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Purpose: The variance between the winning bid and the owner's estimated cost (OEC) is one of the construction management risks in the pre-tendering phase. The study aims to enhance the quality of the owner's estimation for predicting precisely the contract cost at the pre-tendering phase and avoiding future issues that arise through the construction phase. Design/methodology/approach: This paper integrated artificial neural networks (ANN), deep neural networks (DNN) and time series (TS) techniques to estimate the ratio of a low bid to the OEC (R) for different size contracts and three types of contracts (building, electric and mechanic) accurately based on 94 contracts from King Saud University. The ANN and DNN models were evaluated using mean absolute percentage error (MAPE), mean sum square error (MSSE) and root mean sums square error (RMSSE). Findings: The main finding is that the ANN provides high accuracy with MAPE, MSSE and RMSSE a 2.94%, 0.0015 and 0.039, respectively. The DNN's precision was high, with an RMSSE of 0.15 on average. Practical implications: The owner and consultant are expected to use the study's findings to create more accuracy of the owner's estimate and decrease the difference between the owner's estimate and the lowest submitted offer for better decision-making. Originality/value: This study fills the knowledge gap by developing an ANN model to handle missing TS data and forecasting the difference between a low bid and an OEC at the pre-tendering phase. [ABSTRACT FROM AUTHOR]

Securing sensitive data in cloud storage systems is crucial with the increasing sophistication of cyber-attacks in today's digital ecosystem. The rising need to protect cloud storage systems from illegal access, data breaches, and new cyber threats makes the research necessary. Because of the difficulty in keeping up with the ever-changing nature of security threats and user behaviour, innovative, flexible, and dynamic ways to access management have had to be developed. The ever-changing nature of modern threats and the requirement for constant monitoring and instantaneous responses provide difficulties for cloud storage security. The present research presents Deep Reinforcement Learning-Enhanced Adaptive Access Control (DRL-AAC), which makes access control decisions that are dynamic and aware of their context using modern reinforcement learning algorithms. It has been used to design and implement a Deep Reinforcement Learning (DRL-AAC) enhanced adaptive access control system for dynamically managing cloud storage. The system should be able to make access determinations in real-time, adapting to changing security threats and user habits. DRL-AAC uses user profiling to analyze and detect abnormalities and unauthorized access based on user behaviour patterns. DRL-AAC has wide-ranging potential uses, from consumer to enterprise to government cloud storage. Because of its intelligence and flexibility, it may be used to protect data in various settings. This research provides a comprehensive simulation analysis to prove the efficiency of DRL-AAC. The experimental findings demonstrate that the suggested DRL-AAC model increases the computational intensity analysis ratio of 96.2%, level of data privacy of 97.8%, adaptability ratio of 95.9%, resource allocation efficiency of 97.5% efficiency, scalability ratio of 98.8% compared to other existing models. The purpose of the research is to demonstrate the efficacy of DRL-AAC in doing its intended task of detecting and neutralizing potential security risks with as few false positives and negatives as possible. The paper aspires to verify DRL-AAC as a ground-breaking approach to bolstering cloud storage security. It can potentially improve cloud storage security by responding in real-time to new threats and user habits, protecting sensitive information on the cloud. The proposed work is evaluated by investigating its computational intensity, data privacy, adaptability, resource efficiency, and scalability. [ABSTRACT FROM AUTHOR]

Background Objective Method Results Conclusion Sexual position is one of the several aspects that affects ejaculation control, which is essential for sexual pleasure. Still little is known, nevertheless, about the connection between sexual positions and ejaculation duration.To investigate the impact of various sexual positions on the duration of ejaculation and gain a deeper understanding of the elements that influence the ability to control ejaculation.An online survey was carried out on a sample of 1904 heterosexual men between the ages of 18–65 years. Premature ejaculation (PE) diagnostic tool was used to define PE. Demographic, behavioral, and physiological traits that are linked to PE and non‐PE groups were collected. The participants listed their preferred sexual positions and the ones they changed to during ejaculating. Also, analyses were performed between male/female active or deep/shallow thrust positions and PE status.Although there were no appreciable variations in age or circumcision between PE and non‐PE groups, the age of first sexual experience was associated with PE status. Groups also varied in the number of weekly ejaculations, the duration of the favored ejaculations, and the characteristics of the erection. The most preferred sexual posture was the doggy style regardless of the group. When the non‐PE group felt to ejaculate, they preferred to change the position significantly more than the PE group (74% vs 67.2%; p < 0.05). However, when participants felt ejaculate, non‐PE participants tended to switch to shallow thrusting positions significantly more than PE participants, who preferred deeper positions (27.1% vs. 18%; p < 0.05).This study underlines the relevance of considering sexual positions in controlling PE. Modifying positions during sexual intercourse may offer a non‐pharmacological therapeutic alternative for improving ejaculation control. Future studies in this field might help to create tailored PE treatment strategies. [ABSTRACT FROM AUTHOR]

Introduction: Pediatric acute limb ischemia (PALI) is a consequence of sudden loss of blood flow to an extremity that can progress to irreversible ischemia if not promptly treated. This may unfortunately result in life-changing consequences which include limb loss, limb length discrepancy, emotional trauma, and or financial stress. Due to the rarity of this condition, and the lack of high-quality evidence, treatment strategies have largely been anecdotal or extrapolated from treatment of the adult patient. The article is a guide to improve overall functional outcomes and reduce morbidity of PALI. Methods: The authors retrospectively analysed patient data on the electronic patient record of the hospital between January 2021 and December 2023. The patients with PALI were divided into three broad categories: iatrogenic arterial injury managed with the best medical management, managed surgically; iatrogenic deep vein injury/thrombosis, and trauma. Results: A total of 13 patients were referred during the study period. 9 were male. 8 cases were line related, 2 trauma and 3 acute deep vein thrombosis [DVT]. Two patients succumbed prior to intervention. Eight patients were managed with best medical therapy, two surgically and one by endovascular means. Conclusion: Since experience worldwide is limited with PALI, we decided to share our experience and a propose comprehensive algorithm for the management of PALI emphasizing a multi-disciplinary team approach that we follow at our hospital. Early and decisive involvement of vascular surgeons, hematologists, intervention radiologists, and pediatric intensive care physicians is necessary to achieve the most favorable outcomes.

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AbstractThis paper describes deep neural network (DNN) models based on hyperparameter optimization for the prediction of the compressive strength of concrete. The novelty of this research lies in the implementation of optimized hyperparameters to train the DNN models with the aim to enhance their predictive accuracy. Utilizing the Keras Tuner library, the most effective hyperparameters for the DNN models were identified. These models are then trained and evaluated using an experimental dataset of 1030 instances, encompassing nine quantitative variables that include various concrete mix ingredients and age. The target variable for all models is the compressive strength of concrete. The assessment of model performance, based on metrics like mean squared error (MSE) and R2 values, was conducted on previously unseen test data. The optimal configuration for the hidden layers in the model was identified as five layers, containing 12, 16, 16, 40, and 26 neurons, respectively. The optimal learning rate for the model was 0.001. With this set of optimal hyperparameters, the best DNN model achieved an MSE of 28.76 and an R2 value of 0.89 on the testing data. The results demonstrate a significant improvement in the DNN models' performance when trained with the optimized hyperparameters. In comparison to the regression model, the performance of the DNN models was significantly better. Adopting the predictive models developed in this research offers the potential for substantial cost and time savings by circumventing the need for labour-intensive and time-consuming laboratory tests.

Deep or Social Ecology? A Debate from Herbert Marcuse Marcuse After comparing the different perspectives of ‘Deep Ecology’ and ‘Social Ecology,’ the essay proceeds to analyze the Andrew Light’s proposal. Particular attention is paid to the role that Light believes Marcusian theory of nature and society could play to recompose the different perspectives.

Compared to supervised denoising models based on deep learning, the unsupervised Deep Image Prior (DIP) denoising approach offers greater flexibility and practicality by operating solely with the given noisy image. However, the random initialization of network input and network parameters in the DIP leads to a slow convergence during iterative training, affecting the execution efficiency heavily. To address this issue, we propose the Masked-Pre-training-Based Fast DIP (MPFDIP) Denoising Model in this paper. We enhance the classical Restormer framework by improving its Transformer core module and incorporating sampling, residual learning, and refinement techniques. This results in a fast network called FRformer (Fast Restormer). The FRformer model undergoes offline supervised training using the masked processing technique for pre-training. For a specific noisy image, the pre-trained FRformer network, with its learned parameters, replaces the UNet network used in the original DIP model. The online iterative training of the replaced model follows the DIP unsupervised training approach, utilizing multi-target images and an adaptive loss function. This strategy further improves the denoising effectiveness of the pre-trained model. Extensive experiments demonstrate that the MPFDIP model outperforms existing mainstream deep-learning-based denoising models in reducing Gaussian noise, mixed Gaussian–Poisson noise, and low-dose CT noise. It also significantly enhances the execution efficiency compared to the original DIP model. This improvement is mainly attributed to the FRformer network's initialization parameters obtained through masked pre-training, which exhibit strong generalization capabilities for various types and intensities of noise and already provide some denoising effect. Using them as initialization parameters greatly improves the convergence speed of unsupervised iterative training in the DIP. Additionally, the techniques of multi-target images and the adaptive loss function further enhance the denoising process. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Natural Gas Geoscience is the property of Natural Gas Geoscience and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The aim of the study was to measure the students’ cognitive engagement by considering their motivational drives during the remote learning process because of COVID-19 pandemic. Stipulated this, the study employed Biggs’s (1987) Study Process Questionnaire to measure students’ cognitive engagement enrolled in the public and private universities by offering them online learning facility. Statistically significant results were witnessed for the demographic variables (gender, university type, discipline, and prior online experience) in relation to students’ cognitive engagement. Stepwise regression analysis predicted cognitive engagement of social sciences’ students’ through surface approach and surface strategy for the postgraduate students. Furthermore, it also estimated that students’ with earlier experience of online learning predicted more profound (deep) motive drive and female students’ projected more deep strategy as compared to their male counterparts. This study presents implications to the teachers to employ useful online teaching strategies to engage students’ remotely and to organize their teaching sessions according to the needs of the learners. [ABSTRACT FROM AUTHOR]

The recommendation system can mine valuable information according to user preferences, so it is widely used in various industries. However, the performance of recommendation systems is generally affected by the problem of data sparsity, and LightGBM can alleviate the impact caused by data sparsity to a certain extent. To this end, this paper proposes a fusion recommendation model based on the LightGBM and deep learning—CLGM model. The model is composed of LighGBM, cross network and deep neural network. First, the features in the dataset are fused and extracted through LightGBM, and the feature with the highest classification accuracy is selected as the input of the neural network layer; Then, using the cross network and the deep neural network, the linear cross combination feature relationship and nonlinear correlation relationship between high-order features are respectively obtained; finally, the results obtained by the pre-order network are linearly weighted and combined to obtain the final recommendation result. In this paper, AUC and Logloss are used as evaluation indicators to verify the model on the public dataset Criteo and dataset Avazu. The simulation experiment results show that, compared with the four typical recommendation models, the recommendation effect of this model is better. [ABSTRACT FROM AUTHOR]

This paper describes deep neural network (DNN) models based on hyperparameter optimization for the prediction of the compressive strength of concrete. The novelty of this research lies in the implementation of optimized hyperparameters to train the DNN models with the aim to enhance their predictive accuracy. Utilizing the Keras Tuner library, the most effective hyperparameters for the DNN models were identified. These models are then trained and evaluated using an experimental dataset of 1030 instances, encompassing nine quantitative variables that include various concrete mix ingredients and age. The target variable for all models is the compressive strength of concrete. The assessment of model performance, based on metrics like mean squared error (MSE) and R² values, was conducted on previously unseen test data. The optimal configuration for the hidden layers in the model was identified as five layers, containing 12, 16, 16, 40, and 26 neurons, respectively. The optimal learning rate for the model was 0.001. With this set of optimal hyperparameters, the best DNN model achieved an MSE of 28.76 and an R² value of 0.89 on the testing data. The results demonstrate a significant improvement in the DNN models' performance when trained with the optimized hyperparameters. In comparison to the regression model, the performance of the DNN models was significantly better. Adopting the predictive models developed in this research offers the potential for substantial cost and time savings by circumventing the need for labour-intensive and time-consuming laboratory tests. [ABSTRACT FROM AUTHOR]

Copyright of Natural Gas Industry is the property of Natural Gas Industry Journal Agency and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: Hemangiomas are the most common vascular tumors in childhood, and the treatment options have undergone profound changes in recent years. In this study, we aimed to compare the efficacy and safety of beta-blockers on hemangiomas, both topi-cal and oral, with non-pharmacological treatment in the pediatric age group. Material and Methods: We retrospectively reviewed the medical records of pediatric patients with hemangiomas. Results: Fifty-three patients (F/M=40/13) were enrolled in this study. Superficial hemangiomas were detected in 14 (26.4%) patients, and deep hemangiomas were detected in 39 (73.6%) patients. Seventeen patients were followed without medication, 19 were treated with a topical beta blocker, and 17 were treated with an oral beta blocker. Twelve patients with superficial hemangi-omas were followed without medication, while two received topical timolol treatment. A comparison of lesion progression in patients with superficial hemangiomas in the non-pharmacological treatment and topical treatment groups showed that the mean scores of success, in terms of mean fading and reduction in lesion depth, were significantly higher at the first month (7.0 vs. 1.66; p=0.049; 6.0 vs. 1.5; p=0.045). Among patients with deep hemangiomas, a comparison of mean fading scores showed no difference between the oral and topical treatment groups in the first and fourth months (p=0.551, p=0.551). Conclusion: We believe that oral beta-blockers can be used instead of topical treatment in the future, and they will be preferred more by clinicians and families due to less side effects.

Deep rock mass is in a complex mechanical environment characterized by high ground stress, high ground temperature, high karst water pressure, and strong mining disturbance, resulting in difficult support and high levels of failure in the pump chamber group. To solve the problem of the instability of the deep pump chamber group, this paper takes the −890-level pump absorbing well chamber group of the Daqiang coal mine as the engineering background. Through theoretical analysis, numerical simulation, and field tests, the reasons for the failure of the chamber group are analyzed, and the effects of intensive design and traditional design on the stability control of the surrounding rock are compared. According to the characteristics of high constant resistance, high elongation, and energy absorption of the negative Poisson’s ratio (NPR) cable, the instability energy criterion of intersection under the NPR cable support is established, where the chamber is stable at KN ≤ 1. The intensive control strategy of the pump absorbing well with a high prestressed NPR cable and three-dimensional truss as the core is presented and applied in the field. The results show that high ground stress, low surrounding rock strength, dense chamber group distribution, unreasonable excavation sequence of the chamber group, and inappropriate support are the main reasons for the failure of the deep pump absorbing well chamber group. Compared with the traditional design, the intensive design simplifies the layout and construction procedure of the chamber by considering the absorbing well, improves the stress conditions of the chamber, reduces the displacement and stress of the roadway, makes the plastic zone range smaller and more uniform, and eliminates the spatial effect. The deformation energy of the surrounding rock is released through the high constant resistance and large deformation of the NPR cable and the reserved gap between the truss and the surrounding rock, and the deformation of the surrounding rock is limited through the application of high prestress to the NPR cable and the strength of the three-dimensional truss material, which allows for the full use of the self-bearing capacity of the surrounding rock and effectively ensures the roadway stability. The field application shows that this strategy can effectively ensure the stability of the chamber group; the deformation of the surrounding rock is controlled within 70 mm, and there is no shedding, cracking, or destruction of the sprayed layer after concrete sealing, which indicates that the technology plays an important role in controlling the stability of the deep roadway and can provide a reference for similar projects.

We herein report a case of developmental venous anomaly (DVA) with venous congestion caused by stenosis of the collecting vein that presented with intracerebral hemorrhage (ICH). A 74-year-old woman was referred to our hospital a few days after the onset of motor aphasia. Computed tomography (CT) and magnetic resonance imaging (MRI) showed ICH in the left frontal lobe. Angiography revealed DVA in the left frontal lobe in the late venous phase. Stenosis of the collecting vein of DVA at the entrance to the superior sagittal sinus was detected and accompanied by cavernous malformation (CM) beside DVA. Cone-beam CT revealed the absence of the left septal vein and hypoplastic transverse caudate veins. The patient was treated by blood pressure management and no additional neurological symptoms were detected. DVA develops to compensate for the absence of pial or deep venous systems, and generally benign and clinically asymptomatic. However, the outflow restriction of DVA causes chronic venous hypertension and the formation of CM. These abnormalities are considered to occur during post-natal life and may result in ICH. The risk of hemorrhage needs to be considered in cases of DVA with restricted venous outflow or CM. [ABSTRACT FROM AUTHOR]

Background: Low motivation and suboptimal cognitive skills are common among forensic psychiatric patients. By focusing on doing and experiencing, innovative technologies could offer an alternative to existing treatment for this patient group. One promising technology is DEEP, a VR biofeedback game that teaches diaphragmatic breathing, which has shown its potential in reducing stress in other populations. This exploratory study aimed at identifying if, how and for whom DEEP can be of added value in forensic mental healthcare. Methods: This study used a qualitative approach. Six focus groups with 24 healthcare providers and 13 semi-structured interviews with forensic psychiatric inpatients were conducted in two Dutch forensic mental healthcare organizations. All healthcare providers and patients experienced DEEP before participating. The data were coded inductively, using the method of constant comparison. Results: The data revealed six themes with accompanying (sub)codes, including (1) the possible advantages and (2) disadvantages of DEEP, (3) patient characteristics that could make DEEP more or (4) less suitable and beneficial, (5) ways DEEP could be used in current treatment, and (6) conditions that need to be met to successfully implement DEEP in forensic mental healthcare. The results showed that DEEP can offer novel ways to support forensic psychiatric patients in coping with negative emotions by practicing diaphragmatic breathing. Its appealing design might be suitable to motivate a broad range of forensic psychiatric patient groups. However, DEEP cannot be personalized, which might decrease engagement and uptake of DEEP long-term. Regarding its place in current care, DEEP could be structurally integrated in existing treatment programs or used ad hoc when the need arises. Finally, this study showed that both healthcare providers and patients would need practical support and information to use DEEP. Conclusion: With its experience-based and gamified design, DEEP could be useful for forensic mental healthcare. It is recommended that patients and healthcare providers are included in the evaluation and implementation from the start. Besides, a multilevel approach should be used for formulating implementation strategies. If implemented well, DEEP can offer new ways to provide forensic psychiatric patients with coping strategies to better control their anger. [ABSTRACT FROM AUTHOR]

This paper focuses on the use of truss topology optimization using an evolutionary approach to find optimized, additively manufactured components through a multi-objective formulation, keeping in mind application areas like 3D printing. Additive manufacturing plays a key role here, as the inherent complexity of most optimized truss structures would not allow for any different manufacturing techniques. The multi-optimization formulation consists of optimizing two conflicting objectives: minimization of structural compliance and minimization of heat flowing from the structure to its supports. A background on truss topology optimization, including a literature overview and the motivation behind the problem, is presented. The physical models and the optimization algorithms are explained and afterward, an example problem is solved and analyzed. [ABSTRACT FROM AUTHOR]

Purpose: Open reduction internal fixation of tibial plateau and pilon fractures may be complicated by deep surgical site infection requiring operative debridement and antibiotic therapy. The management of superficial surgical site infection is controversial. We sought to determine whether superficial infection is associated with an increased risk of deep infection requiring surgical debridement after fixation of tibial plateau and pilon fractures. Methods: This is a secondary analysis of data from the VANCO trial, which included 980 adult patients with a tibial plateau or pilon fracture at elevated risk of infection who underwent open reduction internal fixation with plates and screws with or without intrawound vancomycin powder. An association of superficial surgical site infection with deep surgical site infection requiring debridement surgery and antibiotics was explored after matching on risk factors for deep surgical site infection. Results: Of the 980 patients, we observed 30 superficial infections (3.1%) and 76 deep infections (7.8%). Among patients who developed a superficial infection, the unadjusted incidence of developing a deep infection within 90 days was 12.8% (95% confidence interval [CI] 1.3–24.2%). However, after a 3:1 match on infection risk factors, the 90-day marginal probability of a deep surgical site infection after sustaining a superficial infection was 6.0% (95% CI − 6.5–18.5%, p = 0.35). Conclusion: Deep infection after superficial infection is uncommon following operative fixation of tibial plateau and pilon fractures. Increased risk of subsequent deep infection attributable to superficial infection was inconclusive in these data. Level of evidence: Prognostic Level II. [ABSTRACT FROM AUTHOR]

Atrial myopathy may underlie the progression of atrial fibrillation (AF) from a treatable disease to an irreversible condition with poor ablation outcomes. Electrophysiological methods to unmask areas prone to re-entry initiation could be key to defining latent atrial myopathy. Consecutive patients referred for AF ablation were prospectively included at four institutions. Decrement evoked potential mapping (DEEP) was performed in eight left atrial sites and five right atrial sites, from two different pacing locations (endocardially from the left atrial appendage, epicardially from the proximal coronary sinus). The electrograms (EGMs) during S1 600 ms drive and after an extra stimulus (S2 at +30 ms above atrial refractoriness) were studied at each location and assessed for decremental properties. Follow-up was 12 months. Seventy-four patients were included and 85% had persistent AF. A total of 17,614 EGMs were individually analysed and measured. Nine percent of the EGMs showed DEEP properties (local delay of >10 ms after S2) with a mean decrement of 33±26 ms. DEEPs were more frequent in the left atrium than the right atrium (9.4% vs 8.0%; p<0.001) and more prevalent in persistent AF patients than paroxysmal AF patients (9.8% vs 4.6% p=0.001). Atrial DEEPs were more frequently unmasked in normal bipolar voltage areas and by epicardial pacing than endocardial pacing (9.6% vs 8.4%, respectively; p=0.004). Within the left atrium, the roof had the highest prevalence of DEEP EGMs. DEEP mapping of both atria is useful for highlighting areas with a tendency for unidirectional block and re-entry initiation. Those areas are more easily unmasked by epicardial pacing from the coronary sinus and more prevalent in persistent AF patients than in paroxysmal AF patients. [ABSTRACT FROM AUTHOR]

The slurry pump, which forms the core equipment of the deep-sea mining (DSM) system, provides lifting power for the ore from the seabed to the sea level, which is crucial for the safety of coarse ore particle transportation. Velocity slip plays a significant role in revealing the migration of the pump particles. Therefore, this study analyzes the velocity slip in a slurry pump using the computational fluid dynamics–discrete element method (CFD-DEM) for the first time. The relationship between the pump head and velocity slip was proposed and verified in this study based on the velocity triangle and Euler equation of the solid-liquid two-phase flow in the impeller. The effects of different particle sizes on the velocity slip are compared in detail. According to the computational results, the head depends on the larger velocity slip of the impeller outlet and lower velocity slip at the inlet. The peak value of the velocity slip was significantly reduced, and the peak position of the velocity slip and zero-point position moved backward for particle sizes ranging between 5-15 mm. This study provides a reference for the problems of particle migration and velocity slip in slurry pumps.

Slurry transport pumps, the central equipment of deep-sea mining (DSM) systems, provide the lifting power required for lifting mineral ores from the seafloor to the surface. The current technical challenges are associated with transport security and the economic aspects of coarse ore particles in pumps and pipelines. This paper focuses on the transportation characteristics of slurry pumps and uses theoretical methods, numerical calculations, and experimental methods to identify a feasible working mode. The geometric parameters of impeller channels in pump hydraulics significantly influence the migration properties of particles which in turn affects the overall security and economy of the system. The ratio of the impeller cross-sectional area F2/F1 (F1: cross-sectional area of the impeller outlet; F2: cross-sectional area of the impeller inlet) affects the particle passing capacity but negatively impacts pump efficiency. The percent of particles in the excellent passage interval of 0.2 s to 0.25 s increases from 25 to 43% when the number increases from 1.57 to 2.51. The pump behavior increases of the head by 5–10 m, and the efficiency decreases by 5–10%. So, the recommended span of F2/F1 is 1.57–2.00, and satisfying particle passing ability and efficiency can be achieved in this range. This study can provide a reference for the commercial transportation of slurry ores for deep-sea mining systems.

Compared to supervised denoising models based on deep learning, the unsupervised Deep Image Prior (DIP) denoising approach offers greater flexibility and practicality by operating solely with the given noisy image. However, the random initialization of network input and network parameters in the DIP leads to a slow convergence during iterative training, affecting the execution efficiency heavily. To address this issue, we propose the Masked-Pre-training-Based Fast DIP (MPFDIP) Denoising Model in this paper. We enhance the classical Restormer framework by improving its Transformer core module and incorporating sampling, residual learning, and refinement techniques. This results in a fast network called FRformer (Fast Restormer). The FRformer model undergoes offline supervised training using the masked processing technique for pre-training. For a specific noisy image, the pre-trained FRformer network, with its learned parameters, replaces the UNet network used in the original DIP model. The online iterative training of the replaced model follows the DIP unsupervised training approach, utilizing multi-target images and an adaptive loss function. This strategy further improves the denoising effectiveness of the pre-trained model. Extensive experiments demonstrate that the MPFDIP model outperforms existing mainstream deep-learning-based denoising models in reducing Gaussian noise, mixed Gaussian–Poisson noise, and low-dose CT noise. It also significantly enhances the execution efficiency compared to the original DIP model. This improvement is mainly attributed to the FRformer network’s initialization parameters obtained through masked pre-training, which exhibit strong generalization capabilities for various types and intensities of noise and already provide some denoising effect. Using them as initialization parameters greatly improves the convergence speed of unsupervised iterative training in the DIP. Additionally, the techniques of multi-target images and the adaptive loss function further enhance the denoising process.

BackgroundLow motivation and suboptimal cognitive skills are common among forensic psychiatric patients. By focusing on doing and experiencing, innovative technologies could offer an alternative to existing treatment for this patient group. One promising technology is DEEP, a VR biofeedback game that teaches diaphragmatic breathing, which has shown its potential in reducing stress in other populations. This exploratory study aimed at identifying if, how and for whom DEEP can be of added value in forensic mental healthcare.MethodsThis study used a qualitative approach. Six focus groups with 24 healthcare providers and 13 semi-structured interviews with forensic psychiatric inpatients were conducted in two Dutch forensic mental healthcare organizations. All healthcare providers and patients experienced DEEP before participating. The data were coded inductively, using the method of constant comparison.ResultsThe data revealed six themes with accompanying (sub)codes, including (1) the possible advantages and (2) disadvantages of DEEP, (3) patient characteristics that could make DEEP more or (4) less suitable and beneficial, (5) ways DEEP could be used in current treatment, and (6) conditions that need to be met to successfully implement DEEP in forensic mental healthcare. The results showed that DEEP can offer novel ways to support forensic psychiatric patients in coping with negative emotions by practicing diaphragmatic breathing. Its appealing design might be suitable to motivate a broad range of forensic psychiatric patient groups. However, DEEP cannot be personalized, which might decrease engagement and uptake of DEEP long-term. Regarding its place in current care, DEEP could be structurally integrated in existing treatment programs or used ad hoc when the need arises. Finally, this study showed that both healthcare providers and patients would need practical support and information to use DEEP.ConclusionWith its experience-based and gamified design, DEEP could be useful for forensic mental healthcare. It is recommended that patients and healthcare providers are included in the evaluation and implementation from the start. Besides, a multilevel approach should be used for formulating implementation strategies. If implemented well, DEEP can offer new ways to provide forensic psychiatric patients with coping strategies to better control their anger.

Endoscopic procedures are routinely applied to cancer screening programs and surveillance. The preferred technique is usually deep sedation with propofol being a convenient agent allowing for a quicker patient recovery while maintaining a similar safety profile compared to traditional agents. However, adverse events, including respiratory depression and consequent undesirable cardiovascular side effects, may occur. The goal of this work is to evaluate the patient safety impact of adding capnography during endoscopic procedures under deep propofol sedation. Data were retrospectively collected from patients undergoing deep, procedural sedation for gastrointestinal (GI) endoscopy in October 2019 to January 2021 in a single Turkish university hospital. Included in the analysis were all adult patients classified by the American Society of Anesthesiologists (ASA) as I–IV, who were scheduled for GI endoscopy utilizing propofol alone or in combination. Data on 1840 patients were collected, of whom 1610 (730 pre- and 880 post-capnography implemention) met inclusion criteria. The primary outcome was a change in the composite incidence of mild oxygen desaturation (SpO2 75–90% for <60 s), severe oxygen desaturation (SpO2 < 75% anytime or <90% for >60 s), bradycardia (<60 ppm), and tachycardia (>25% from baseline). Without capnography, on average, 7.5 events of the primary endpoint were observed per 100 procedures and 2.9 with additional capnography monitoring (p < 0.001). A significant reduction was observed for mild oxygen desaturation, with a resulting odds ratio of 0.25 (95% CI 0.14 to 0.46). ASA I patients had the highest difference in combined incidence of any oxygen desaturation of 5.85% in the pre-capnography group and 0.64% in the post-capnography group. Although procedural sedation using propofol is not associated with severe adverse events, the incidence of composite adverse events could be reduced with the addition of capnography monitoring. [ABSTRACT FROM AUTHOR]

Copyright of Ahi Evran Medical Journal is the property of Ahi Evran University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study aims to shed light on the inclined direct stress paths of curved struts in deep ring beams by converting them into real members. Five specimens were tested with three loading and three supporting points. Three specimens were conventional rings and two were in the form of a frame that took its cross-sectional dimensions from the strut-and-tie model (STM) in ACI 318-19. The effect of reinforcing struts was studied by reinforcing the rings with both proposed and conventional reinforcements, and the frames with the proposed reinforcement only. The findings show that the proposed reinforcement reduced weight and cost by approximately 18% and 13%, respectively, and provided openings for services by approximately 24%. Additionally, strut curvature was found to reduce load capacity by 3 to 6%, meaning that the STM is safe and can be used efficiently in this type of deep curved member. [ABSTRACT FROM AUTHOR]

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Deep coal resources are the crucial backup for China's energy security in the future. However, large deformation and instability of rock mass in deep roadway engineering still poses serious problems in the deep "three high and one complex" geomechanical environment. Therefore, this study departed from the support surrounding rock interaction and developed a series of NPR anchor bolts/cables with high strength, high constant resistance and large deformation mechanical properties. It constructed the constitutive equation of NPR anchor bolts, and carried out comprehensive mechanical tests indoor and on-site to verify the unique mechanical properties of NPR anchor bolts/cables. This study analyzed the interaction relationship between NPR support and surrounding rock, derived the constitutive relationship of rock mass supported by NPR, and elucidated the excavation compensation mechanical effect after adopting NPR coupling support, which revealed the high pre-stress NPR coupling support mechanism. By drawing on the actual working conditions of Daqiang Coal Mine, this study proposed the NPR support technology for intensive chambers group of deep pump house suction well with NPR bolt /cable as the core. The numerical analysis and field monitoring data show that the high prestress NPR coupling support technology can significantly improve the bearing characteristics of the support surrounding rock, effectively reduce the distribution and development range of the plastic zone of the rock mass in the roadway engineering, homogenize the support surrounding rock stress field, and reduce the overall deformation of the surrounding rock by more than 68 %, ensuring the long-term stability of the rock mass in the deep roadway engineering. The research results can provide reference for stability control of deep roadway engineering under similar conditions.

This paper provides a techno-economic assessment of coupling VVER-1000 Power Plants and hybrid desalination processes, including MED-TVC and RO systems, with a total 100,000 m3/day freshwater capacity. DEEP and DE-TOP tools initially developed by IAEA for evaluating nuclear desalination projects are used here. To reduce the environmental effect of rejecting waste brine and increasing the efficiency of the RO system, in the proposed hybrid desalination system, the waste of MED-TVC is used as the feed water of the RO system. DE-TOP obtained the most efficient extraction steam point to supply MED-TVC system with 90MWth at the point with a pressure of 1.1 MPa and mass flux of 121 kg/s. An intermediate circuit is considered to ensure there will be no contamination into the produced water to comply with IAEA safety requirements. In this manner, the power plant's cogeneration efficiency reaches 33% to 34%. Furthermore, the financial results show freshwater's levelized cost is almost 1.06 $/m3.

Energy efficiency investments are subject to various risks and uncertainties, which may affect involved actors’ profitability, and as such, there is a need for developing tools to support their decisions. So far, the developed methodological frameworks that aim at projecting the profitability and risk of such investments present a high technical complexity, and involved actors have difficulties in digesting their results. Consequently, despite their technical and academic value, the existing frameworks cannot achieve the essence of their scope, i.e., to provide concrete implications to capital providers. This article introduces the concept of IRR curve, as an appropriate tool for assessing the economic performance of energy efficiency investments from investors’ point of view. Based on this tool, an integrated framework for profitability assessment of energy efficiency investments is developed, by linking their economic performance and riskiness to investor preferences. The added value of this methodological framework lies in the comparability that it provides between different energy efficiency investments, thereby facilitating investment selection. Utilising this framework, capital providers can be supported in the selection of the country, sector, and project type to invest, while on the other hand, they can better comprehend the risk nature of energy efficiency investments as well as project their profitability and optimal investment holding period. To demonstrate the functionality of the proposed framework, we applied it to the German energy efficiency sector. Our findings provide evidence for the profitability potential of energy efficiency investments and implications about investment holding period.