Search Results (59)

The increased occurrence and severity of cyber-attacks on critical infrastructure have underscored the need to embrace systematic and prospective approaches to resilience. The current research takes as its hypothesis that the InfraGuard Cybersecurity Framework—a capability model that measures the maturity of cyber resilience through three functional pillars, Cyber as a Shield, Cyber as a Space, and Cyber as a Sword—is an implementable and understandable means to proceed with. The model treats the significant aspects of situational awareness, active defense, risk management, and recovery from incidents and is measured using globally standardized maturity models like ISO/IEC 15504, NIST CSF, and COBIT. The contributions include multidimensional measurements of resilience, a scored scale of capability (0–5), and domain-based classification enabling organizations to assess and enhance their cybersecurity situation in a formalized manner. The framework’s applicability is illustrated in three exploratory settings of power grids, healthcare systems, and airports, each constituting various levels of maturity in resilience. This study provides down-to-earth recommendations to policymakers through the translation of the attributes of resilience into concrete assessment indicators, promoting policymaking, investment planning, and global cyber defense collaboration. Full article

Background/Objectives: While anterior lumbar interbody fusion (ALIF) is a well-established treatment for degenerative lumbar spine pathology, the timing and pace of postoperative recovery remain poorly defined. Understanding these temporal trends is clinically important for setting patient expectations and optimizing postoperative care. Methods: This retrospective single-institution study evaluated functional recovery in patients undergoing primary, single-level stand-alone (SA) ALIF, or with percutaneous posterior instrumentation (PI). Patient-reported outcome measures (PROMs), including the Oswestry Disability Index (ODI), the Visual Analog Scale (VAS) for back and leg pain, and the SF-12 Physical Component Score (PCS), were assessed preoperatively and at 2 weeks, 6 weeks, 3 months, 6 months, 1 year, and 2 years postoperatively. Achievement of minimum clinically important difference (MCID), global rating change (GRC), and return-to-activity milestones were also analyzed. Results: A total of 143 patients were included (90 SA; 53 PI). PROMs showed significant improvement through 1 year. VAS-back improved by 2 weeks, while ODI and SF12 PCS initially worsened but improved after 6 weeks. By 6 months, over half of the cohort achieved MCID, with continued gains through 1 year. Most patients returned to driving and work, and over 90% discontinued narcotics. Recovery trajectories were comparable between groups, despite early delays in the instrumented cohort. Conclusions: These findings provide time-specific recovery benchmarks that can guide surgical decision-making, patient education, and expectations around functional milestones. Full article

This research aimed to enhance biodiesel stability through catalytic transfer hydrogenation using a biomimetic bimetallic catalyst and glycerol as a hydrogen donor. The effects of catalyst species, intermediate solvent, glycerol feed, and glycerol form on biodiesel stability were investigated. In this study, the examined bimetallic catalysts were Zn-Cr-bicarbonate, Zn-Cr-formate, Zn-Cr-Ni, and Cu-Ni/SiO₂. Based on the results, the most excellent catalyst was presented by Cu-Ni/SiO₂ catalyst with DMF solvent and 10 wt% glycerol feed. This combination demonstrated a significant reduction in iodine (ΔIV = −4.9 g-I₂/100 g) and peroxide values (ΔPV = −5.2 meq-O₂/kg) accompanied by an elevation of oxidative stability (ΔOS = 4.3 h). Moreover, the reaction of catalytic transfer hydrogenation using these bimetallic catalysts followed the theoretical mechanism of the simultaneous dehydrogenation–hydrogenation process with two different metals. The promotion of bicarbonate and formate ions on the bimetallic catalyst provided hydrogen transfer assistance in the catalyst. Hence, the continuous improvement of biodiesel properties is expected to promote sustainable implementation of cleaner diesel fuel. Full article

The objective of this paper is to ascertain the manner in which the Qurʾān teaching activities are conducted in Jaʿfarī communities that are part of the multicultural structure of Türkiye. The methodological approach of this study is a case study design. This study’s sample consists of nine instructors who voluntarily work in Qurʾān teaching centres for Jaʿfarī communities. Criterion sampling and maximum diversity were employed in the selection of the participants. The data were collected using a semi-structured interview form. The collected data were subjected to a descriptive analysis. This study’s findings are as follows: firstly, the importance of early childhood education in the transmission of the Qurʾān among the Jaʿfarīs is evident. Secondly, the basis of this teaching is the Alif-Baa Juz education. Thirdly, mosques, association centres and neighbourhood houses play an important role in this teaching activity. Finally, according to Qurʾān instructors, traditionally the mosques were the primary centres for Qurʾān education; however, this has been lost especially after the pandemic. In addition to having the knowledge and competency of the recitation of the Qurʾān, the instructors who are supposed to work in these places are required to behave in accordance with Islamic morality. Over the recent times, families have started to prioritise their children’s academic success over religious education. Lastly, Jaʿfarīs do not have any safety concerns pertaining to the state while carrying out religious education activities. The findings, in general, reveal that Jaʿfarīs have similarities with the traditional teaching of Qurʾān in terms of method, content, materials and to some extent instructor competence, etc. The results of this paper are significant in terms of providing a concrete indicator of the pluralistic understanding that Türkiye advocates in religious education policies and in understanding the religious education practices of Jaʿfarī communities. Full article

Objectives: The aim of this study was to compare the radiographic results, mechanical complications, and clinical outcomes between anterior and posterior lumbar interbody fusion at L5–S1 (ALIF51 and PLIF51 groups, respectively) using a matched cohort of patients undergoing long fusion for adult spinal deformity (ASD). Methods: Patients who underwent hybrid surgery of ≥5-level fusion to the pelvis with a minimum follow-up duration of 2 years were included. The baseline characteristics of the groups were controlled using a propensity score matching analysis. The radiographic results, mechanical complications such as proximal junctional kyphosis/failure and metal failure, and clinical outcomes were compared between the groups. Results: In total, 79 patients were assigned to each group with comparable baseline data, except for a higher frequency of anterior column realignment procedures in the PLIF51 group than in the ALIF51 group (49.4% vs. 31.6%). At the last follow-up, L5–S1 segmental lordosis (SL) was significantly greater in the ALIF51 group than in the PLIF51 group (12.1° vs. 7.3°, p < 0.001). The final C7–sagittal vertical axis (SVA) was significantly smaller in the ALIF51 group than in the PLIF51 group (25.4 mm vs. 35.5 mm, p = 0.032). However, other global sagittal parameters were comparable between the groups. The mechanical complication rates, including metal failure at L5–S1, and the final clinical outcomes were comparable between the groups. Conclusions: ALIF51 has modest advantages over PLIF51 in terms of better restoring L5–S1 SL and C7–SVA with avoiding more invasive procedures above the L5–S1 levels. Other sagittal parameters, mechanical complication rates, and clinical outcomes did not differ between the groups. Full article

The anterior lumbar interbody fusion (ALIF) and lumbar disc arthroplasty (LDA) procedures are both commonly performed to improve the quality of life and pain in people with lower back pain. However, few recent studies have compared 30-day complications on a large scale. The objectives of this study were to compare the 30-day complications seen after ALIF and LDA and identify risk factors for these complications. The National Surgical Quality Improvement Program (NSQIP) database was queried between the years 2012–2021 (10 years in total) for records of patients who underwent either ALIF or LDA as a primary procedure. Patients in each group underwent a 1:1 propensity match for age, gender, BMI, ASA status, diabetes mellitus (DM), hypertension requiring medication, congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD), inpatient/outpatient status, smoking status, and bleeding disorders. Bivariate and multivariate analyses were performed to determine significant differences in complications and risk factors between these cohorts. A total of 1218 propensity-matched subjects, with 609 receiving ALIF and 609 receiving LDA, were included in the analyses of this study. The incidence of extended length of stay (LOS) (>4 days) was higher in the ALIF cohort compared to the LDA cohort (14.6% vs. 4.76%, p < 0.001). Multivariate analysis demonstrated that subjects who underwent LDA had lower odds (Odds Ratio [OR]: 0.457; 95% Confidence Interval [CI]: 0.283–0.738, p = 0.001) of experiencing extended LOS compared to the ALIF cohort. Longer operative times increased the odds of prolonged LOS in both cohorts. The results of this study suggest that ALIF is associated with longer LOS than LDA when baseline demographic data are controlled. Further, longer operative times increase the odds that subjects receiving either ALIF or LDA will experience a prolonged LOS. Besides extended LOS, ALIF and LDA produce a relatively similar 30-day complication profile. Full article

Giant freshwater prawn (Macrobrachium rosenbergii) farming in Bangladesh began in the 1970s and has become a significant export industry. Despite its potential, there are concerns about the environmental sustainability of prawn farming due to its high greenhouse gas (GHG) footprint, but implementation of integrated multi-trophic aquaculture (IMTA) may help minimize the GHG emission. A key element in IMTA is using plants to take up inorganic nutrients released by the prawns, producing valuable plant products and cleaning the water. Using a quadrat sampling method, we conducted a field study in combined prawn and shrimp ponds, aquaculture fishponds, and non-aquaculture waters in south- west Bangladesh to characterize plant diversity and identify suitable species for IMTA in prawn farms. A total of 38 plant species were identified with densities ranging from 4.5–6.1 plants/m² in the aquaculture ponds to 11.6–17.1 plants/m² in the prawn/shrimp and the non-aquaculture ponds. Free-floating plants were the most abundant, followed by emergent, floating anchored, and submerged plants. Most plants have commercial values as food, fodder, fish feed, fertilizer, or medicines to local people. Our results suggest that species within the Oxalis, Ipomoea, Azolla, and Lemna genera are suitable extractive aquatic plants for the implementation of IMTA in prawn farms and may improve the sustainability of prawn production. Full article

Smishing attacks, a sophisticated form of cybersecurity threats conducted via Short Message Service (SMS), have escalated in complexity with the widespread adoption of mobile devices, making it increasingly challenging for individuals to distinguish between legitimate and malicious messages. Traditional phishing detection methods, such as feature-based, rule-based, heuristic, and blacklist approaches, have struggled to keep pace with the rapidly evolving tactics employed by attackers. To enhance cybersecurity and address these challenges, this paper proposes a hybrid deep learning approach that combines Bidirectional Gated Recurrent Units (Bi-GRUs) and Convolutional Neural Networks (CNNs), referred to as CNN-Bi-GRU, for the accurate identification and classification of smishing attacks. The SMS Phishing Collection dataset was used, with a preparatory procedure involving the transformation of unstructured text data into numerical representations and the training of Word2Vec on preprocessed text. Experimental results demonstrate that the proposed CNN-Bi-GRU model outperforms existing approaches, achieving an overall highest accuracy of 99.82% in detecting SMS phishing messages. This study provides an empirical analysis of the effectiveness of hybrid deep learning techniques for SMS phishing detection, offering a more precise and efficient solution to enhance cybersecurity in mobile communications. Full article

This paper presents a novel approach to manage distributed DC microgrids (DCMG) by integrating a time-of-use (ToU) electricity pricing scheme and an internal price rate calculation mechanism. The proposed power-management system is designed to effectively handle uncertainties such as utility grid (UG) availability, fluctuating electricity prices, battery state of charge (SOC) levels, and frequent plug-ins and plug-outs of electric vehicles (EVs). Uncertainties in DCMG systems often lead to inefficiencies, power imbalances, and inexact voltage regulation issues within DCMGs. In addition, to maintain the power balance and constant voltage regulation under various operational states, the proposed scheme also incorporates secondary control into the DCMG power-management system. Unlike the existing approaches that often fail to adapt dynamically to changing conditions, the proposed method is the first approach to consider the concept of internal price rate in designing the DCMG power management. To address this challenge, this approach proposes a more resilient power-management strategy to enhance the efficiency and adaptability of DCMG systems. Extensive simulations and experimental validations demonstrate the practicality and adaptability of the proposed control strategy under diverse test conditions, including operation transitions between grid-connected mode (GCM) and islanded mode (IM), low battery SOC condition, operation transition from the current control mode (CCM) to distributed secondary control mode (DSCM), and EV plug-in scenarios. The test results confirm that the proposed method enhances the reliability, efficiency, and economic viability of DCMG systems, making it a promising solution for future smart grid and renewable energy integrations. Full article

Eye diseases are defined as disorders or diseases that damage the tissue and related parts of the eyes. They appear in various types and can be either minor, meaning that they do not last long, or permanent blindness. Cataracts, glaucoma, and diabetic retinopathy are all eye illnesses that can cause vision loss if not discovered and treated early on. Automated classification of these diseases from fundus images can empower quicker diagnoses and interventions. Our research aims to create a robust model, BayeSVM500, for eye disease classification to enhance medical technology and improve patient outcomes. In this study, we develop models to classify images accurately. We start by preprocessing fundus images using contrast enhancement, normalization, and resizing. We then leverage several state-of-the-art deep convolutional neural network pre-trained models, including VGG16, VGG19, ResNet50, EfficientNet, and DenseNet, to extract deep features. To reduce feature dimensionality, we employ techniques such as principal component analysis, feature agglomeration, correlation analysis, variance thresholding, and feature importance rankings. Using these refined features, we train various traditional machine learning models as well as ensemble methods. Our best model, named BayeSVM500, is a Support Vector Machine classifier trained on EfficientNet features reduced to 500 dimensions via PCA, achieving 93.65 ± 1.05% accuracy. Bayesian hyperparameter optimization further improved performance to 95.33 ± 0.60%. Through comprehensive feature engineering and model optimization, we demonstrate highly accurate eye disease classification from fundus images, comparable to or superior to previous benchmarks. Full article

Accurate vehicle detection is crucial for the advancement of intelligent transportation systems, including autonomous driving and traffic monitoring. This paper presents a comparative analysis of two advanced deep learning models—YOLOv8 and YOLOv10—focusing on their efficacy in vehicle detection across multiple classes such as bicycles, buses, cars, motorcycles, and trucks. Using a range of performance metrics, including precision, recall, F1 score, and detailed confusion matrices, we evaluate the performance characteristics of each model.The findings reveal that YOLOv10 generally outperformed YOLOv8, particularly in detecting smaller and more complex vehicles like bicycles and trucks, which can be attributed to its architectural enhancements. Conversely, YOLOv8 showed a slight advantage in car detection, underscoring subtle differences in feature processing between the models. The performance for detecting buses and motorcycles was comparable, indicating robust features in both YOLO versions. This research contributes to the field by delineating the strengths and limitations of these models and providing insights into their practical applications in real-world scenarios. It enhances understanding of how different YOLO architectures can be optimized for specific vehicle detection tasks, thus supporting the development of more efficient and precise detection systems. Full article

Background: Anterior lumbar interbody fusion (ALIF) and posterior spinal fusion (PSF) play pivotal roles in restoring lumbar lordosis in spinal surgery. There is an ongoing debate between combined single-position surgery and traditional prone-position PSF for optimizing segmental lumbar lordosis. Methods: This retrospective study analyzed 59 patients who underwent ALIF in the supine position followed by PSF in the prone position at a single institution. Cobb angles were measured preoperatively, post-ALIF, and post-PSF using X-ray imaging. One-way repeated measures ANOVA and post-hoc analyses with Bonferroni adjustment were employed to compare mean Cobb angles at different time points. Cohen’s d effect sizes were calculated to assess the magnitude of changes. Sample size calculations were performed to ensure statistical power. Results: The mean segmental Cobb angle significantly increased from preoperative (32.2 ± 13.8 degrees) to post-ALIF (42.2 ± 14.3 degrees, Cohen’s d: −0.71, p < 0.0001) and post-PSF (43.6 ± 14.6 degrees, Cohen’s d: −0.80, p < 0.0001). There was no significant difference between Cobb angles after ALIF and after PSF (Cohen’s d: −0.10, p = 0.14). The findings remained consistent when Cobb angles were analyzed separately for single-screw and double-screw ALIF constructs. Conclusions: Both supine ALIF and prone PSF significantly increased segmental lumbar lordosis compared to preoperative measurements. The negligible difference between post-ALIF and post-PSF lordosis suggests that supine ALIF followed by prone PSF can be an effective approach, providing flexibility in surgical positioning without compromising lordosis improvement. Full article

Continuous power management for a decentralized DC microgrid (DCMG) is proposed in this study to achieve power balance and voltage regulation even under system uncertainty and voltage sensor failure. The DCMG system achieves continuous power management through only the primary controller to reduce the computational burden of each power agent. To enhance the reliability and resilience of the DCMG system under DC bus voltage (DCV) sensor failure, a DCV sensor fault detection algorithm is suggested. In this algorithm, DCV sensor failure is detected by comparing the measured DCV with the estimated DCV. If power agents identify the failure of the DCV sensor, it changes the operation properly according to the proposed control mode decision algorithm to guarantee the stability of the DCMG system. When uncertain conditions like sudden grid disconnection, DCV sensor failure, electricity price change, power variation in distributed generations, and critical battery status occur, the DCMG system is changed to transitional operation modes. These transitional operation modes are employed to transmit the power agent information to other agents without digital communication links (DCLs) and to accomplish power sharing even under such uncertain conditions. In the transitional operation modes of the DCMG system, the DCV levels are temporarily shifted to an appropriate level, enabling each power agent to detect the uncertainty conditions, and subsequently to determine its operation modes based on the DCV levels. The reliability and effectiveness of the proposed control strategy are confirmed via various simulation and experimental tests under different operating conditions. Full article

Railway infrastructure safety is a paramount concern, with bolt integrity being a critical component. In the realm of railway maintenance, the detection of missing bolts is a vital task that ensures the stability and safety of tracks. Traditionally, this task has been approached through manual inspections or conventional automated methods, which are often time-consuming, costly, and prone to human error. Addressing these challenges, this paper presents a state-of-the-art solution with the development of a lightweight convolutional neural network (CNN) featuring an integrated attention mechanism. This novel model is engineered to be computationally efficient while maintaining high accuracy, making it particularly suitable for real-time analysis in resource-constrained environments commonly found in railway inspections. The proposed CNN utilises a distinctive architecture that synergises the speed of lightweight networks with the precision of attention-based mechanisms. By integrating an attention mechanism, the network selectively concentrates on regions of interest within the image, effectively enhancing the model’s capability to identify missing bolts with remarkable accuracy. Comprehensive testing showcases a remarkable 96.43% accuracy and an impressive 96 F1-score, substantially outperforming existing deep learning frameworks in the context of missing bolt detection. Key contributions of this research include the model’s innovative attention-integrated approach, which significantly reduces the model complexity without compromising detection performance. Additionally, the model offers scalability and adaptability to various railway settings, proving its efficacy not just in controlled environments but also in diverse real-world scenarios. Extensive experiments, rigorous evaluations, and real-time deployment results collectively underscore the transformative potential of the presented CNN model in advancing the domain of railway safety maintenance. Full article

Though different types of commercial probiotics are supplemented in biofloc technology (BFT), very little information is available on their effects on the farmed fish. Therefore, this study focused on evaluating the effects of three most commonly used commercial probiotics on the growth performance, intestinal histomorphology, and intestinal microbiota of Nile tilapia (Oreochromis niloticus) reared in BFT. Tilapia fry, with an average weight of 3.02 ± 0.50 g, were stocked at a density of 60 fry/0.2 m³, and cultured for 90 days. Three commercial probiotics were administered, with three replications for each: a single-genus multi-species probiotic (Bacillus spp.) (T1), a multi-genus multi-species probiotic (Bacillus sp., Lactobacillus sp., Nitrosomonas sp., Nitrobacter sp.) (T2), and a multi-species probiotic (Bacillus spp.) combined with enzymes including amylase, protease, cellulase, and xylanase (T3). The results showed significant variations in growth and feed utilization, with T3 outperforming other treatments in terms of weight gain, liver weight, and intestine weight. Adding Bacillus spp. with enzymes (T3) to water significantly increased the histomorphological parameters (villi length, villi depth, crypt depth, muscle thickness, intestinal thickness) as well as microbes (total viable count and total lactic acid bacteria) of intestine of fish compared to T1 and T2, leading to improved digestion and absorption responses. It is concluded that the supplementation of commercial probiotics has potential benefits on farmed fish species in BFT. Full article