highlighted issues in the optimization of surgical scheduling problems. However, most recent reviews limited the issues at the operational level and excluded the problem characteristics such as surgery group type and schedule cyclicity. This study aims to review the state-of-the-art of MSSP and identify new trends in optimization strategies (problem characteristics and objective function), uncertainty scheduling factors (types and approaches), and solution and evaluation methods. These aspects are the key components in developing an effective MSSP optimization model. This paper reviews articles published between 2000 and 2021 that addressed the MSSP, concentrating on papers between 2016 and 2021. We underlined the popularity of medical specialities as the surgery group, one-week horizon length, a cyclic schedule, multi-objective optimization and evaluation by benchmarking. The analysis shows that surgery duration is the most prominent uncertainty type, whereas strategies for handling this are stochastic programming, robust optimization, and fuzzy programming. We highlighted the role of heuristic approaches in addressing the MSSP’s computational complexity. This review’s trends, challenges, and potential solutions are essential for future researchers in developing optimization models for MSSP.

E-shopping is a form of electronic commerce used for business (B2B) and business (B2C) transactions. In Malaysia, it can be considered as infant and far from reaching its full potential. Millennials are the largest e-commerce buyers, with the age group consuming 54 percent of their goods online. Full time students recorded the highest smartphone users with 95.5%. Data collection was done by distributing self-designed questionnaires to 216 respondents. 95% of the questions are coming from the previous researchers done the similar topic and the raw data are being analyzed using IBM SPSS Statistics Subscription. The findings revealed that the perceived benefit is the most significant factors that influence the students toward online shopping. In details, there is relationship between perceived benefit, ambassador, price offered and ease of use with the online purchasing behavior except for trust on the seller. In conclusion, e-commerce or e-shopping has become convenience and reliable option for the students to get particular needs. 90% of the respondents had experienced online shopping beforehand. The benefit from online shopping is it enable everyone to conduct the transaction with the access of the internet and mobile phone. It is recommended for future researcher to conduct physical distribution of questionnaire directly to the potential students to get more convincing audience and population. Last but not least, as a result, vendors have found a new approach to create value for customers and build relationships with them towards marketing mix strategy and obtain more customer with the advance strategy and approach.

Many works have employed Machine Learning (ML) techniques in the detection of Diabetic Retinopathy (DR), a disease that affects the human eye. However, the accuracy of most DR detection methods still need improvement. Gray Wolf Optimization-Extreme Learning Machine (GWO-ELM) is one of the most popular ML algorithms, and can be considered as an accurate algorithm in the process of classification, but has not been used in solving DR detection. Therefore, this work aims to apply the GWO-ELM classifier and employ one of the most popular features extractions, Histogram of Oriented Gradients-Principal Component Analysis (HOG-PCA), to increase the accuracy of DR detection system. Although the HOG-PCA has been tested in many image processing domains including medical domains, it has not yet been tested in DR. The GWO-ELM can prevent overfitting, solve multi and binary classifications problems, and it performs like a kernel-based Support Vector Machine with a Neural Network structure, whilst the HOG-PCA has the ability to extract the most relevant features with low dimensionality. Therefore, the combination of the GWO-ELM classifier and HOG-PCA features might produce an effective technique for DR classification and features extraction. The proposed GWO-ELM is evaluated based on two different datasets, namely APTOS-2019 and Indian Diabetic Retinopathy Image Dataset (IDRiD), in both binary and multi-class classification. The experiment results have shown an excellent performance of the proposed GWO-ELM model where it achieved an accuracy of 96.21% for multi-class and 99.47% for binary using APTOS-2019 dataset as well as 96.15% for multi-class and 99.04% for binary using IDRiD dataset. This demonstrates that the combination of the GWO-ELM and HOG-PCA is an effective classifier for detecting DR and might be applicable in solving other image data types.

Bird mating optimizer (BMO) is a population-based metaheuristic that has been recently extended to solve combinatorial optimization problems. Even though the algorithm shows promising performance in solving combinatorial optimization problems, it suffers from slow convergence and poor efficiency which leads to poor solution quality for some problem instances. This is due to the limited capability of BMO in exploiting the search space and identifying more promising regions. Therefore, in this work we propose a hybrid BMO with five single-based metaheuristics: hill-climbing, late acceptance hill-climbing, simulated annealing, iterated greedy heuristic and variable iterated greedy heuristic. Each of these algorithms is used inside the BMO to exploit the search space, and improve the quality of solution generated from the BMO population. This work also compares which one of these five is better for hybridizing with BMO. The performance of these algorithms is tested on two combinatorial problems: travelling salesman problem and berth allocation problem. Experimental results demonstrate that the hybrid algorithm is superior to BMO when applied to both problems and it improved the BMO by 1.13% for BAP and by 4.13% for TSP. Furthermore, the hybrid algorithm is able to match the best-known results for most of the instances. In addition, the proposed hybrid approaches perform well over both tested domains and obtain competitive results when compared to the best-known results that have previously been presented in the scientific literature.

Composite steel-concrete construction has been widely implemented in high-rise buildings and bridges having long spans due to its favorable characteristics inherited from both materials acting as one unit. The main objective of this research is to propose a new beam system having a high strength-weight ratio, and to replace the shear studs used in composite beams by steel mesh wraps around the steel tube without any flexural or shear reinforcements. This new structural beam can be part of a lightweight-precast floor system with fast and easy ducting through its hollow tubular part. Four simply supported T-shaped beams of 3 m length are investigated experimentally to study and compare their flexural behavior. All beams are tested under two points quasi-static point loading. A comparison was initially performed between a control T-shaped reinforced concrete (RC) beam and a fully encased steel tube in a T-shaped RC beam of the same section dimensions as the control beam and without any mesh wrapping. The effect of full and partial wrapping of the steel tube was also investigated in the other two beams where 100 % and 60 % of the encased steel tube length were wrapped by a 3 mm steel mesh. Discussions and interpretation of the load-deflection behaviors and the failure modes are presented in this paper. The obtained results showed that the composite beam with unwrapped encased steel tubular section provided an advantage over the control RC beam in terms of load/weight ratio and ductility by 28.5 % and 22.4 % respectively. Besides, the use of steel mesh wraps in different length percentages revealed a much better partial composite action between the steel tube and the surrounding concrete. The attained strength ranged between 18.2 % and 33 %, whereas the ductility was enhanced by 63.8 % and up to 66.7 %.

As some of the objective functions are piecewise, so they are non-differentiable at specific points which have a significant impact on deep network rate and computational time. The non-differentiability issue increases the computational time dramatically. This issue is solved by the reformulation of the absolute value equation (AVE) through a parametrized single smooth equation. However, utilizing a single smoothing function is less effective to produce a better curve at the breaking points. Therefore, this work formulates a new smoothing function of Aggregation Fischer Burmister (AFB) via amalgamating of two popular smoothing functions: Aggregation (AGG) and Fischer-Burmeister (FB). These functions are having the ability of minimum estimation from both sides of the canonical piecewise function (CPF). If an amalgamation of smoothing functions can affect the differentiability of the piecewise objective function, then amalgamating the AGG and FB smoothing functions will produce a smooth secant line slope on both sides with less computational time. To evaluate the proposed technique, we implement a Newton algorithm using MATLAB, with random initial values. A new smoothing function is formulated by firstly converting the piecewise objective function to CPF. Then, we applied it to the Newton algorithm. Finally, to validate the AVE difficulty of the new piecewise function, we perform one run for each initial value, and 30 runs for time evaluation. The experimental analysis verified that the proposed technique outperformed other techniques of AGG and FB individually in terms of the natural logarithm, exponential, and square root. Hence, this novel technique yields promising smooth approximation for AVE with less computational time.

The bird mating optimizer is a new metaheuristic algorithm that was originally proposed to solve continuous optimization problems with a very promising performance. However, the algorithm has not yet been applied for solving combinatorial optimization problems. Thus, the formulation may not be able to generate a discrete feasible solution. Many continuous algorithms used random-key representation to represent the discrete solution using real numbers or a discrete variant of the algorithm is used to deal with the discrete solution of the problem. However, there is no evidence which methodology is better for solving combinatorial optimization problems. Therefore, this work proposes two variants of bird mating optimizer (random-key bird mating optimizer and the discrete bird mating optimizer), to identify which one is more efficient in solving combinatorial optimization problems. In the first one, we use a random-key encoding scheme, whilst, in the later one, we use crossover (multi-parent) and mutation operators to combine the components of the selected parents to generate new broods. The performance of these algorithms is tested on the travelling salesman problem and berth allocation problem, and are compared with the results of two well-known optimization algorithms: Genetic Algorithm and Particle Swarm Optimization. Experimental results show that the discrete bird mating optimizer is more efficient than the others on all tested benchmark instances. Indeed, it is able to attain the best-known results in some of the BAP benchmark instances. These indicate the applicability and the effectiveness of the proposed discrete bird mating optimizer in solving combinatorial optimization problems.

Data clustering algorithms experience challenges in identifying data points that are either noise or outlier. Hence, this paper proposes an enhanced connectivity measure based on the outlier detection approach for multi-objective data clustering problems. The proposed algorithm aims to improve the quality of the solution by utilising the local outlier factor method (LOF) with the connectivity validity measure. This modification is applied to select the neighbour data point’s mechanism that can be modified to eliminate such outliers. The performance of the proposed approach is assessed by applying the multi-objective algorithms to eight real-life and seven synthetic two-dimensional datasets. The external validity is evaluated using the F-measure, while the performance assessment matrices are employed to assess the quality of Pareto-optimal sets like the coverage and overall non-dominant vector generation. Our experimental results proved that the proposed outlier detection method has enhanced the performance of the multi-objective data clustering algorithms.

In this paper, the novel heuristic search algorithm called Smart Root Search (SRS) was examined for solving a set of different-sized service time–cost optimization in cloud computing service composition (STCOCCSC) problems, and its performance was compared with those of the ICACRO-C, ICACRO-I, ICA, and Niching PSO algorithms. STCOCCSC is an np-hard problem due to the large number of unique services available as well as the many service providers who provide services with different quality levels. Finding closer-to-optimal solutions supports cloud clients by providing them with higher quality-lower price services. The SRS obtained results proved that the SRS provided 6.74, 11.2, 47.95, and 87.29 percent performance improvement on average to the comparative algorithms, respectively, for all considered five problems. Furthermore, employing symmetry concepts in dividing the problem search space helps the algorithm to avoid premature convergence and any efficiency reduction while facing higher-dimensional search spaces. Due to these achievements, the SRS is a multi-purpose, flexible, and scalable heuristic search algorithm capable of being utilized in various optimization applications.

The purpose of this community service activity is to provide education on waste management to the students of the Quranic Recitation Hall Babussalam, Limpok Village. This community service activity was attended by 20 students, consisting of groups of primary and junior high schools, divided into two stages, namely: making the rubbish bin and counseling on waste management at the Quranic Recitation Hall Babussalam. The counseling and socialization were carried out by direct introduction to students and by providing waste collection facilities according to the types of waste. The results of this community service activity can be declared successful as seen from their seriousness in listening to the presentation of the material, responding spontaneously, and discussing seriously during the activity. They can understand the benefits of solid waste management and are proven to be able to mention the negative and positive effects of waste. Garbage can be an income if it is sorted out and a disaster if it is disposed of in any place according to them and an agent of change, especially in the environment where they live and the Limpok Village community in general. In conclusion, all participants had a good understanding of the introduction to waste processing methods, types of organic and inorganic waste, and residues which were marked by the ability of each participant to answer questions given by the service team.

The diversity and quality of high-quality and diverse-solution external memory of the hybrid Elitist-Ant System is examined in this study. The Elitist-Ant System incorporates an external memory for preserving search diversity while exploiting the solution space. Using this procedure, the effectiveness and efficiency of the search may be guaranteed which could consequently improve the performance of the algorithm and it could be well generalized across diverse problems of combinatorial optimization. The generality of this algorithm through its consistency and efficiency is tested using a Nurse-Rostering Problem. The outcomes demonstrate the competitiveness of the hybrid Elitist-Ant System’s performance within numerous datasets as opposed to those by other systems. The effectiveness of the external memory usage in search diversification is evidenced in this work. Subsequently, such usage improves the performance of the hybrid Elitist-Ant System over diverse datasets and problems. Keywords: Metaheuristics, Elitist-Ant System, External memory, Diversification, Intensification, Nurse Rostering Problem

The steganography is prone to number of attacks such as geomatical, salt & pepper, gaussian, median filtering, attacks. To overcome these problems, the cryptography and error correction codes are comes in the pictures and hybrid with steganography algorithms. The cryptography algorithms add one layer of security on steganography algorithms and error correction codes improves the robustness of steganography algorithms. On the other side, the hybridization of the algorithms, increase memory complexity and increase computation time for data embedding. Thus, in this paper a review on multi-layer algorithms for video steganography is done. This paper comprehensively reviews the steganography, spatial and frequency domain techniques hybrid with cryptography and error correction techniques. In-depth analysis on published literatures revealed that the spatial domain techniques require shorter computation time for data embedding and provide higher data hiding capacity than frequency domain methods. Performance of such techniques was evaluated in terms of visual quality and robustness parameters. Present challenges and future trends towards the improvement of the multi-layer data security in video steganography are discussed to provide the taxonomy for further navigation.

Existing text clustering methods utilize only one representation at a time (single view), whereas multiple views can represent documents. The multiview multirepresentation method enhances clustering quality. Moreover, existing clustering methods that utilize more than one representation at a time (multiview) use representation with the same nature. Hence, using multiple views that represent data in a different representation with clustering methods is reasonable to create a diverse set of candidate clustering solutions. On this basis, an effective dynamic clustering method must consider combining multiple views of data including semantic view, lexical view (word weighting), and topic view as well as the number of clusters. The main goal of this study is to develop a new method that can improve the performance of web search result clustering (WSRC). An enhanced multiview multirepresentation consensus clustering ensemble (MMCC) method is proposed to create a set of diverse candidate solutions and select a high-quality overlapping cluster. The overlapping clusters are obtained from the candidate solutions created by different clustering methods. The framework to develop the proposed MMCC includes numerous stages: (1) acquiring the standard datasets (MORESQUE and Open Directory Project-239), which are used to validate search result clustering algorithms, (2) preprocessing the dataset, (3) applying multiview multirepresentation clustering models, (4) using the radius-based cluster number estimation algorithm, and (5) employing the consensus clustering ensemble method. Results show an improvement in clustering methods when multiview multirepresentation is used. More importantly, the proposed MMCC model improves the overall performance of WSRC compared with all single-view clustering models.

The text clustering is considered as one of the most effective text document analysis methods, which is applied to cluster documents as a consequence of the expanded big data and online information. Based on the review of the related work of the text clustering algorithms, these algorithms achieved reasonable clustering results for some datasets, while they failed on a wide variety of benchmark datasets. Furthermore, the performance of these algorithms was not robust due to the inefficient balance between the exploitation and exploration capabilities of the clustering algorithm. Accordingly, this research proposes a Memetic Differential Evolution algorithm (MDETC) to solve the text clustering problem, which aims to address the effect of the hybridization between the differential evolution (DE) mutation strategy with the memetic algorithm (MA). This hybridization intends to enhance the quality of text clustering and improve the exploitation and exploration capabilities of the algorithm. Our experimental results based on six standard text clustering benchmark datasets (i.e. the Laboratory of Computational Intelligence (LABIC)) have shown that the MDETC algorithm outperformed other compared clustering algorithms based on AUC metric, F-measure, and the statistical analysis. Furthermore, the MDETC is compared with the state of art text clustering algorithms and obtained almost the best results for the standard benchmark datasets.

The coronavirus disease (COVID-19), is an ongoing global pandemic caused by severe acute respiratory syndrome. Chest Computed Tomography (CT) is an effective method for detecting lung illnesses, including COVID-19. However, the CT scan is expensive and time-consuming. Therefore, this work focus on detecting COVID-19 using chest X-ray images because it is widely available, faster, and cheaper than CT scan. Many machine learning approaches such as Deep Learning, Neural Network, and Support Vector Machine; have used X-ray for detecting the COVID-19. Although the performance of those approaches is acceptable in terms of accuracy, however, they require high computational time and more memory space. Therefore, this work employs an Optimised Genetic Algorithm-Extreme Learning Machine (OGA-ELM) with three selection criteria (i.e., random, K-tournament, and roulette wheel) to detect COVID-19 using X-ray images. The most crucial strength factors of the Extreme Learning Machine (ELM) are: (i) high capability of the ELM in avoiding overfitting; (ii) its usability on binary and multi-type classifiers; and (iii) ELM could work as a kernel-based support vector machine with a structure of a neural network. These advantages make the ELM efficient in achieving an excellent learning performance. ELMs have successfully been applied in many domains, including medical domains such as breast cancer detection, pathological brain detection, and ductal carcinoma in situ detection, but not yet tested on detecting COVID-19. Hence, this work aims to identify the effectiveness of employing OGA-ELM in detecting COVID-19 using chest X-ray images. In order to reduce the dimensionality of a histogram oriented gradient features, we use principal component analysis. The performance of OGA-ELM is evaluated on a benchmark dataset containing 188 chest X-ray images with two classes: a healthy and a COVID-19 infected. The experimental result shows that the OGA-ELM achieves 100.00% accuracy with fast computation time. This demonstrates that OGA-ELM is an efficient method for COVID-19 detecting using chest X-ray images.

In multilabel classification, each sample can be allocated to multiple class labels at the same time. However, one of the prominent problems of multilabel classification is missing labels (incomplete labels) in multilabel text. The multilabel classification performance is reduced significantly with the presence of missing labels. In order to address the incomplete or missing label problem, this study proposes two methods: an aggregated feature and label graph-based missing label handling method (GB-AS), and a unified graph-based missing label propagation method (UG-MLP). GB-AS is used to obtain an initial label matrix based on the similarity of both document levels: feature-based weighting representation and label-based weighting representation. On the other hand, UG-MLP is introduced to construct a mixed graph that combines GB-AS and label correlations into a single groundwork. A high-order label correlation is learned from the incomplete training data and applied to supplement the missing label matrix, which guides the creation of multilabel classification models. The combination of the mixed graphs by UG-MLP is aimed to obtain the benefits of both graphs to increase the classification performance. To evaluate UG-MLP, the metrics of precision, recall and F-measure were used on three benchmark datasets, namely, the Reuters-21578, Bibtex and Enron datasets. The experimental results show that UG-MLP outperformed GB-AS as well as other state-of-the-art approaches. Therefore, we can infer from the findings that by plotting a unified graph based on joining aggregated feature and label weightings together with the label correlation, the performance of multilabel classification can be improved.

Fouad Fayad,1 Nelly R Ziade,1 Georges Merheb,2 Said Attoui,3 Alla Aiko,4 Kamel Mroue,3 Abdel Fattah Masri5 1Rheumatology Department, Hotel Dieu de France Hospital, Saint Joseph University, Beirut, Lebanon; 2Department of Rheumatology, Notre Dame des Secours University Hospital, Jbeil, Lebanon, Holy Spirit University, Kaslik, Lebanon; 3Rheumatology Department, Hammoud University Medical Center, Saida, Lebanon; 4Department of Medicine (Rheumatology), Saint George Hospital, Beirut, Lebanon; 5Department of Internal Medicine (Rheumatology), American University of Beirut Medical Center, Beirut, Lebanon Introduction: To investigate the treatment preferences of patients with rheumatoid arthritis (RA) and determine whether these preferences are related to specific disease characteristics. Method: A national survey was designed to collect demographic, disease, treatment, and preference data on RA patients enrolled in 7 private and university hospital clinics in Lebanon. Associations between patient factors and treatment preferences for RA were analyzed by χ2 or Mann–Whitney U test. Results: A total of 693 patients (83% female; 67% aged 41–70 years) consulting 7 trained rheumatologists completed the survey. Most patients (80%) had established RA >24 months, and approximately one-third (34%) were in remission according to the disease activity score in 28 joints (DAS28). Most (87%) were receiving oral agents (60% oral only). Almost two-thirds of patients (64%) expressed a preference for oral treatments, and more than half (53%) ranked doctor’s advice as the most influential factor when choosing treatment. In univariable analysis, health coverage, radiographic damage, disease duration, current therapy, and previous side effects were significantly associated with treatment preference. In multivariable analyses, only radiographic damage and current route of administration were independently associated with preference (both P

The performance of data clustering algorithms is mainly dependent on their ability to balance between the exploration and exploitation of the search process. Although some data clustering algorithms have achieved reasonable quality solutions for some datasets, their performance across real-life datasets could be improved. This paper proposes an adaptive memetic differential evolution optimisation algorithm (AMADE) for addressing data clustering problems. The memetic algorithm (MA) employs an adaptive differential evolution (DE) mutation strategy, which can offer superior mutation performance across many combinatorial and continuous problem domains. By hybridising an adaptive DE mutation operator with the MA, we propose that it can lead to faster convergence and better balance the exploration and exploitation of the search. We would also expect that the performance of AMADE to be better than MA and DE if executed separately. Our experimental results, based on several real-life benchmark datasets, shows that AMADE outperformed other compared clustering algorithms when compared using statistical analysis. We conclude that the hybridisation of MA and the adaptive DE is a suitable approach for addressing data clustering problems and can improve the balance between global exploration and local exploitation of the optimisation algorithm.

The Container Pre-marshalling Problem (CPMP) has the significant effect of reducing ship berthing time, and can help in increasing terminal turnover rate. In order to solve the CPMP, this research proposes a Multi-objectives Memetic Discrete Differential Evolution algorithm (MODDE). To date, existing research in CPMP only focuses on single-objective approaches. However, this is not a suitable approach due to the considerable effort required to validate the hard constraints of CPMP. Therefore, this work aims at addressing the effect of minimizing the number of miss-overlaid containers on the total number of movements in building the final feasible bay layout by embedding it in the multi-objectives evaluation function. The proposed algorithm combines the Discrete Differential Evolution mutation with the Memetic Algorithm evolutionary steps in order to find high quality CPMP solutions, achieve high convergence rate and avoid premature convergence and local optima problems. In addition, it improves the exploration and exploitation capabilities of the algorithm. The standard pre-marshalling benchmark dataset (i.e., Bortfeldt-Forster) is used to evaluate the effectiveness of the proposed algorithm. The experimental results reveal that the proposed MODDE algorithm can find good solutions on instances of the standard pre-marshalling benchmarks. This demonstrates that using the multi-objectives approach with a combination of the Discrete Differential Evolution mutation and the Memetic Algorithm evolutionary is a suitable approach for solving multi-objectives CPMP.

Having the best solution for Vehicle Routing Problem (VRP) is still in demand. Beside, Cuckoo Search (CS) is a popular metaheuristic based on the reproductive strategy of the Cuckoo species and has been successfully applied in various optimizations, including Capacitated Vehicle Routing Problem (CVRP). Although CS and hybrid CS have been proposed for CVRP, the performance of CS is far from the state-of-art. Therefore, this study proposes a hybrid CS with Simulated Annealing (SA) algorithm for the CVRP, consisting of three improvements—the investigation of 12 neighborhood structures, three selections strategy and hybrid it with SA. The experiment was conducted using 16 instances of the Augerat benchmark dataset. The results show that 6 out of 12 neighborhood structures were the best and the disruptive selection strategy is the best strategy. The experiments’ results showed that the proposed method could find optimal and near-optimal solutions compared with state-of-the-art algorithms.

In this paper, a novel heuristic search algorithm called Smart Root Search (SRS) is proposed. SRS employs intelligent foraging behavior of immature, mature and hair roots of plants to explore and exploit the problem search space simultaneously. SRS divides the search space into several subspaces. It thereupon utilizes the branching and drought operations to focus on richer areas of promising subspaces while extraneous ones are not thoroughly ignored. To achieve this, the smart reactions of the SRS model are designed to act based on analyzing the heterogeneous conditions of various sections of different search spaces. In order to evaluate the performance of the SRS, it was tested on a set of known unimodal and multimodal test functions. The results were then compared with those obtained using genetic algorithms, particle swarm optimization, differential evolution and imperialist competitive algorithms and then analyzed statistically. The results demonstrated that the SRS outperformed comparative algorithms for 92% and 82% of the investigated unimodal and multimodal test functions, respectively. Therefore, the SRS is a promising nature-inspired optimization algorithm.

The metaheuristic genetic algorithm (GA) is based on the natural selection process that falls under the umbrella category of evolutionary algorithms (EA). Genetic algorithms are typically utilized for generating high-quality solutions for search and optimization problems by depending on bio-oriented operators such as selection, crossover, and mutation. However, the GA still suffers from some downsides and needs to be improved so as to attain greater control of exploitation and exploration concerning creating a new population and randomness involvement happening in the population at the solution initialization. Furthermore, the mutation is imposed upon the new chromosomes and hence prevents the achievement of an optimal solution. Therefore, this study presents a new GA that is centered on the natural selection theory and it aims to improve the control of exploitation and exploration. The proposed algorithm is called genetic algorithm based on natural selection theory (GABONST). Two assessments of the GABONST are carried out via (i) application of fifteen renowned benchmark test functions and the comparison of the results with the conventional GA, enhanced ameliorated teaching learning-based optimization (EATLBO), Bat and Bee algorithms. (ii) Apply the GABONST in language identification (LID) through integrating the GABONST with extreme learning machine (ELM) and named (GABONST-ELM). The ELM is considered as one of the most useful learning models for carrying out classifications and regression analysis. The generation of results is carried out grounded upon the LID dataset, which is derived from eight separate languages. The GABONST algorithm has the capability of producing good quality solutions and it also has better control of the exploitation and exploration as compared to the conventional GA, EATLBO, Bat, and Bee algorithms in terms of the statistical assessment. Additionally, the obtained results indicate that (GABONST-ELM)-LID has an effective performance with accuracy reaching up to 99.38%.

The Container Pre-marshalling Problem (CPMP) has the significant effect of reducing ship berthing time, and can help in increasing terminal turnover rate. In order to solve the CPMP, this research proposes a Multi-objectives Memetic Discrete Differential Evolution algorithm (MODDE). To date, existing research in CPMP only focuses on single-objective approaches. However, this is not a suitable approach due to the considerable effort required to validate the hard constraints of CPMP. Therefore, this work aims at addressing the effect of minimizing the number of miss-overlaid containers on the total number of movements in building the final feasible bay layout by embedding it in the multi-objectives evaluation function. The proposed algorithm combines the Discrete Differential Evolution mutation with the Memetic Algorithm evolutionary steps in order to find high quality CPMP solutions, achieve high convergence rate and avoid premature convergence and local optima problems. In addition, it improves the exploration and exploitation capabilities of the algorithm. The standard pre-marshalling benchmark dataset (i.e., Bortfeldt-Forster) is used to evaluate the effectiveness of the proposed algorithm. The experimental results reveal that the proposed MODDE algorithm can find good solutions on instances of the standard pre-marshalling benchmarks. This demonstrates that using the multi-objectives approach with a combination of the Discrete Differential Evolution mutation and the Memetic Algorithm evolutionary is a suitable approach for solving multi-objectives CPMP.

In video surveillance scheme, counting individuals is regarded as a crucial task. Of all the individual counting techniques in existence, the regression technique can offer enhanced performance under overcrowded area. However, this technique is unable to specify the details of counting individual such that it fails in locating the individual. On contrary, the density map approach is very effective to overcome the counting problems in various situations such as heavy overlapping and low resolution. Nevertheless, this approach may break down in cases when only the heads of individuals appear in video scenes, and it is also restricted to the feature’s types. The popular technique to obtain the pertinent information automatically is Convolutional Neural Network (CNN). However, the CNN based counting scheme is unable to sufficiently tackle three difficulties, namely, distributions of non-uniform density, changes of scale and variation of drastic scale. In this study, we cater a review on current counting techniques which are in correlation with deep net in different applications of crowded scene. The goal of this work is to specify the effectiveness of CNN applied on popular individuals counting approaches for attaining higher precision results.

Abstract Most autoinflammatory diseases are caused by mutations in innate immunity genes. Previously, four variants in the RHO GTPase CDC42 were discovered in patients affected by syndromes generally characterized by neonatal-onset of cytopenia and auto-inflammation, including hemophagocytic lymphohistiocytosis and rash in the most severe form (NOCARH syndrome). However, the mechanisms responsible for these phenotypes remain largely elusive. Here, we show that the recurrent p.R186C CDC42 variant, which is trapped in the Golgi apparatus, elicits a block in both anterograde and retrograde transports. Consequently, it favours STING accumulation in the Golgi in a COPI-dependent manner. This is also observed for the other Golgi-trapped p.*192 C*24 CDC42 variant, but not for the p.Y64C and p.C188Y variants that do not accumulate in the Golgi. We demonstrate that the two Golgi-trapped CDC42 variants are the only ones that exhibit overactivation of the STING pathway and the type I interferon response, and elicit endoplasmic reticulum stress. Consistent with these results, patients carrying Golgi-trapped CDC42 mutants present very high levels of circulating IFNα at the onset of their disease. In conclusion, we report further mechanistic insights on the impact of the Golgi-trapped CDC42 variants. This increase in STING activation provides a rationale for combination treatments for these severe cases.

Abstract Denosumab is a monoclonal anti-RANKL antibody that inhibits bone resorption, increases bone mass, and reduces fracture risk. Denosumab discontinuation causes an extensive wave of rebound resorption, but the cellular mechanisms remain poorly characterized. We utilized in situ hybridization (ISH) as a direct approach to identify the cells that activate osteoclastogenesis through the RANKL/OPG pathway. ISH was performed across species, skeletal sites, and following recombinant OPG (OPG:Fc) and parathyroid hormone 1–34 (PTH) treatment of mice. OPG:Fc treatment in mice induced an increased expression of RANKL mRNA mainly in trabecular, but not endocortical bone surface cells. Additionally, a decreased expression of OPG mRNA was detected in bone surface cells and osteocytes of both compartments. A similar but more pronounced effect on RANKL and OPG expression was seen one hour after PTH treatment. These findings suggest that bone surface cells and osteocytes conjointly regulate the activation of osteoclastogenesis, and that OPG:Fc treatment induces a local accumulation of osteoclastogenic activation sites, ready to recruit and activate osteoclasts upon treatment discontinuation. Analysis of publicly available single-cell RNA sequencing (scRNAseq) data from murine bone marrow stromal cells revealed that Tnfsf11 + cells expressed high levels of Mmp13, Limch1, and Wif1, confirming their osteoprogenitor status. ISH confirmed co-expression of Mmp13 and Tnfsf11 in bone surface cells of both vehicle- and OPG:Fc-treated mice. Under physiological conditions of human/mouse bone, RANKL is expressed mainly by osteoprogenitors proximate to the osteoclasts, while OPG is expressed mainly by osteocytes and bone-forming osteoblasts.

Maize is widely growth in arid and semi-arid region where, drought is common and a limiting factor for crop production. Potassium plays a key role in enhancing plant growth under drought condition. The objective of this study is to determine the effect of K fertilization with and without NP on maize growth grown in sandy loam soil under adequate and deficit irrigation conditions. The following treatments were investigated in pot experiment: (1) control with no fertilizer application (C); (2) 128 kg N + 328 kg P2O5 ha-1 (NPK0); (3) 128 kg N + 328 kg P2O5 ha-1 + 152.5 kg K2O ha1 (NPK1); (4) 128 kg N + 328 kg P2O5 ha-1 + 305 kg K2O ha-1 (NPK2); and 128 kg N + 328 kg P2O5 ha-1 + 457.5 kg K2O ha-1 (NPK3). Treatments were investigated under adequate and deficit soil moisture content. Each pot filled with 3.5 kg air-dry soil and seeded with maize and pots were watered according to the treatments. The results indicated that plant growth and nutrient uptake were significantly reduced under water stress condition. The application of NP increased plant growth and nutrient uptake and further were increased with K application. K application also enhanced plant tolerance to deficit soil moisture condition. In addition, K enhanced nutrient uptake and leaf chlorophyll content. Based on the results, it can be concluded that application of NP for maize was not adequate to achieve the highest plant growth, unless it is combined with K application. In addition, K application enhances plant tolerance to water stress.

Hikmat Abdel-Razeq,1,2 Baha Sharaf,1 Suhaib Khater,1 Huda Jafar Baidoun,2 Hira Bani Hani,1 Ayat Taqash,3 Osama El Khatib,1 Sarah Edaily,1 Mahmoud Abunasser,1 Faris Tamimi,1 Yosra Nabeel Al-Masri,1 Tamer Moh’d Waleed Al-Batsh,1 Anas Zayed,1 Tala Ghatasheh,1 Tala Radaideh1 1Department of Internal Medicine, Section of Hematology and Medical Oncology, King Hussein Cancer Center, Amman, Jordan; 2School of Medicine, the University of Jordan, Amman, Jordan; 3Office of Scientific Affairs and Research. King Hussein Cancer Center, Amman, JordanCorrespondence: Hikmat Abdel-Razeq, Department of Internal Medicine, Section of Hematology and Medical OncologyKing Hussein Cancer Center, 202 Queen Rania Al Abdullah Street, P.O. Box: 1269, Amman, 11941, Jordan, Tel: +962-6 5300460, Email habdelrazeq@khcc.joBackground: Cyclin-dependent kinase (CDK) 4/6 inhibitors have revolutionized the treatment landscape of hormone receptor-positive (HR+) and human epidermal growth factor receptor 2-negative (HER2 −) metastatic breast cancer (MBC). Here, we present the real-world clinical outcomes and toxicity data of patients treated at a single cancer center.Methods: A retrospective analysis was conducted on patients with HR+/HER2− MBC treated with ribociclib plus endocrine therapy (ET). Outcomes measured included progression-free survival (PFS), overall survival (OS), and adverse events.Results: A total of 356 patients (median age 52, range 27– 91 years) were enrolled, all with metastatic disease; 204 (57.5%) had de novo metastasis, and 183 (51.4%) had visceral metastasis. Ribociclib was combined with aromatase inhibitors in 321 patients (90.2%) and with fulvestrant in 35 patients (9.8%). Dose reduction was needed in 101 patients (28.4%), primarily due to neutropenia (21.3%) and abnormal liver enzymes (5.9%). After a median follow-up of 36.3 months, median PFS was 27.3 months (95% CI: 21.3– 31.7). PFS was significantly better in patients receiving ribociclib as first-line therapy (32.1 months, 95% CI: 27.7– 42.1, p < 0.0001) and those with non-visceral metastasis (38.6 months, 95% CI: 29.8–NR, p < 0.0001). Similarly, OS was significantly better in first-line treatment (48.6 months, 95% CI: 39.1–NR) and non-visceral metastasis cases (NR, 95% CI: 40.6–NR, p < 0.0001). No significant differences in 3-year PFS and OS were found between patients with and without dose reductions.Conclusion: In real-world settings, and away from the stringency of controlled clinical trials, endocrine therapy in combination with ribociclib in patients with HR-positive/HER2-negative MBC is an effective and well-tolerated therapy with a manageable toxicity profile and a low drug discontinuation rate. Dose reduction due to toxicity did not worsen the outcome.Keywords: metastatic breast cancer, MBC, CDK4/6 inhibitors, ribociclib, aromatase inhibitors, fulvestrant

Abstract Medullary carcinoma of the breast (MedBC) is a rare histological type that accounts for less than 5% of all invasive breast cancers. Here, we performed an exploratory study aimed to determine whether imaging mass cytometry (IMC) can be used to characterize the immune infiltration and the spatial distribution heterogeneity in the rare subtype of MedBC compared to atypical MedBC and TNBC-TILS+ tumors. In both MedBC and TNBC-TILs+, there was a notable enrichment of immune cells in the peripheral regions of the tumors, whereas in atypical MedBC, the immune cells exhibited a central enrichment pattern. This distribution of infiltrated cells reflects an active immune recruitment correlated to more favorable prognosis. In MedBC, spatial analysis shows that immune cells are localized at a greater distance from the tumor cells. IMC highlights the heterogeneity of immune microenvironment across three main subtypes of breast tumors and could help to define distinct immune patterns.

A Chance fracture, also known as a “seatbelt fracture,” is “an unstable spinal injury that usually occurs at the thoracolumbar junction. Complications of a Chance fracture include spinal cord injury, neurological deficits, and long-term functional impairment if not promptly identified and treated. In this case report, we present the case of a 13-year-old patient who sustained a Chance fracture following a 30-foot vertical fall, an atypical mechanism of injury. Imaging revealed a vertebral body fracture with a horizontal break through the posterior elements and accompanying ligamentous tears. Management included surgical stabilization via pedicle screw fixation and laminectomy. This case highlights the importance of considering axial loading mechanisms in pediatric trauma patients and the role of timely surgical intervention in preventing long-term complications.

A thyroidectomy is a safe and low-risk procedure done for a wide variety of thyroid disorders. Complications of a thyroidectomy are commonly due to damage of structures adjacent to the thyroid gland. In this case report, we present the case of a 39-year-old patient with a cervical pneumatocele a month after thyroidectomy for thyroid papillary carcinoma. The symptoms included neck swelling when speaking that resolved with rest. A follow-up neck CT scan found a collection of air anterior to the trachea most compatible with cervical pneumatocele. Exploratory surgery ensued and a pneumatocele arising from a tracheal subcutaneous fistula was cauterized. On follow-up laryngoscopy a month later, no further complications were found, and the patient reported resolution of symptoms. Tracheal injury following thyroidectomy is a rare complication and CT imaging can be helpful in diagnosing these complications and expediting management.

In current toxicological research, 2D cell cultures and animal models are well- accepted and commonly employed methods. However, these approaches have many drawbacks and are distant from the actual environment in human. To embrace this, great efforts have been made to provide alternative methods for non-animal skin models in toxicology studies with the need for more mechanistically informative methods. This review focuses on the current state of knowledge regarding the in vitro 3D skin model methods, with different functional states that correspond to the sustainability in the field of toxicology testing. We discuss existing toxicology testing methods using in vitro 3D skin models which provide a better understanding of the testing requirements that are needed. The challenges and future landscape in using the in vitro 3D skin models in toxicology testing are also discussed. We are confident that the in vitro 3D skin models application may become an important tool in toxicology in the context of risk assessment.

Pneumocephalus, is a general term describing the presence of air within the intracranial structures. It most commonly occurs due to dural injury, often the sequela of head trauma or surgery. More infrequently, nontraumatic pneumocephalus can be related to infection, Valsalva, fistulization between air-containing organs and intracranial structures, or vascular air embolism. While postsurgical pneumocephalus is often benign, serious consequences of pneumocephalus exist, including tension pneumocephalus and cerebral infarction. We present a case of air embolism and cerebral infarction in a patient with Ehlers-Danlos syndrome, found to have large cavitary lesion in the left upper lobe of the lung, with associated pulmonary vascular malformation seen on bronchoscopy. To our knowledge this is the first reported case of air embolism associated with a pulmonary cavitary lesion and vascular malformation, in a patient with Ehlers-Danlos syndrome.

This review article provided a thorough examination of porous ceramic materials, concentrating on production, characteristics, and the involvement of pore-forming agents. The primary objective of this research was to evaluate the effects of various ceramic materials and pore-forming agents on the structure, porosity, and mechanical characteristics of porous ceramics. The study's scope included a thorough investigation of key sources of literature, such as academic publications, review articles, and industry reports, to provide a comprehensive understanding of porous ceramic technology. According to the literature review, the selection of ceramic material and pore-forming agents has a significant influence on the pore size distribution, porosity, and mechanical strength of porous ceramics. Various manufacturing methods, including foaming, sintering, and sol-gel procedures, were explored in terms of their influence on porous ceramic microstructure and characteristics. Furthermore, the study emphasized the need to optimize processing settings and select pore-forming agents to obtain the necessary qualities in porous ceramic materials. Overall, this review is useful for researchers, engineers, and practitioners who desire to learn more about porous ceramic manufacturing, characteristics, and applications.

Introduction: The present study reports the first clinical experience with subcostal uniportal VATS (suVATS) compared with the conventional lateral uniportal VATS (luVATS) approach. Patients and Methods: All patients who underwent suVATS between January 2019 and April 2020 were included. Patients who had undergone luVATS for similar indications were included as the control group. The data were prospectively and retrospectively analysed. Results: The suVATS group included 38 patients with a mean age of 61 (30–83) years. The luVATS group included 33 patients (mean age, 69 years; range: 46–89 years). An intercostal block was performed intraoperatively in the luVATS group. Local infiltration under anaesthesia was performed around the incision in the suVATS group. The duration of the surgery was significantly longer in the suVATS group. However, the chest tube treatment and hospital stay duration were significantly shorter in the suVATS group. The routinely recorded Visual Analogue Scale scores on the first post-operative day and the day of discharge were significantly lower in the suVATS group. Conclusion: Subcostal uniportal VATS enables a shorter drainage treatment duration and hospital stay and significantly reduces post-operative pain. Thus, a faster patient recovery can be achieved.

Abstract Background Randomised controlled trials (RCTs) are typically designed to determine beneficial intervention effects. In addition, an important aspect of every trial is to collect data on any potential harmful effects, with the aim of ensuring that the benefit-risk balance is appropriate. The language used by trialists to describe these potential harmful effects is inconsistent. In pharmacological trials, researchers collect adverse events; when a causal relationship is suspected adverse events are further classified as adverse reactions. Academic researchers have moved to collectively refer to these as harm outcomes; the pharmaceutical industry refer to these events as safety outcomes. In trials of complex interventions, phrases such as unintended consequences or effects are used. With the inconsistent use of terminology by researchers and the potential benefits to be gained from harmonising communications, we sought public opinion on terminology used to describe harmful effects and how these outcomes are communicated in the scientific literature, as well as in public facing material on medications. Methods We held two in-person public involvement meetings with public partners, in London and Aberdeen in 2023. Both meetings followed a pre-specified format. We provided a background to the topic including the information researchers collect on potential harms in clinical trials and shared examples on how this information gets presented in practice. We then discussed public partners’ perspectives on terminology used and communication of intervention harm in academic journals and in public facing materials. A summary of these discussions and the main topics raised by public partners are presented. Results Public partners endorsed the use of different terms for different situations, preferring the use of ‘side-effect’ across all contexts and reserving the use of ‘harm’ to indicate more severe events. Generally, public partners were happy with the type of information presented in public facing materials but discussions revealed that presentation of information on public NHS websites led to misconceptions about harm. Conclusion This work provides a starting point on preferred terminology by patients and the public to describe potential harmful intervention effects. Whilst researchers have tried to seek agreement, public partners endorsed use of different terms for different situations. We highlight some key areas for improvement in public facing materials that are necessary to avoid miscommunication and incorrect perception of harm.

Abstract Background Without increasing radiation exposure, ultralow-dose computed tomography (CT) of the chest provides improved diagnostic accuracy of radiological pneumonia diagnosis compared to a chest radiograph. Yet, radiologist resources to rapidly report the chest CTs are limited. This study aimed to assess the diagnostic accuracy of emergency clinicians’ assessments of chest ultralow-dose CTs for community-acquired pneumonia using a radiologist’s assessments as reference standard. Methods This was a cross-sectional diagnostic accuracy study. Ten emergency department clinicians (five junior clinicians, five consultants) assessed chest ultralow-dose CTs from acutely hospitalised patients suspected of having community-acquired pneumonia. Before assessments, the clinicians attended a focused training course on assessing ultralow-dose CTs for pneumonia. The reference standard was the assessment by an experienced emergency department radiologist. Primary outcome was the presence or absence of pulmonary opacities consistent with community-acquired pneumonia. Sensitivity, specificity, and predictive values were calculated using generalised estimating equations. Results All clinicians assessed 128 ultralow-dose CTs. The prevalence of findings consistent with community-acquired pneumonia was 56%. Seventy-eight percent of the clinicians’ CT assessments matched the reference assessment. Diagnostic accuracy estimates were: sensitivity = 83% (95%CI: 77–88), specificity = 70% (95%CI: 59–81), positive predictive value = 80% (95%CI: 74–84), negative predictive value = 78% (95%CI: 73–82). Conclusion This study found that clinicians could assess chest ultralow-dose CTs for community-acquired pneumonia with high diagnostic accuracy. A higher level of clinical experience was not associated with better diagnostic accuracy.

The purpose of this study was to examine and explain the influence of the non-physical work environment, empowerment and training on the performance of contract employees at the West Muna Regency Regional Financial and Asset Management Agency. This research approach is a survey with an explanatory research design. Data collection was carried out using an instrument in the form of a questionnaire. Respondents in this study were all employees of the West Muna Regency Regional Financial and Asset Management Agency, namely as many as 59 people using a census method. The data analysis method of this research is descriptive analysis and Multivariate Regression Analysis.The results showed that the non-physical work environment partially had a positive and significant effect on employee performance, empowerment partially had a positive and significant effect on employee performance, training partially had a positive and significant effect on employee performance, and the non-physical work environment, empowerment and training together had a positive and significant effect on employee performance.

Abstract Vitamin D3 is clinically used for the treatment of vitamin D3 deficiency or osteoporosis, partially because of its role in regulating phosphate (Pi) and calcium (Ca2+) homeostasis. The renal sodium-phosphate cotransporter 2a (Npt2a) plays an important role in Pi homeostasis; however, the role of vitamin D3 in hypophosphatemia has never been investigated. We administered vehicle or vitamin D3 to wild-type (WT) mice or hypophosphatemic Npt2a−/− mice. In contrast to WT mice, vitamin D3 treatment increased plasma Pi levels in Npt2a−/− mice, despite similar levels of reduced parathyroid hormone and increased fibroblast growth factor 23. Plasma Ca2+ was increased ~ twofold in both genotypes. Whereas WT mice were able to increase urinary Pi and Ca2+/creatinine ratios, in Npt2a−/− mice, Pi/creatinine was unchanged and Ca2+/creatinine drastically decreased, coinciding with the highest kidney Ca2+ content, highest plasma creatinine, and greatest amount of nephrocalcinosis. In Npt2a−/− mice, vitamin D3 treatment completely diminished Npt2c abundance, so that mice resembled Npt2a/c double knockout mice. Abundance of intestinal Npt2b and claudin-3 (tight junctions protein) were reduced in Npt2a−/− only, the latter might facilitate the increase in plasma Pi in Npt2a−/− mice. Npt2a might function as regulator between renal Ca2+ excretion and reabsorption in response to vitamin D3.

Abstract Activation-induced cytidine deaminase (AID) is a B cell-specific mutator required for antibody diversification. However, it is also implicated in the etiology of several B cell malignancies. Evaluating the AID-induced mutation load in patients at-risk for certain blood cancers is critical in assessing disease severity and treatment options. We have developed a digital PCR (dPCR) assay that allows us to quantify mutations resulting from AID modification or DNA double-strand break (DSB) formation and repair at sites known to be prone to DSBs. Implementation of this assay shows that increased AID levels in immature B cells increase genome instability at loci linked to chromosomal translocation formation. This includes the CRLF2 locus that is often involved in translocations associated with a subtype of acute lymphoblastic leukemia (ALL) that disproportionately affects Hispanics, particularly those with Latin American ancestry. Using dPCR, we characterize the CRLF2 locus in B cell-derived genomic DNA from both Hispanic ALL patients and healthy Hispanic donors and found increased mutations in both, suggesting that vulnerability to DNA damage at CRLF2 may be driving this health disparity. Our ability to detect and quantify these mutations will potentiate future risk identification, early detection of cancers, and reduction of associated cancer health disparities.

Mohammad Al Masri,1 Hala AlOmari,2 Lina AbuKhader3 1Audiology and Speech Pathology Department, Al-Ahliyya Amman University, Amman, Jordan; 2Hearing and Speech Sciences, University of Jordan, Amman, Jordan; 3Middle East Hearing Association, Amman, JordanCorrespondence: Mohammad Al Masri, Head of the Audio-Vestibular Department, Audiology and Speech Pathology Department, Al-Ahliyya Amman University, Amman, Jordan, Tel +962797157576, Email Masri333@gmail.com; masri@ammanu.edu.joObjective: This study aimed to establish and validate a Jordanian Arabic version of the Dizziness Handicap Inventory (DHI-JA) equivalent to the original DHI in English (DHI-E).Methods: The THI-E questionnaires were translated into formal Jordanian Arabic by two bilingual volunteer audiologists. The final version, curated by the author, was administered to 20 participants with normal balance and 64 patients experiencing dizziness at the Middle East Hearing and Balance Centre.Results: The results demonstrated excellent internal consistency and reliability of DHI-JA. A highly significant correlation was observed between the total scores and sub-scores of DHI-JA and DHI-E.Conclusion: This study concludes that the DHI-JA is a valid and reliable tool for assessing the severity of dizziness and balance disorders in the Jordanian Arabic-speaking population.Keywords: dizziness handicap inventory, Arabic dizziness handicap inventory, vertigo, balance disorder

Abstract Background Despite advancements in family planning (FP) services, several barriers persist in the Occupied Palestinian territory (oPt), blocking women’s access to suitable, high-quality and equitable FP services. The aim of this study was to understand how healthcare providers perceive their abilities, barriers and opportunities in providing good quality FP services. Furthermore, it seeks to explore knowledge and training regarding FP among healthcare providers engaged in providing FP services. Methods A qualitative study was undertaken from August to September 2022 in seven Primary Health Care (PHC) clinics distributed in three governorates and operating under the Palestinian Ministry of Health (MoH) in the West Bank. Semi-structured, in-depth face-to-face interviews were conducted with 13 health providers (Physicians, midwives and nurses), using an interview guide in Arabic language. Transcripts were subsequently analyzed using the six phases of reflexive thematic analysis. Results FP services face various challenges, including shortages in resources such as staff, supplies, infrastructures and FP methods. Midwives possess significant potentials to offer accessible, high-quality, efficient and equitable FP services, yet, their capacities remain underutilized, representing a missed opportunity for a country like Palestine. The study provided a current overview of FP services while illustrating the need for quality FP services and the need for an updated continuous education and training, updated standardized guidelines and protocols and supportive supervision are needed across all levels of healthcare providers. Finally, providers reported a wide range of structural barriers to FP services. Conclusions It is crucial to meticulously address both community-related and health system factors to enhance the fulfillment of FP needs and reduce unintended and closely spaced pregnancies. Policymakers should invest in the development of laws and regulations regarding FP services, promoting a comprehensive and holistic approach to FP services. This includes formulating supportive policies, capacity building of human resources and maintaining security of FP commodities.

Penelitian ini bertujuan untuk mengetahui kelayakan, kepraktisan dan keefektifan video pembelajaran IPA menggunakan aplikasi Doratoon berbasis literasi sains. Jenis penelitian ini menggunakan penelitian pengembangan atau research and development (R&D) dengan model ADDIE yang terdiri dari 5 tahapan yaitu tahap analisis, perancangan, pengembangan, implementasi, dan evaluasi. Subjek pada penelitian ini adalah peserta didik berjumlah 29 orang dan 1 orang guru kelas V SDN 173175 Simarpinggan. Teknik pengumpulan data menggunakan teknik wawancara, dokumentasi, angket, dan tes. Hasil validasi oleh ahli materi dan ahli media adalah “Sangat Layak”. Uji kepraktisan oleh praktisi pendidikan menyatakan bahwa video pembelajaran “Sangat Praktis” dan hasil respon peserta didik diperoleh pada uji coba perorangan dan uji coba kelompok kecil menunjukkan bahwa video pembelajaran “Sangat Praktis”. Berdasarkan hasil uji keefektifan diketahui bahwa setelah menggunakan video pembelajaran yang dikembangkan terlihat peningkatan hasil belajar masing-masing peserta didik. Dilihat dari rata-rata nilai peserta didik berada pada kategori ‘Tuntas’ dan “Efektif” setelah menggunakan video pembelajaran. Dari penelitian dan data yang ditemukan dapat disimpulkan bahwa video pembelajaran IPA menggunakan aplikasi Doratoon berbasis literasi sains layak untuk digunakan dalam pembelajaran.

Koordinasi bagi pebulutangkis usia dini sangat penting sehingga tes dan pengukuran perlu dilakukan untuk mengetahui keterampilan yang dicapainya. Tujuan Penelitian untuk mengetahui hasil tes dan pengukuran koordinasi pebulutangkis usia dini. Metode kuantitatif melalui tes dan pengukuran dilakukan. Responden ditentukan dengan purposive sampling yang melibatkan 30 pebulutangkis putra usia dini serta 10 pebulutangkis putri dengan dilakukan tes dan pengukuran. Teknik analisis data deskriptif presentase diterapkan. Prosedur penelitian menerapkan tes koordinasi dengan melakukan pukulan dinding pada jarak 2-meter selama 1 menit. Hasil tes pebulutangkis putra kategori usia dini kriteria sangat baik = 2, baik = 3, sedang = 8, kurang = 7 dan kurang sekali = 10. Pada pebulutangkis putri usia dini sangat baik = 1, baik = 1, sedang = 2, kurang = 1 dan kurang sekali = 5. Temuan penelitian tidak semua pebulutangkis usia dini memiliki hasil tes koordinasi yang baik sehingga perlu adanya evaluasi yang tepat oleh pelatih yang berkaitan dengan program latihan sehingga mendapatkan hasil optimal. Kesimpulannya tes koordinasi dapat dijadikan sebagai potret kemajuan perkembangan latihan sehingga dapat mengukur dengan tepat kemampuan pebulutangkis usia dini sekaligus dapat dijadikan sebagai rekam jejak dari pebulutangkis yang dapat dijadikan pegangan dan dilakukan tindak lanjut dalam meningkatkan keterampilan gerak pebulutangkis.

Mohammad A Jareebi,1 Ahmad Y Alqassim,1 Saja A Almraysi,1 Weaam A Alfaifi,1 Taif A Masri,1 Rana S Alaki,1 Wasan M Qawfashi,1 Khairiah Ibrahim Nehari,1 Sawsan B Hakami,1 Ibrahim M Gosadi,1 Mohammed A Muaddi,1 Abdullah A Alharbi,1 Anwar M Makeen,1 Sarah MA Salih,1 Ali Abdullah Masmali2 1Department of Family and Community Medicine, Jazan University, Jazan, Saudi Arabia; 2Department of Jazan Health Affairs, King Fahd Central Hospital, Jazan, Saudi ArabiaCorrespondence: Mohammad A Jareebi, Department of Family and Community Medicine, Jazan University, Jazan, Saudi Arabia, Email mjareebi@jazanu.edu.saBackground: Despite deep cultural traditions, incense burning significantly impacts respiratory health. Effects of Arabian bakhour remain unknown in Saudi Arabia’s Jazan region with prevalent use. This cross-sectional study addresses this gap by investigating bakhour exposure and respiratory diseases.Methods: This was descriptive cross-sectional study conducted in Jazan area, Saudi Arabia, from October 2023 to March 2024. A total of 1612 participants age more than 18 years, both gender and resident of Jazan Area were included. Those aged less than 18 years were excluded. SPSS v 26 was used for data analysis.Results: The sample (n=1612) had a mean age of 29± 11 years and was 63% female. Bakhour use was nearly universal (98%), especially using coal (73%). Higher bakhour frequency significantly associated with increased cough (p< 0.01) and dyspnea (p< 0.01). Certain bakhour types linked to greater allergic rhinitis prevalence (p< 0.01). Regression analysis revealed cough during bakhour use worsened respiratory health (increased respiratory score) by 3.89 times (95% CI 1.13– 6.64; p=0.006) while dyspnea increased the score by 7.48 times (95% CI 4.70– 10.25; p< 0.001).Conclusion: This study provides valuable insights into the association between Bakhour use and respiratory health in the Jazan region. The findings emphasize the need for further research and public health interventions to mitigate potential respiratory risks associated with Bakhour use.Keywords: bakhour, respiratory health, cough, dyspnea, allergic rhinitis, household air pollution, environmental health