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ABSTRACT Objective: In the following years after the United States Preventive Service Task Force (USPSTF) recommendation against prostate cancer screening with PSA in 2012, several authors worldwide described an increase in higher grades and aggressive prostate tumors. In this scenario, we aim to evaluate the potential impacts of USPSTF recommendations on the functional and oncological outcomes in patients undergoing robotic-assisted radical prostatectomy (RARP) in a referral center. Material and Methods: We included 11396 patients who underwent RARP between 2008 and 2021. Each patient had at least a 12-month follow-up. The cohort was divided into two groups based on an inflection point in the outcomes at the end of 2012 and the beginning of 2013. The inflection point period was detected by Bayesian regression with multiple change points and regression with unknown breakpoints. We reported continuous variables as median and interquartile range (IQR) and categorical variables as absolute and relative percent frequencies. Results: Group 1 had 4760 patients, and Group 2 had 6636 patients, with a median follow-up of 109 and 38 months, respectively. In the final pathology, Group 2 had 9.5% increase in tumor volume, 24% increase on Gleason ≥ 4+3 (ISUP 3), and 18% increase on ≥ pT3. This translated to a 6% increase in positive surgical margins and 24% reduction in full nerve sparing in response to the worsening pathology. There was a significant decline in post-operative outcomes in Group 2, including a 12-month continence reduction of 9%, reduction in potency by 27%, and reduction of trifecta by 22%. Conclusions: The increasing number of high-risk patients has led to worse functional and oncologic outcomes. The initial rapid rise in PSM was leveled by the move towards more partial nerve sparing. Among some historical changes in prostate cancer diagnosis and management in the period of our study, the USPSTF recommendation coincided with worse outcomes of prostate cancer treatment in a population who could benefit from PSA screening at the appropriate time.

ABSTRACT Introduction Urolift® is a surgical modality to treat lower urinary tract symptoms (LUTS) in patients with enlarged prostates (1). However, the inflammatory process caused by the device usually displaces the prostate’s anatomical landmarks and challenges surgeons performing robotic-assisted radical prostatectomy (RARP). In this video, we will illustrate several technical challenges in patients with Urolift ® who underwent RARP. Material and Methods We performed a video compilation with several surgical steps illustrating key aspects and critical details of the anterior bladder neck access, lateral bladder dissection from the prostate, and posterior prostate dissection to avoid ureteral and neural bundles injuries. Results We perform our RARP technique with our standard approach in all patients (2 -6). The beginning of the case is performed like every patient with an enlarged prostate. We first identify the anterior bladder neck and then complete its dissection with Maryland and Scissors. However, extra care must be taken in the anterior and posterior bladder neck approach due to the clips found during the dissection. The challenge starts when opening the lateral sides of the bladder until the base of the prostate. It is crucial to perform the bladder neck dissection beginning at the internal plane of the bladder wall. Such dissection is the easiest way to recognize the anatomical landmarks and potential foreign materials, such as clips, placed during previous surgeries. We cautiously work around the clip to avoid using cautery on the top of the metal clips because energy is transmitted from one edge to the other of the Urolift ®. This can be dangerous if the edge of the clip is close to the ureteral orifices. The clips are usually removed to minimize cautery conduction energy. Finally, after isolating and removing the clips, the prostate dissection and subsequent surgical steps are continued with our conventional technique. Before proceeding, we ensure that all clips are removed from the bladder neck to avoid complications during the anastomosis. Conclusions Robotic-assisted radical prostatectomy in patients with Urolift ® is challenging due to modified anatomical landmarks and intense inflammatory processes in the posterior bladder neck. When dissecting the clips placed next to the base of the prostate, it is crucial to avoid cautery because energy conduction to the other edge of the Urolift ® can cause thermal damage to the ureters and neural bundles.

Nowadays, blended learning represents a combination of both e-learning and Face-to-Face learning approaches, which has been considered as an emerging concept of modern education. It has been regarded as a prominent alternative learning approach compared to the conventional e-learning approach. The degree of satisfaction of students with blended learning played a crucial role in evaluating the effectiveness of adopted techniques for blended learning. Therefore, this study examines a number of key factors that affect students' satisfaction within a blended learning environment among undergraduate students involved in a private university in Saudi Arabia. In this paper, a comprehensive online questionnaire is used to assess the impact of blended learning based on seven factors of the blended learning environment. The respondents for this study are generally 221 young undergraduate students with an average age of 20-30 years old. The collected data is analysed by using SmartPLS 3. The results reveal that students are mostly satisfied with the blended learning factors, particularly, with the convergence of the Face-to-Face and Videoconferencing classes and the role of their instructors. Those students are moderately satisfied with the SIS, the online forums, and their course materials and modules, and least satisfied with the LMS and the E-library factors. Accordingly, the suggested recommendations pertaining to the future research are highlighted where this paper offers useful insights for future researchers based on different empirical evidences.

This paper investigates theoretically and experimentally the influence of using basalt fiber (BF) on bond strength of recycled coarse aggregate (RCA) concrete. The natural aggregate (NA) was replaced by RCA with 20 %, 40 %, 60 %, 80 %, and 100 %. BF was added with three different percentages. Ninety concrete cubes were tested under pull-out loading to failure using steel bar diameters 12, 14, and 18 mm. The behavior of specimens was analyzed by recording ultimate tensile loads and crack patterns. The experimental bond strengths were correlated with the theoretical strengths calculated using equations of different international codes in addition to existing models by previous researchers. The experimental results revealed that the bond strength improved when the RCA levels and BF contents increased. The 80 % RCA and 1.5 % BF showed the highest increase. Also the bond strength decreased as the bar diameter increased. The mode of failure for all specimens in this study was splitting failure and BF caused ductile failure. The experimental bond strengths were higher than the calculated theoretical strengths. Thus, theoretical equations can be used for determining bond strength of RCA.

In this research, the performance of hot mix asphalts mixes (HMA) with RAP and RCA was evaluated, in terms of their Marshall stability, flow, and volumetric properties, to verify their applicability as a replacement for the natural aggregate in the flexible pavement surface layers of HMA mixtures. The experimental work was divided into two phases; the first phase investigated the crushed limestone, white hard-rock, and basalt aggregate materials with a nominal maximum aggregate size of 19 mm. The second phase studied the replacement of crushed limestone, by weight, with the RAP and RCA at different asphalt cement contents. The coarse and fine aggregate of the recycled materials were used at four percentages of 25%, 50%, 75%, and 100%, respectively. The experimental test results showed that replacing limestone with recycled aggregate materials affected the mechanical characteristics, mainly the volumetric properties of the HMA. Using RCA in the hot asphalt mixes violated the upper limit of 5% for the air voids property and increased the optimum asphalt content (OAC) up to 5.96% for mixes prepared with 50% RCA. The results also revealed that adding RAP aggregate to the limestone in the HMA improved their Marshall stability, and the highest value of 29.32 kN was recorded for the asphalt mixes prepared with 75% RAP at 3% asphalt content. The combination of RAP and RCA aggregates indicated that as the RCA proportion in the mix increased, lower load capacity was observed and higher OAC compared to the other asphalt mixes.

This paper presents a comparative study on the Reinforced Concrete (RC) wall design equations for one-way action panels given in the ACI 318–19, Eurocode 2, Australian standards (AS 3600), British standards (BS 8110–1:1997), and other proposed equations in the literature. In addition, ABAQUS software was used to predict the axial compressive capacity of walls. Comparisons are made among different aspects such as the slenderness ratio (0 ≤ H/t ≤ 42), concrete compressive strength (f'c = 21, 28, 35, and 42-MPa), steel yield stress (fy = 300 and 420-MPa), and reinforcement ratios (ρ = 0.5%, 1%, 2%, and 4%). The force carried by the steel and concrete individually was also investigated. Moreover, ABAQUS software was used to calculate the first eigen-value of the wall and the result was compared with the calculations of Euler's buckling formula. The results indicate that the change in the design axial ratio for low H/t was negligible. However, the axial capacity decreased non-linearly with increasing the H/t for (H/t > 6) and increased linearly with increasing f'c. The capacity increased linearly with the increase of the steel ratios; however, the increment recorded by ABAQUS was not linear. Further, when increasing both f'c and ρ, the axial capacity increases; however, this increase is not constant and decreases with increasing the steel ratios. By increasing f'c, the contribution of concrete to the axial capacity increased while the contribution of the steel decreased. In addition, buckling of RC walls is not the dominant failure mode.

This paper presents experimental and analytical research to study the shear and flexural performance of Reinforced Concrete (RC) beams strengthened using two types of externally bonded Carbon Fiber Reinforced Polymer (CFRP) composites; including laminates and sheets. A total of 24 full-scale RC beams were tested up to failure. Concrete compressive strengths of 17, 32, and 47 MPa were used, with CFRP composites attached in six different configurations in an effort to optimize the flexural and shear load-carrying capacities of the specimens tested. Results showed that externally bonded CFRP composites considerably improved both the shear and flexural strengths of the beams. Notably speaking, the effectiveness of the CFRP composite material was inversely proportional to the concrete compressive strength. Significant improvement of the shear strength ranging from 28.9% to 29.6% was observed for beams with CFRP U-wraps compared to beams with laminate strips which ranged between 14.4% and 23.8% when the compressive strength was increased from 17 to 47 MPa. Meanwhile, those strengthened with half U-wraps had a notable improvement in their flexural strength (i.e., ultimate load carrying capacity) by a range of 22.4%–46.2%, as opposed to laminate strengthened beams that varied between 17.8% and 38.4% with increased compressive strength of the concrete mix. Finally, theoretical predictions using the ACI 440.2R-14 guidelines were compared to experimental results; however, the differences turned out to be relatively large due to the intrinsic empirical nature of the Code predicted equations.

This research paper aims to evaluate the performance of hot asphalt mixes containing reclaimed asphalt pavement (RAP) at different percentages. The usage of RAP as a recycled construction material has become a common practice in many countries nowadays. To achieve this objective, experimental tests were conducted on a total of 15 asphalt mixtures prepared with five different contents of asphalt cement, including 3.5, 4, 4.5, 5, and 5.5%. Three Marshall mixes were made for each asphalt content. Following the ASTM standards, Marshall stability and flow tests were performed to determine the optimum asphalt content of each mix tested (i.e., control, 25% RAP, 50% RAP, and 75% RAP mixes, respectively). The results showed an improvement in the stability and flow values with the increase in RAP content. Maximum stability of 15.11 kN was obtained with a 75% RAP mix at 4% asphalt cement content. Notably, as the asphalt content increases, lower air voids values and vice versa, higher voids filled with asphalt were observed for the four tested mixes.

This experimental study presents the influence of basalt fiber on the rheological and mechanical properties and the durability behavior of self-compacting concrete (SCC). In this study, a total of five self-compacting concrete mixtures were prepared: one as a control mix and the other mixes with 0.05%, 0.1%, 0.15%, and 0.2% basalt fibers. Slump flow and V-funnel flow tests were employed to assess the influence of basalt fibers on the rheological properties of fresh self-compacting concrete (SCC). Additionally, mechanical properties, including compressive strength, splitting tensile strength, and flexural strength, were analyzed. Furthermore, the mechanical properties were assessed following exposure to elevated temperatures (400 °C and 600 °C) as well as 100 and 200 freeze-thaw (F/T) cycles. Additionally, water absorption and ultrasonic pulse velocity tests were conducted on the SCC mixes after 28 days of curing. The results revealed that the addition of fiber has a significant effect on the rheological properties of fresh SCC mixtures. As the volume of fibers increases, the reduction in rheological properties increases. Basalt fiber had no effect on the compressive strength, while the splitting and flexural strength were significantly enhanced by 33% using basalt fiber. As temperatures and freezing-thawing cycles escalated, the mechanical properties of SCC exhibited a decline. Experimental findings indicated that elevating the temperature to 600 °C resulted in a decrease of over 20% in both the tensile and compressive strengths of SCC. Moreover, the results demonstrated that the incorporation of basalt fibers substantially enhanced the mechanical properties of SCC when subjected to high temperatures and freezing-thawing cycles. In addition, water absorption increased slightly by the incorporation of basalt fiber. [ABSTRACT FROM AUTHOR]

Energy efficiency is a critical aspect of engineering due to the associated monetary and environmental benefits it can bring. One aspect in particular, namely, the prediction of heating and cooling loads, plays a significant role in reducing energy use costs and in minimising the risks associated with climate change. Recently, data-driven approaches, such as artificial intelligence (AI) and machine learning (ML), have provided cost-effective and high-quality solutions for the prediction of heating and cooling loads. However, few studies have focused on interpretable classifiers that can generate not only reliable predictive systems but are also easy to understand for the stakeholders. This research investigates the applicability of ML techniques (classification) in the prediction of the heating and cooling loads of residential buildings using a dataset consisting of various variables such as roof area, building height, orientation, surface area, wall area, and glassing area distribution. Specifically, we sought to determine whether models that derive rules are competitive in terms of performance when compared with other classification techniques for assessing the energy efficiency of buildings, in particular the associated heating and cooling loads. To achieve this aim, several ML techniques including k-nearest neighbor (kNN), Decision Tree (DT)-C4.5, naive Bayes (NB), Neural Network (Nnet), Support Vector Machine (SVM), and Rule Induction (RI)- Repeated Incremental Pruning to Produce Error (RIPPER) were modelled and then evaluated based on residential data using a range of model evaluation parameters such as recall, precision, and accuracy. The results show that most classification techniques generate models with good predictive power with respect to the heating or cooling loads, with better results achieved with interpretable classifiers such as Rule Induction (RI), and Decision Trees (DT). [ABSTRACT FROM AUTHOR]

The use of renewable energy, especially wind power, is the most practical way to mitigate the environmental effects that various countries around the world are suffering from. To meet the growing need for electricity, wind energy is, nevertheless, being used more and more. Researchers have come to understand that a near-perfect output power estimate must be sacrificed. Variations in the weather influence wind energy, including wind speed, surface temperature, and pressure. In this study, the wind turbine output power was estimated using three approaches of artificial neural networks (ANNs). The multilayer feed-forward neural network (MLFFNN), cascaded forward neural network (CFNN), and recurrent neural network (RNN) were employed for estimating the entire output power of wind turbine farms in Egypt. Therefore, each built NN made use of wind speed, surface temperature, and pressure as inputs, while the wind turbine's output power served as its output. The data of 62 days were gathered from wind turbine farm for the training and efficiency examination techniques of every implemented ANN. The first 50 days' worth of data were utilized to train the three created NNs, and the last 12 days' worth of data were employed to assess the efficiency and generalization capacity of the trained NNs. The outcomes showed that the trained NNs were operating successfully and effectively estimated power. When analyzed alongside the other NNs, the RNN produced the best main square error (MSE) of 0.00012638, while the CFNN had the worst MSE of 0.00050805. A comparison between the other relevant research studies and our suggested approach was created. This comparison led us to the conclusion that the recommended method was simpler and had a lower MSE than the others. Additionally, the generalization ability was assessed and validated using the approved methodology. [ABSTRACT FROM AUTHOR]

Natural fibres have been used in a wide range of polymer composite reinforcements to produce new composites with good properties that can be used in many applications. In this study, the fracture toughness, acoustic, and thermal properties of date palm fibre (DPF)/polyester composite and DPF/sheep wool hybrid reinforced polyester were investigated. Physical properties, such as water absorption and thickness swelling, have also been reported. Finally, scanning electron microscopy (SEM) images of the composites and hybrids were obtained. The composites and hybrids were produced by compression moulding and cut into standard shapes before being tested. DPF and DPF/sheep wool contents (wt%) were 0%, 10%, 20% and 30%. The test results indicate that the fracture toughness improved from 3.45 MPa·m1/2 (with 0% additive content) to 11.55 MPa·m1/2 (with 20% DPF content) and 13.95 MPa·m1/2 (with 20% hybrid content). The energy release rate increased from 3.99 kJ/M2 (with 0% additive content) to 39.82 kJ/M2 (with 20% DPF content) and 53 kJ/M2 (with 20% hybrid content) due to the good adhesion between the matrix and fibres. DPF improved the acoustic insulation properties of the polyester/DPF composite with mass fractions of 10%, 20%, and 30%. At a frequency of 2000 Hz, the acoustic attenuation in decibels (dB) increased from 16.2 dB with pure polyester to 17.2, 18.2, and 19.5 dB with 10%, 20%, and 30% DPF content, respectively. The increase in sheep wool and DPF hybrids in the polyester matrix increased the acoustic attenuation to 18.4, 20.6, and 24 dB, respectively, because DPF and sheep wool have good acoustic insulation properties. Thermal conductivity was 0.161 W/m·C with 0% additive content, decreasing to 0.133, 0.107, and 0.082 W/m·C with 10%, 20%, and 30% DPF content, respectively, and 0.112, 0.073, and 0.051 W/m·C for 10%, 20%, and 30% DPF/sheep wool hybrid content, respectively, owing to good thermal insulation properties of DPF and sheep wool. Water absorption gradually increased with DPF and DPF/sheep wool hybrid because DPF and sheep wool have good absorption properties. Thickness swelling increased with increasing DPF and DPF/sheep wool hybrid contents but not to the same extent. SEM images indicated good distribution and bonding of DPF with the polyester matrix up to 20% and good results for DPF/sheep wool with the polyester hybrid at 10% and 20% hybrid content. [ABSTRACT FROM AUTHOR]

Light composite materials based on Magnesium matrix reinforced with carbon or ceramic nanofibers were fabricated by powder metallurgy rout using high frequency induction heat sintering (HFIHS) technique. A comprehensive comparative study has been investigated on using ceramic nanofibers and carbon nanofibers as a fibers reinforced Magnesium metal matrix in order to evaluate the overall improvements in mechanical properties. Electrospinning technique followed by calcination process were used efficaciously to synthesize and fabricate the Titanium Oxide (TiO2) fibers and carbon fibers. Mg/nanofibers mixtures were prepared via powder metallurgy route using mechanical alloying technique by adding 1, 3, 5 and 10 wt% calcined nanofibers to Mg matrix. In inert atmosphere and room temperature, mixtures were initially processed using high energy ball milling for 15 + 15 min. The final bulk cylindrical samples were obtained by performing consolidation or sintering process using high frequency induction heat sintering furnace (HFIHS). Characterization of the sintered composites have been investigated using field emission scanning electron microscopes (FESEM), energy-dispersive X-ray spectroscopy (EDS) and X-ray diffraction (XRD). While, hardness test and compression test were performed in order to evaluate the mechanical properties of the fabricated composites. The obtained result shows that, the ultimate compressive strength increased to 281 MPa at 5 wt% TiO2, which represent about 12.4% more than the pure Mg. Hardness improved up to 64.4% in case of using the ceramic nanofibers as reinforcement. While using CNFs as reinforcement to the Mg matrix, slightly deteriorates the mechanical properties.

The United Kingdom and European codes for the analysis and design of tubular scaffold structures assume that the scaffolds are subjected primarily to vertical loads and to horizontal loads at right-angles to the scaffold. The effects of dynamic loading caused by large winds tend to be ignored and the code analyses often only require static loading on the structures to be considered. To investigate side loads, a scaffold frame built according to the UK standard was made and inserted into a testing rig. Five different load combinations were made to determine the behaviour of the scaffold under different side loads, which were varied cyclically to simulate different wind loads, especially when vertical loads were also applied. The results showed that cyclical loads affected scaffold behaviour, especially when the bases of the scaffold standards were not tied to the base at the bottom of the test rig. Changes should be made to the UK and European codes BS EN 74.1, BS 5975 and BS EN 128 11-1 for the design of scaffold structures to increase safety.

Energy efficiency is currently a hot topic in engineering due to the monetary and environmental benefits it brings. One aspect of energy efficiency in particular, the prediction of thermal loads (specifically heating and cooling), plays a significant role in reducing the costs associated with energy use and in minimising the risks associated with climate change. Recently, data-driven approaches, such as artificial intelligence (AI) and machine learning (ML) techniques, have provided cost-effective and high-quality solutions for solving energy efficiency problems. This research investigates various ML methods for predicting energy efficiency in buildings, with a particular emphasis on heating and cooling loads. The review includes many ML techniques, including ensemble learning, support vector machines (SVM), artificial neural networks (ANN), statistical models, and probabilistic models. Existing studies are analysed and compared in terms of new criteria, including the datasets used, the associated platforms, and, more importantly, the interpretability of the models generated. The results show that, despite the problem under investigation being studied using a range of ML techniques, few have focused on developing interpretable classifiers that can be exploited by stakeholders to support the design of energy-efficient residential buildings for climate impact minimisation. Further research in this area is required. [ABSTRACT FROM AUTHOR]