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Federated Learning (FL) is a paradigm of distributed machine learning that enables multiple devices or clients to work together in training a common model while keeping the privacy of individual data. However, FL has several issues such as slow convergence, communication overhead, and vulnerability to adversarial attacks, particularly in Industry 5 environments such as the Internet of Things (IoT) and its integration with traditional manufacturing processes. These challenges stem from the diverse and non-IID nature of data distributed across clients, which leads to slow convergence and increased communication rounds. This paper aims to address these challenges by proposing a grey wolf optimisation-based federated learning (GWOFL) approach for offering resilience in Industry 5.0 settings against adversarial attacks. The proposed approach decreases the number of communication rounds, reduces the payload between clients and the server, and withstands adversarial attacks simultaneously. It also reduces communication overhead and successfully defends against data poisoning attacks. Experimental results have revealed the efficiency of the proposed approach in overcoming the challenges of FL using the MNIST and CIFAR-10 datasets. The proposed approach converges faster, along with higher accuracy compared with the peer FL methods.

Effective classification and detection of equipment on construction sites is critical for efficient equipment management. Despite substantial research efforts in this field, most previous studies have focused on classifying a limited number of equipment categories. Furthermore, there is a scarcity of research dedicated to heavy construction equipment. Hence, this study develops a robust Convolutional Neural Network (CNN) model to classify heavy construction machinery into 12 different types. The study utilizes a comprehensive dataset of equipment images, which was divided into three distinct subsets: 60% for training the model, 30% for validating its performance, and 10% for testing its accuracy. The model’s robustness was ensured by monitoring accuracy and loss measures during the training and validation phases. The CNN model achieved approximately 85% training accuracy with a minimum loss of 0.40. The testing phase revealed a high overall precision of 80%. The CNN model accurately classifies concrete mixer machines and telescopic handlers with an Area Under the Curve (AUC) of 0.92, however pile driving machines have a lower accuracy with an AUC of 0.83. These findings demonstrate the model’s high ability to distinguish between several types of heavy construction equipment. This paper contributes to the relatively unexplored area of classifying heavy construction equipment by providing a practical tool for automating equipment classification, leading to enhanced efficiency, safety, and maintenance protocols in construction management.

The accumulation of material waste on construction sites often stems from underutilized procured materials, prompting a plethora of practical and scientific endeavors to mitigate waste and enhance construction project sustainability. This research study analyzes and synthesizes existing research on material waste management in construction projects from 2000 to 2023, aiming to investigate significant research themes and propose potential future research directions. Bibliometric and scientometric analyses are employed to examine the 205 documents retrieved from the Scopus database. The findings highlight that keywords like construction waste, waste management, and construction and demolition waste are the most prominent in the research domain. China and Malaysia are the leading contributors to this research subject. The Waste Management Journal is recognized as a seminal journal on construction material waste management. The qualitative evaluation reveals several prominent research themes: waste quantification and minimization, lifecycle assessment of waste, waste disposal and diversion practices, causal factors of waste generation and management, and circular economy and sustainable construction. The paper offers valuable insights for both scholars seeking new research avenues and practitioners interested in exploring prospective business opportunities.

Intelligent transportation systems, especially Autonomous Vehicles (AVs), are emerging as a paradigm with the potential to change modern society. However, with this, there is a strong need to ensure the security and privacy of such systems. AV ecosystems depend on machine learning algorithms to autonomously control their operations. Given the amount of personal information AVs collect, coupled with the distributed nature of such ecosystems, there is a movement to employ federated learning algorithms to develop secure decision-making models. Although federated learning is a viable candidate for data privacy, it is vulnerable to adversarial attacks, particularly data poisoning attacks, where malicious vectors would be injected in the training phase. Additionally, hyperparameters play an important role in establishing an efficient federated learning model that can be resilient against adversarial attacks. In this paper, to address these challenges, we propose a novel Optimized Quantum-based Federated Learning (OQFL) framework to automatically adjust the hyperparameters of federated learning using various adversarial attacks in AV settings. This work is innovative in two ways: first, a quantum-behaved particle swarm optimization technique is used to update the hyperparameters of the learning rate, local and global epochs. Second, the proposed technique is utilized within a cyber defense framework to defend against adversarial attacks. The performance of the proposed framework was evaluated using two benchmark datasets: MINST and Fashion-MINST, where they include images that would be extracted from smart cameras of AVs. This framework is shown to be more resilient against various adversarial attacks compared with peer techniques.

Background: An inflammatory disorder known as trigger finger (TF) results in pain in the proximal and distal palm and digit, as well as restricted movement and diminished function. Physiotherapists use a specific treatment called dry needling (DN) to reduce musculoskeletal pain and assist patients with mobility restriction. Objectives: This study aimed to examine the effectiveness of DN on quality of life in patients suffering from trigger finger. Methods: Forty patients, ranging in age from 45 to 75, were randomized into one of two groups in this Prospective pre and post-test, single-blind parallel group randomized controlled trial. Twenty patients were assigned to the intervention group, which included traditional physiotherapy splinting and ultrasound (ultrasonic dosage was 3MHz, the intensity of 0.5 W/cm2, and the duty cycle 50%. Duration: 5 minutes) as well as to DN upon a nodule at the proximal end of the 1st annular (A1) pulley as well as the discrepancy that existed between the flexor tendon's diameter along with its sheath at the metacarpal head. Twenty patients were assigned to the control group, which consisted of traditional treatment alone, twice weekly, for a total of ten sessions over five weeks. The trial evaluated the quality of life, severity of pain, and hand grip strength using the World Health Organization Quality-of-Life Scale (WHOQOL-BREF), visual analogue scale, and Camry dynamometer respectively two times, beforetreatment and after 10 treatment sessions. Results: Within-group analysis using MANOVA demonstrated a significant decline in VAS (p = 0.001) and a significant improvement in quality of life (p= 0.001) and hand grip strength (p= 0.001). Regarding between group comparison using MANOVA there was significant improvement in favor of the DN intervention (p= 0.001) for all measured variables. Conclusions: A 5-week dry needling approach with a traditional physiotherapy program was effective in improving in quality of life, pain intensity, and hand grip strength in patients with trigger finger, emphasizing it as the better option. Trial registration:: Clinical Trial. Gov, NCT05671523. Registered 14 December 2022.

Background: Today, low back pain is a serious global health issue. Currently, 85% of low back pain is caused by muscular imbalance and is a result of Lower cross syndrome (LCS), which are chronic postural defects. The physical therapy technique known as Global Postural Re-education (GPR) was created in France and is based on the concept that muscle chains make up the entire muscular system. Aim: The purpose of this study was to investigate the effect of global postural re-education on low back pain patients with lower cross syndrome. Design: Prospective pre and post-test, single-blind randomized controlled trial. Setting: Outpatient physio therapy clinic at Damanhur teaching hospital. Population: Fifty participants (25 to 40 years old) suffer from low back pain with lower cross syndrome. Methods: Participants were randomly assigned to two equal groups: global posture re-education approach and conventional physiotherapy was given to study group "A" and conventional physiotherapy was given to control group "B" throughout the course of 15 treatment sessions, twice sessions weekly. The patients' pre- and post-treatment progress was assessed using the clinometer digital smartphone application to measure anterior pelvic tilting, flexible ruler to measure lumbar lordosis, Modified Thomas test to measure hip flexor flexibility, Visual analogue scale (VAS) to measure pain and Oswestry Low Back Pain Disability Questionnaire Arabic version to measure function disability. Results: Comparing the study group and the control group after treatment showed that the study group had a significantly lower anterior pelvic tilting (P= 0.001), lower lumbar lordosis (p= 0,024), higher flexibility of hip flexor muscle (p= 0,001), lower VAS (p= 0,001) and improving in function disability (p= 0,001) compared to the control group. Conclusion: Adding a Global postural re-education approach is thought to be effective in treating low back pain patients with lower cross syndrome. Clinical Rehabilitation Impact: Global postural re-education approach decreases anterior pelvic tilting angle, lumbar lordotic angle, pain, and disability caused by low back pain and increase hip flexor flexibility.

The primary goal of pavement maintenance and rehabilitation (M&R) planning is to achieve transportation agencies’ objectives within limited budgets. Therefore, numerous optimization models have been developed for scheduling M&R activities at project or network levels; however, their effectiveness is contingent upon pavement condition deterioration and improvement, M&R alternatives, and various climatic, environmental, and regional factors. Moreover, these models should produce comprehensive, efficient, and practical schedules on which highway agencies can rely. This research study introduces a framework and optimization model for large road networks with different types of pavement surfaces, considering various pavement condition indicators and distresses. Employing the genetic algorithm (GA), a single-objective optimization model was created to generate optimal M&R schedules. The reliability of this GA model was tested to ensure the generation of optimal solutions. Furthermore, the model’s sensitivity to budget variations was assessed. The developed framework and GA model were used to analyze Interstate 80 sections in Wyoming, USA. The results demonstrate that the GA model consistently achieved the most cost-effective outcomes over a 5-year period, with fewer than 1,000 iterations. Moreover, given the available budget, implementing the generated treatment plan has the potential to preserve road networks in good condition. The findings suggest that optimization models should consider multiple condition indicators and distresses when developing practical schedules for effective pavement maintenance. It was unearthed that funds should be allocated among network components based on their relative conditions. This article could assist policymakers and asset managers in making budgetary decisions and developing M&R strategies for the long-term sustainability of transportation networks.

Civil infrastructure projects, including roads, bridges, distribution networks, and treatment facilities, need to be executed successfully to actualize the expected return on investment, deliver the planned services, and fulfill national objectives. Hence, this research investigates the critical success factors (CSFs) influencing the performance of infrastructure construction projects using qualitative and quantitative methods. The qualitative methodology comprises a thorough and systematic literature review to identify the CSFs affecting infrastructure project performance. The CSFs were categorized by the construction project phases to assess the significance of each phase on project performance. The quantitative technique entails two stages: a pilot study with structured interviews and a questionnaire survey administered to subject matter experts. The data was analyzed using three statistical techniques: one-sample t-test, mean score, and factor analysis. Furthermore, the CSFs were classified into five categories using principal component analysis. According to the findings, the key CSFs of infrastructure projects include the project manager’s competency, commitment, and involvement; sufficient funding throughout the project’s life cycle; and effective communication and coordination among stakeholders. The results will provide construction professionals with valuable insights into the topmost CSFs that contribute to the success of infrastructure construction projects.

The purpose of this study was to compare the strength and endurance of the trunk muscles in women with chronic ankle instability (CAI) compared to those who do not have CAI.

Background: Kidney transplant recipients may develop post-transplant diabetes mellitus (PTDM). Dipeptidyl peptidase 4(DPP-4) inhibitors are evolving agents in the management of patients with diabetes mellitus. Aim: To evaluate the efficacy and safety of DPP-4 inhibitors in the management of post-transplant diabetes mellitus (PTDM) in renal transplant recipients. Methods: We performed a systematic search of the electronic databases using keys words and Mesh terms. Data were extracted and reviewed using structured proforma. A comprehensive review of the eligible studies was performed independently by each of two reviewers; conflicts were resolved by the third reviewer. The primary efficacy endpoint was the difference in glycosylated hemoglobin (HbA1c) comparing any of the DPP-4 inhibitors to either placebo or other hypoglycaemic agent. The primary safety endpoints were the worsening of graft functions and change in Tacrolimus trough level. We performed the Random effect model using standardised mean difference. Results: We identified seven studies that were eligible for the systematic review; only one study compared Sitagliptin to insulin Glargine. One study involved head to head comparison of three DPP-4 inhibitors. The other five studies were pooled in the meta-analysis. DPP-4 inhibitors had a favourable glycemic effect as measured by HbA1c when compared to either placebo or oral anti-hyperglycemic medications (standardised mean difference in HbA1c = -0.993, 95% CI= -1.303 to -0.683, P=0.001). DPP-4 inhibitors use did not result in significant change in eGFR ((standardised mean difference = 0.147, 95% CI= -0.139 - 0.433, p=0.312).) nor Tacrolimus level (standardised Mean Difference= 0.152, 95% CI= -0.172 to 0.477, P=0.354). Conclusion: Current evidence supports the short term efficacy and safety of DDP-4 inhibitor agents in the management of post transplantation diabetes mellitus (PTDM) in kidney transplant recipients. However, more RCTs are required to investigate the long-term safety and efficacy of these agents in kidney transplant recipients.

Novel complexes of Ru (III), Cu (II) and Au (III) (2–4) were prepared using 6-phenyl-2-thioxo-4-(trifluoromethyl)-1,2-dihydropyridine-3-carbonitrile (HL, 1) adopting either electrochemical or traditional chemical methods. The electrochemical method is preferred in the synthesis of the complexes than the chemical one because it affords pure products with higher yields in shorter reaction time. The novel thiopyridine complexes were characterized by elemental analyses, IR, 1H, 19F-NMR, TGA and DTA measurements. The antimicrobial activity evaluation revealed that the complex bearing copper metal 3has nearly the same activity as the reference drug ciprofloxacin. Anti-inflammatory activity evaluation showed that complex 4containing gold displayed anti-inflammatory activity higher than the reference drug celecoxib upon using carrageenan rat hind paw edema method.

Deep learning (DL) has proven its efficacy in extracting useful distress information from image-based data of infrastructure assets, such as pavements. Despite the overwhelming research on this topic, state-of-the-art DL approaches fail to perform satisfactorily on independent datasets as noted from an object detection-based competition. Besides, a lack of clarification in computing DL performance measures and inadequate discussion on DL implementation framework still exist. To this end, this paper contributes to the body of knowledge by synthesizing the performance of DL models from the existing relevant literature using the ‘random effect meta-analysis’ approach. Meta-analysis requires an estimate of the uncertainty in the reported performance measure (i.e., F1-score) to assign weights to individual studies and compute an overall performance measure for a group of studies. Hence, this paper introduces a statistical approach to calculate the uncertainty in the reported F1-score to compute the within-study variance. The methods, statistics, and results presented in this paper will help understand the requisites for future studies on DL in pavement distress evaluation, ultimately improving pavement asset management.

Background:Early prediction and clinical intervention are extremely important in order to delay or hinder diabetic nephropathy (DN) progression. Objectives:This study aimed to detect early signs of DN and study the potential ameliorating effect of the dipeptidyl peptidase-4 inhibitor, linagliptin, on some early markers for DN in fructose-streptozotocin (Fr-STZ)-induced diabetic rats. Method:Fr-STZ rats were treated with either linagliptin (3 mg/kg p.o. daily), metformin (350 mg/kg p.o. daily), or their combination for 6 weeks. Results:Fr-STZ DN rats exhibited obvious tubular renal damage and glomerular podocyte injury as confirmed by renal kidney ­injury molecule-1 (KIM-1), neutrophil gelatinase-associated lipocalin (NGAL), and vanin-1 mRNA, as well as urinary N-acetyl-β-D-glucosaminidase elevation and nephrin mRNA suppression, associated with the appearance of marked renal interstitial fibrosis and glomerulosclerosis despite the absence of microalbuminuria. Initiation of oxidative, inflammatory, fibrotic, and apoptotic reactions was evident in the settings of renal hyperglycemia. Linagliptin significantly modulated the aforementioned renal tubular injury makers and restored glomerular nephrin expression as well as reversed renal histopathological alterations. Oxidative, inflammatory, fibrotic and apoptotic processes were also alleviated. Conclusions:This study suggests that linagliptin exerts renoprotection against early features of DN in rats probably by inhibition of high glucose-induced renal tubular and glomerular injury thereby hampering KIM-1 and NGAL as well as vanin-1 associated with renal inflammation, fibrosis and oxidative stress.

Tumors at the craniocervical junction represent a formidable challenge due to its intimate association with the medulla and cervical spinal cord, lower cranial nerves, and vertebral artery (VA). Foramen magnum meningioma (FMM) reparents 2% of all intracranial meningiomas and 4% of posterior fossa meningiomas. The far lateral approach is preferred for FMM with VA involvement. Here, we describe a case of a 58-year-old woman who presented with headache, nausea and vomiting and gait disturbance and was found to have large FMM encasing the dominant VA. The patient was treated with a left far lateral approach with limited condylectomy resulting in gross total resection and preservation of lower cranial nerves function.

Prior research has shown that students’ previous grade point average (GPA) is the best predictor for future academic success. However, it can only partly predict the variability in dental school performance. The aim of this study was to assess the predictive value of multiple mini‐interviews (MMI) as an admission criterion by comparing them with the academic performance of dental students over a two‐year period. All incoming undergraduate dental students at the King Abdulaziz University Faculty of Dentistry (KAUFD) during academic year 2013–14 were invited to participate in MMI. Students rotated through six objective structured clinical exam (OSCE)‐like stations for 30 minutes total and were interviewed by two trained faculty interviewers at each station. The stations were focused on noncognitive skills thought to be essential to academic performance at KAUFD. The academic performance of these students was then followed for two years and linked to their MMI scores. A total of 146 students (71 males and 75 females) participated in an interview (response rate=92.9%). Most students scored in the acceptable range at each MMI station. Students’ total MMI score, ambitions, and motives were significant predictors of GPA during the two years of follow‐up (p<0.038 and p<0.001, respectively). In this study, MMI was found to be able to predict future academic performance of undergraduate dental students.

The purpose of this study was to determine whether the mucosal expression of interleukin (IL)‐23p19 has a role in the pathogenesis of ulcerative colitis, and to determine its relation to disease severity. This study was performed on 50 patients with ulcerative colitis and 10 normal individuals as the controls. They were divided into Group I (27 patients with mild to moderate disease), Group II (23 patients with severe disease), and Group III (10 normal individuals). All patients and the controls were subjected to histopathological study, IL‐23p19 immunohistochemical staining, IL‐23R expression by flow cytometry, and serum IL‐23 by enzyme‐linked immunoassay. There was a significant increase in IL‐23p19 gene expression and IL‐23R level in patients with ulcerative colitis, compared to the controls. A significant positive correlation was detected between increased expression of the IL‐23p19 gene, IL‐23R, high serum IL‐23, and the severity of the disease. Increased expression of the IL‐23p19 gene has a role in the pathogenesis of ulcerative colitis. Targeted therapy directed against IL‐23p19 may be effective in its treatment. Increased expression of the IL‐23p19 gene and IL‐23R with high serum IL‐23 is correlated positively with disease severity.

The purpose of this study was to determine whether the mucosal expression of interleukin (IL)-23p19 has a role in the pathogenesis of ulcerative colitis, and to determine its relation to disease severity.

Purpose:Dysmorphology syndromes are among the most common referrals to clinical genetics specialists. Inability to match the dysmorphology pattern to a known syndrome can pose a major diagnostic challenge. With an aim to accelerate the establishment of new syndromes and their genetic etiology, we describe our experience with multiplex consanguineous families that appeared to represent novel autosomal recessive dysmorphology syndromes at the time of evaluation.Methods:Combined autozygome/exome analysis of multiplex consanguineous families with apparently novel dysmorphology syndromes.Results:Consistent with the apparent novelty of the phenotypes, our analysis revealed a strong candidate variant in genes that were novel at the time of the analysis in the majority of cases, and 10 of these genes are published here for the first time as novel candidates (CDK9, NEK9, ZNF668, TTC28, MBL2, CADPS, CACNA1H, HYAL2, CTU2, and C3ORF17). A significant minority of the phenotypes (6/31, 19%), however, were caused by genes known to cause Mendelian phenotypes, thus expanding the phenotypic spectrum of the diseases linked to these genes. The conspicuous inheritance pattern and the highly specific phenotypes appear to have contributed to the high yield (90%) of plausible molecular diagnoses in our study cohort.Conclusion:Reporting detailed clinical and genomic analysis of a large series of apparently novel dysmorphology syndromes will likely lead to a trend to accelerate the establishment of novel syndromes and their underlying genes through open exchange of data for the benefit of patients, their families, health-care providers, and the research community.Genet Med 18 7, 686–695.

Dysmorphology syndromes are among the most common referrals to clinical genetics specialists. Inability to match the dysmorphology pattern to a known syndrome can pose a major diagnostic challenge. With an aim to accelerate the establishment of new syndromes and their genetic etiology, we describe our experience with multiplex consanguineous families that appeared to represent novel autosomal recessive dysmorphology syndromes at the time of evaluation.

This review addresses significant changes in our understanding of urothelial development and regeneration. Understanding urothelial differentiation will be important in the push to find new methods of bladder reconstruction and augmentation, as well as identification of bladder cancer stem cells.

Cloud computing becomes the most common product within IT markets and the global business in general. It offers a powerful and flexible resources and services including software as a service (SaaS), infrastructure as a service (IaaS) and platform as a service (PaaS). On the other hand, service level agreement (SLA) is a formal contract between the cloud vendors and providers, and customers including organisations and individuals. SLA is considered as an awkward process for both providers and customers since each part has a different understanding about its detailed structure, phases and embedded elements. In this paper, a fine-defined engineering model with five well-structured phases for managing SLA in a cloud environment is introduced. This SLA engineering model will benefit both cloud providers and consumers. It will allow providers to structure and organise the SLA correctly in attractive way for customers. Simultaneously, it will provide customers with the necessary and sufficient awareness to get a fair SLA for resources or services they may access. Also, a case study is presented to demonstrate how the proposed five phases utilised to construct the required SLA in a private cloud.

Hepatocellular carcinoma (HCC) is one of the major complications of liver cirrhosis. Radiofrequency ablation (RFA) is the treatment of choice for patients with an early-stage HCC who are not candidates for surgical management; however, it is associated with a recurrence rate as high as 15–30 after 1 year. The aim of this study was to analyze the risk factors for HCC recurrence in Egyptian patients after RFA.

Rationale: Gaucher disease (GD) is the most common amongst the lysosomal storage disorders. Prevalence of GD in Saudi Arabia is not available in published literature and it is expected to be high and remains undiagnosed. In 2004, a Saudi study reported that GD accounts for 6% of all genetic metabolic disorders. While acid sphingomyelinase deficiency (ASMD) is a rare progressive genetic disorder with no clear data about its incidence in Saudi Arabia. Consequently, this study proposes to determine the prevalence of GD and ASMD in outpatient settings in Saudi Arabia by screening patients with unknown causes of splenomegaly and/or thrombocytopenia. These data will change the local practice and increase the awareness towards GD and ASMD in Saudi Arabia.

Psychiatric morbidity is defined as the presence of handicapping abnormalities of emotions, behavior, and relationships that impede personal and social functioning. Therefore, the present study was designed to argue this issue.

The aim of this study was to evaluate the diagnostic accuracy of soluble triggering receptor expressed on myeloid cells-1 (sTREM-1) in comparison to procalcitonin (PCT) and C-reactive protein (CRP) in early diagnosis of septic meningitis and its usefulness to distinguish between septic and aseptic meningitis.

Asthma is a chronic inflammatory condition of the airways. Pain and anxiety triggered by dental treatment can induce the secretion of endogenous catecholamines. When the situation is combined with the use of local anesthetics with vasoconstrictors, it may increase its undesirable effects on the cardiovascular system and respiratory systems.

In multiplexed computational imaging schemes, high-resolution images are reconstructed by fusing the information in multiple low-resolution images detected by a two-dimensional array of low-resolution image sensors. The reconstruction procedure assumes a mathematical model for the imaging process that could have generated the low-resolution observations from an unknown high-resolution image. In practical settings, the parameters of the mathematical imaging model are known only approximately and are typically estimated before the reconstruction procedure takes place. Violations to the assumed model, such as inaccurate knowledge of the field of view of the imagers, erroneous estimation of the model parameters, and/or accidental scene or environmental changes can be detrimental to the reconstruction quality, even if they are small in number. We present an adaptive algorithm for robust reconstruction of high-resolution images in multiplexed computational imaging architectures. Using robust M-estimators and incorporating a similarity measure, the proposed scheme adopts an adaptive estimation strategy that effectively deals with violations to the assumed imaging model. Comparisons with nonadaptive reconstruction techniques demonstrate the superior performance of the proposed algorithm in terms of reconstruction quality and robustness.