Search

Introduction: A comparative evaluation of the surgical treatment and outcome of patients with pertrochanteric fractures treated with short versus long proximal femoral nail antirotation. Materials and methods: A retrospective review was conducted of patients with pertrochanteric fractures treated between January 2011 and June 2012. In all 80 patients were enrolled in the study, of which 40 were treated with short PFNA and the remaining with long PFNA. Comparative analyses of demographic data, peri-operative outcome and complications were carried out. Results: There was no significant difference noted in the two groups with regards to Arbeitsgemeinschaft fur Osteosynthesefragen (AO) fracture classification, time from injury to surgery, blood transfusion post surgery and hospital stay. The surgical duration for a short PFNA procedure was significantly less (58 minutes) when compared to that of a long PFNA (87 minutes). Similarly intra-operative blood loss was significantly higher in the long PFNA group as compared to the short PFNA. Conclusions: A relatively quicker surgical time of just under an hour , lesser blood loss and better learning curve with trainee surgeons make short PFNA a better implant choice in the treatment of pertrochanteric fractures.

Hydrogen is a promising candidate for green energy sources for future endeavors because of its abundance on Earth. Although its storage is a major challenge for the researchers of this era because of its unsafe and highly explosive nature. The structural, optoelectronic, thermoelectric, vibrational, thermodynamic properties and hydrogen storage capacity of XMgH3 (X = Sr , Ba) are carried out by using the full potential linearized augmented plane wave (FP-LAPW) method in the DFT framework. The theoretical study about these magnesium-based metal hydride perovskites, i.e., SrMgH3 and BaMgH3, declares them structurally stable compounds in space group Pm-3m. The optimization graph for SrMgH3 and BaMgH3 reflects the lowest ground state energy, i.e., −6759 Ry and −16683 Ry, respectively. Comparatively, BaMgH3 seems to be more stable. The electronic band structures and density of states declare them pure metallic due to zero band gap and overlapping of electronic states of the valence and the conduction bands. The electrical conductivity of BaMgH3 increases up to 4. 5 × 1 0 2 0 (Ω ⋅ m ⋅ s) − 1 and thermal conductivity 1. 2 5 × 1 0 1 6 (Ω ⋅ m ⋅ s) − 1 in the temperature range 100 K to 1000 K revealing the good metallic character of BaMgH3. The optical analysis portrays the absorption of compounds in the visible range along with valance shell electrons to the weak bond of hydrogen and dissociates hydrogen molecules at a certain intensity of light. BaMgH3 compound shows minimum scattering and maximum absorption of light in the visible region up to 3 eV. The reflectivity peaks in the visible region 3.0 eV show that 40% of light energy is absorbed due to the opaque nature of BaMgH3. Both these compounds are declared thermodynamically stable due to negative free energy such as −1.20 eV for SrMgH3 and −1.50 eV energy for BaMgH3 at 1000 K, respectively. Moreover, the three acoustic modes showing zero imaginary phonon frequencies at Γ symmetry points predict these compounds' structural and thermodynamical stability. The gravimetric hydrogen storage concentration of SrMgH3 and BaMgH3 is determined as 2.637% and 1.836%, respectively. [ABSTRACT FROM AUTHOR]

This research explores the optimization of firewall systems within private cloud environments, specifically focusing on a 30-day evaluation of the Omni-Secure Firewall. Employing a multi-metric approach, the study introduces an innovative effectiveness metric (E) that amalgamates precision, recall, and redundancy considerations. The evaluation spans various machine learning models, including random forest, support vector machines, neural networks, k-nearest neighbors, decision tree, stochastic gradient descent, naive Bayes, logistic regression, gradient boosting, and AdaBoost. Benchmarking against service level agreement (SLA) metrics showcases the Omni-Secure Firewall’s commendable performance in meeting predefined targets. Noteworthy metrics include acceptable availability, target response time, efficient incident resolution, robust event detection, a low false-positive rate, and zero data-loss incidents, enhancing the system’s reliability and security, as well as user satisfaction. Performance metrics such as prediction latency, CPU usage, and memory consumption further highlight the system’s functionality, efficiency, and scalability within private cloud environments. The introduction of the effectiveness metric (E) provides a holistic assessment based on organizational priorities, considering precision, recall, F1 score, throughput, mitigation time, rule latency, and redundancy. Evaluation across machine learning models reveals variations, with random forest and support vector machines exhibiting notably high accuracy and balanced precision and recall. In conclusion, while the Omni-Secure Firewall System demonstrates potential, inconsistencies across machine learning models underscore the need for optimization. The dynamic nature of private cloud environments necessitates continuous monitoring and adjustment of security systems to fully realize benefits while safeguarding sensitive data and applications. The significance of this study lies in providing insights into optimizing firewall systems for private cloud environments, offering a framework for holistic security assessment and emphasizing the need for robust, reliable firewall systems in the dynamic landscape of private clouds. Study limitations, including the need for real-world validation and exploration of advanced machine learning models, set the stage for future research directions.

Introduction: The oncological safety of a submental flap is thought to be controversial. The objective of our study was to validate our previous study regarding the oncological safety of submental flaps in oral cavity reconstruction. Materials and Methods: An electronic database was searched from 2015 to 2021 for all head and neck tumour patients, where reconstruction was performed using a submental flap. Results: Eighty-eight oral cavity squamous cell carcinoma patients, amongst whom 71 were males (81.8%), with a mean age of 55.3 years (range: 25-79 years), were retrieved from the database. The sites of involvement were 37 buccal mucosa, 27 lower alveolus and 24 tongue. The mean follow-up was 33.5 months. The submental flap reconstruction was done for 88 patients; 3 had complete loss of flap, 17 had incomplete loss/partial necrosis, and 68 patients had uneventful recovery of the flap. We had 16 patients with local recurrence. Of these, 4 (4.5%) patients had clear margins and no lymphadenopathy at the level I at the final histopathology report. Conclusion: This study provides validation of the oncological safety of the submental flap and establishes that nodal positivity at level 1 alone does not contribute to recurrence at the primary site.

The technological revolution has transformed the area of labor with reference to automation and robotization in various domains. The employment of robots automates these disciplines, rendering beneficial impacts as robots are cost-effective, reliable, accurate, productive, flexible, and safe. Usually, single robots are deployed to accomplish specific tasks. The purpose of this study is to focus on the next step in robot research, collaborative multi-robot systems, through flocking control in particular, improving their self-adaptive and self-learning abilities. This review is conducted to gain extensive knowledge related to swarming, or cluster flocking. The evolution of flocking laws from inception is delineated, swarming/cluster flocking is conceptualized, and the flocking phenomenon in multi-robots is evaluated. The taxonomy of flocking control based on different schemes, structures, and strategies is presented. Flocking control based on traditional and trending approaches, as well as hybrid control paradigms, is observed to elevate the robustness and performance of multi-robot systems for collective motion. Opportunities for deploying robots with flocking control in various domains are also discussed. Some challenges are also explored, requiring future considerations. Finally, the flocking problem is defined and an abstraction of flocking control-based multiple UAVs is presented by leveraging the potentials of various methods. The significance of this review is to inspire academics and practitioners to adopt multi-robot systems with flocking control for swiftly performing tasks and saving energy.

Brain tumor detection is crucial for effective treatment planning and improved patient outcomes. However, existing methods often face challenges, such as limited interpretability and class imbalance in medical-imaging data. This study presents a novel, custom Convolutional Neural Network (CNN) architecture, specifically designed to address these issues by incorporating interpretability techniques and strategies to mitigate class imbalance. We trained and evaluated four CNN models (proposed CNN, ResNetV2, DenseNet201, and VGG16) using a brain tumor MRI dataset, with oversampling techniques and class weighting employed during training. Our proposed CNN achieved an accuracy of 94.51%, outperforming other models in regard to precision, recall, and F1-Score. Furthermore, interpretability was enhanced through gradient-based attribution methods and saliency maps, providing valuable insights into the model’s decision-making process and fostering collaboration between AI systems and clinicians. This approach contributes a highly accurate and interpretable framework for brain tumor detection, with the potential to significantly enhance diagnostic accuracy and personalized treatment planning in neuro-oncology.

Recently, experts have become particularly interested in the treatment of disorders caused by magnesium shortage. Hypomagnesemia is produced by a magnesium deficit in the blood, which is an additional stimulation for different diseases such as vomiting, drowsiness, nausea, loss of appetite, and so on. To compensate for this shortage, magnesium is injected as a nanoparticle in the blood (base fluid). The properties of these magnesium nanoparticles specifically their size, dispersion, and surface characteristics are crucial for optimizing their bioavailability and therapeutic efficacy. Advanced material characterization techniques ensure the stability and enhanced performance of these nanoparticles in the physiological environment. Based on these applications, the current research aims to address magnesium deficiency via Powell–Eyring nanofluid flow distorted by the linearly stretchable sheet in the region of the stagnation point. Linear thermal stratification, viscous dissipation, and Joule heating are used to reveal the heat transport features. The magnetic field is applied to the nanofluid at an angle α to further control the fluid dynamics and nanoparticle behavior. Transformations are used to create a set of dimensionless governing equations. Comparative graphs are used to explain the physical behaviors of temperature and velocity fields, demonstrating how material properties impact fluid dynamics. The well-known convergence method (homotopy analysis method) is used to solve the model by comparing graphs.

Plant growth and productivity is restricted by a multitude of abiotic stresses. These stresses negatively affect physiological and metabolic pathways, leading to the production of many harmful substances like ROS, lipid peroxides and aldehydes. This study was conducted to investigate the role of Arabidopsis ALDH3I1 gene in multiple abiotic stress tolerance.Transgenic tobacco plants were generated that overexpress the ALDH3I1 gene driven by the CaMV35S promoter and evaluated under different abiotic stresses, namely salt, drought, cold and oxidative stress. Tolerance to stress was evaluated based on responses of various growth and physiological traits under stress condition.Transgenic plants displayed elevated ALDH3I1 transcript levels compared to WT plants. The constitutive ectopic expression of ALDH3I1 conferred increased tolerance to salt, drought, cold and oxidative stresses in transgenic plants, along with improved plant growth. Transgenic plants overexpressing ALDH3I1 had higher chlorophyll content, photosynthesis rate and proline, and less accumulation of ROS and malondialdehyde compared to the WT, which contributed to stress tolerance in transgenic plants. Our results further revealed that ALDH3I1 had a positive effect on CO2 assimilation rate in plants under abiotic stress conditions.Overall, this study revealed that ALDH3I1 positively regulates abiotic stress tolerance in plants, and has future implications in producing transgenic cereal and horticultural plants tolerant to abiotic stresses. [ABSTRACT FROM AUTHOR]

Due to the recent climate change, organizations all over the globe are developing plans for reducing carbon emissions by developing clean energy technologies and energy efficient devices. However, the path for transition to green energy system is still unclear and in general, the representation of green energy supply for transition pathways is limited. Therefore, this study outlines a plan for getting Swedish energy sector completely carbon neutral by 2050. The approach can also be applicable to the majority of nations worldwide. Computer based simulations are performed on Energy PLAN software for making clean, green and sustainable energy system that can balance every component of entire energy system during the study period 2022 to 2050. This study takes into account the sustainable use of renewable sources for all economic sectors as well as the interchange of energy with nearby nations under the two scenarios. Additionally, the energy system works in tandem with other industries to create a fully carbon-free environment. The results revealed that, 50% de-carbonization is possible till 2035 and 100% de-carbonization is possible till 2050. This enables a discussion of how ambitious 10-year goals might serve as a first step toward the mid-century elimination of fossil fuels from the energy sector.

Real estate price prediction is crucial for informed decision making in the dynamic real estate sector. In recent years, machine learning (ML) techniques have emerged as powerful tools for enhancing prediction accuracy and data-driven decision making. However, the existing literature lacks a cohesive synthesis of methodologies, findings, and research gaps in ML-based real estate price prediction. This study addresses this gap through a comprehensive literature review, examining various ML approaches, including neural networks, ensemble methods, and advanced regression techniques. We identify key research gaps, such as the limited exploration of hybrid ML-econometric models and the interpretability of ML predictions. To validate the robustness of regression models, we conduct generalization testing on an independent dataset. Results demonstrate the applicability of regression models in predicting real estate prices across diverse markets. Our findings underscore the importance of addressing research gaps to advance the field and enhance the practical applicability of ML techniques in real estate price prediction. This study contributes to a deeper understanding of ML’s role in real estate forecasting and provides insights for future research and practical implementation in the real estate industry.

Objective: To correlate the operative findings of patients undergoing laparoscopic cholecystectomy, either acute or elective, with surgical complications and conversion to open cholecystectomy. Study Design: Comparative cross-sectional study. Place and Duration of Study: Combined Military Hospital and Pak-Emirates Military Hospital, Rawalpindi Pakistan, from Jul 2021 to Mar 2022. Methodology: Data from patients who underwent laparoscopic cholecystectomy was collected after gaining consent. Patients were evaluated for the correlation of operative findings of acute and nonacute gallbladder cases with the development of complications and conversion to open cholecystectomy. Results: Out of 250 patients, 205 were non-acute cases, whereas 45 were acute. The most common intraoperative complication was iatrogenic perforation of gallbladder 73(29.2%); more common in elective cases 49(19.6%) than in acute cases 24(9.6%). Post-operative complications included sub-hepatic collections 3(1.2%) and port site infections 2(0.8%). There were six overall conversions (2.4%), of which 3(1.2%) occurred in acute gallbladder cases and 3 in elective gallbladder. Acute cases were significantly more likely to be converted to open operations (p=0.039). They were found to have a significant association with gallstone spillage (p

Introduction Squamous cell carcinoma (SCC) of the maxillary alveolus is a relatively rare disease. There is lack of data on this subsite as compared with other sites. The factors that affect survival in cases of maxillary alveolar SCC are tumor stage, local and cervical metastases, histological grading, and the margin status.

Background and Aims: The preponderance of microscopic colitis (MC) in females may be associated with postmenopausal increased risk of atherosclerosis. The aim of this study was to describe the prevalence of atherosclerotic diseases in adults with MC. Methods: Retrospective observational study of patients with a diagnosis of MC or incomplete MC at our institution from 2008 to 2018. We performed a chart review and extracted data on demographics, comorbidities, medications, diagnosis, imaging, and endoscopy. Data were analyzed descriptively. Logistic regression was used to estimate the unadjusted effects of different variables on MC. Results: Of 269 patients, 265 had a MC diagnosis; 236 (89.06%) had collagenous colitis or lymphocytic colitis; and 29 (10.94%) were diagnosed with incomplete MC. Majority were female (79.55%), ≥ 65 years (59.11%), and white (88.81%). Majority had the following risk factors for atherosclerosis, smoking (52.04%), hypertension (58.21%), and hyperlipidemia (59.5%). The prevalence of coronary artery disease (CAD), peripheral artery disease (PAD), and cerebrovascular disease (CVD) was 16.79% (12.32, 21.27), 8.58% (5.23–11.94), and 7.49% (4.33–10.65), respectively. There was no difference in the prevalence of CAD and CVD after adjusting for sex. Females with MC had reduced odds of PAD compared with males. Conclusion: The prevalence of CAD, PAD, and CVD was 16.79%, 8.58%, and 7.49%, respectively. Similar to the general population, smoking, hypertension, and hyperlipidemia are risk factors for atherosclerosis in MC.