Operational Criteria Research Articles

A Trusted Execution Environment RISC-V System-on-Chip Compatible with Transport Layer Security 1.3

The Trusted Execution Environment (TEE) is designed to establish a safe environment that prevents the execution of unauthenticated programs. The nature of TEE is a continuous verification process with hashing, signing, and verifying. Such a process is called the Chain-of-Trust, derived from the Root-of-Trust (RoT). Typically, the RoT is pre-programmed, hard-coded, or embedded in hardware, which is locally produced and checked before booting. The TEE employs various cryptographic processes throughout the boot process to verify the authenticity of the bootloader. It also validates other sensitive data and applications, such as software connected to the operating system. TEE is a self-contained environment and should not serve as the RoT or handle secure boot operations. Therefore, the issue of implementing hardware for RoT has become a challenge that requires further investigation and advancement. The main objective of this proposal is to introduce a secured RISC-V-based System-on-Chip (SoC) architecture capable of securely booting a TEE using a versatile boot program while maintaining complete isolation from the TEE processors. The suggested design has many cryptographic accelerators essential for the secure boot procedure. Furthermore, a separate 32-bit MicroController Unit (MCU) is concealed from the TEE side. This MCU manages sensitive information, such as the root key, and critical operations like the Zero Stage BootLoader (ZSBL) and key generation program. Once the RoT is integrated into the isolated sub-system, it becomes completely unavailable from the TEE side, even after booting, using any method. Besides providing a secured boot flow, the system is integrated with essential crypto-cores supporting Transport Layer Security (TLS) 1.3. The chip is finally fabricated using the Complementary Metal–Oxide–Semiconductor (CMOS) 180 nm process.

The useful lean tissue and appendicular skeletal muscle mass indices related to physical performance in patients undergoing hemodialysis

BACKGROUND: Low muscle quantity commonly seen in patients undergoing hemodialysis (HD) is the key contributor of declined physical performance and increases the risk of morbidity and mortality. However, how to normalize muscle mass for operational criteria in this population remains unknown. OBJECTIVE: To identify the clinically useful whole body lean tissue mass (LTM) and appendicular skeletal muscle mass (ASM) indices pertinent to physical performance in patients undertaking HD. METHODS: Whole body LTM in 38 and ASM in 22 patients undergoing HD were measured by body composition monitor (BCM) and dual-energy X-ray absorptiometry (DXA), respectively. Physical performance was assessed by handgrip strength, the incremental shuttle walk test, sit-to-stand tests, gait speed, Short Physical Performance Battery and Duke Activity Status Index. Besides crude LTM and ASM, the other muscle indices were utilized normalizing for height, height squared, weight, body mass index (BMI), fat mass and body fat%. RESULTS: Regardless of BCM or DXA being used, the relationships between different muscle mass indices and physical function were not consistent. While the most useful LTM index which was strongly associated with physical function involved adjustment for height, the strongest (and most useful) ASM index was normalization for BMI. CONCLUSION: The superiority of adjustment for BMI or height (height2) recommended by international sarcopenia consensus is also suitable for patients undergoing HD. Patients’ BMI or fat mass should be considered in estimating prevalence of sarcopenia and evaluating relationship between muscle mass and physical performance.

Multi-objective evolutionary algorithm based flexible assembly job-shop rescheduling with component sharing for order insertion

This paper addresses a rescheduling problem in an order-driven flexible assembly job-shop, which encompasses a complete production line from machining to assembly. Some products have a multi-level assembly structure, and certain components can be shared among different products. When an order with tight delivery date is inserted, rescheduling is necessary for adjusting original production scheme and tradeoffs among makespan, total tardiness and count of machine changes. To formulate the flexible assembly job-shop rescheduling problem with component sharing (FAJRP-CS), a mixed-integer linear programming model is established. The model involves three categories of decision variables that determine the machine assignment, shared component allocation, and operation sequencing. Meanwhile, solution of FAJRP-CS is analyzed by using graph model, and a neighborhood structure containing two-type move for critical operations is proposed for local optimizing. Then, a hybrid non-dominated sorting genetic algorithm with tabu search (NSGATS) is developed to maintain diversity while improving convergence. The approach is first compared with 8 algorithms on nine test instances, and the effectiveness of the operators, framework and neighborhood structure are discussed separately. Subsequently, NSGATS is applied to solve the multi-stage rescheduling problem in a medical device workshop with 6 products and 56 components. The experimental results demonstrate that NSGATS is effective for solving the FAJRP-CS.

Surfactant-modified coconut coir powder (SMCCP) as a low-cost adsorbent for the treatment of dye-contaminated wastewater: parameters and adsorption mechanism.

The dye-contaminated wastewater discharged from various industries such as dye manufacturing, paint, textile, paper, and cosmetic is a prime source of surface water pollution having serious detrimental effects on both the environment and human beings. These hazardous dyes when exposed to water obstruct the penetration of sunlight into the water and thus restrain aquatic plants from generating photosynthetic compounds. Moreover, some dyes are potential cancer-causing and also negatively impact the human nervous and respiratory systems. In this current study, modification of coconut coir powder (CCP) was carried out through cationic surfactant treatment and was successively utilized as the adsorbent for decoloring anionic dye (acid blue 185 (AB 185)) containing waste stream. Further, a comparative investigation of the dye removal efficiency of raw CCP and surfactant-modified coconut coir powder (SMCCP) as the adsorbent was studied. On surfactant treatment, using a very minimal SMCCP dosage of 8.3g/L, a very high percentage dye removal of 98.4% is possible, whereas with raw CCP, even after using a higher dosage of 14g/L, only 70.1% dye removal can be achieved. Characterization of SMCCP adsorbent was done by Fourier transform infrared, thermogravimetric, X-ray, and scanning electron microscope analyses. Furthermore, the optimization of critical operating parameters was investigated for the effective adsorption of AB 185 dye in batch mode. The adsorption of AB 185 onto SMCCP was a thermodynamically spontaneous endothermic process, following the Langmuir isotherm and pseudo-second-order kinetic model. Moreover, regeneration of exhausted SMCCP by 0.1 (M) NaOH was achieved with a satisfactorily high recovery of 97% in the first cycle. Subsequently, SMCCP can be successfully reutilized for five consecutive cycles with a loss of 17.6% in the total adsorption capacity. With all such advantages, the present study delivers a new paradigm to utilize the novel adsorbent SMCCP as a promising eco-friendly adsorbent aided by its advantage of regeneration and reusability for the treatment of dye-contaminated wastewater.

Preintervention Wait Time and Survival in People With Rheumatic Heart Disease in Uganda

BackgroundThere is an unmet surgical burden among people living with rheumatic heart disease (RHD) in Uganda. Nevertheless, risk factors associated with time to first intervention and preoperative mortality are poorly understood. MethodsIndividuals with RHD who met indications for valve surgery were identified using the Uganda National RHD Registry (January 2010-August 2022). Kaplan-Meier estimates and multivariable Cox proportional hazard models were used. ResultsOf the cohort with clinical RHD, 64% (1452 of 2269) met criteria for an index operation. Of those, 13.5% obtained a surgical intervention, whereas 30.6% died before the operation. The estimated likelihood of first operation was 50% at 9.3 years of follow-up (95% CI, 8.1-upper limit not reached). Intervention was more likely in men vs women (hazard ratio [HR], 1.78; 95% CI, 1.21-2.64), those with postsecondary education vs primary school or less (HR, 3.60; 95% CI, 1.88-6.89), and those with a history of atrial fibrillation (HR, 2.78; 95% CI, 1.63-4.76). Surgical intervention was less likely for adults vs those aged <18 years (HR, 0.49; 95% CI, 0.32-0.77) and those with New York Heart Association Functional Class III/IV vs I/II (HR, 0.51; 95% CI, 0.32-0.83). The median preoperative survival time among those awaiting first operation was 4.6 years (95% CI, 3.9-5.7 years). History of infective endocarditis, right ventricular dysfunction, pericardial effusion, atrial fibrillation, and having surgical indications for multiple valves were associated with increased probability of death. ConclusionsOur analysis revealed a prolonged time to first surgical intervention and high preintervention death for RHD in Uganda, with factors such as age, sex, and education level remaining barriers to obtaining surgery.

Effect of pillar width on the stability of the salt cavern field for energy storage

Abstract Effective planning of the cavern field involves determining the optimal pillar width between the caverns and the feasible number of caverns based on geological and mining conditions. The proper design of the pillar width is crucial to ensure the stability of the cavern field and the rational utilization of the rock salt deposit. The stability of the pillars is a complex problem influenced by various factors, including rock salt creep, changes in the cavern pressure during operational cycles, mechanical parameters, and failure criteria of the rock salt. To address this problem, the stability of the cavern field in relation to the number of caverns and pillar widths is evaluated. The evaluation is based on the following criteria: displacements, von Mises stress, strength/stress ratio, and safety factor. Three variations of pillar width and three variants of cavern fields, differing in the number of caverns, are considered. Results show that the allowable pillar width is affected by the number of caverns in the cavern field. Moreover, the stability analysis reveals uneven stress and deformation distribution in the cavern field. When the pillar width is 2.0–3.0 times the diameter of a cavern, pillars at the centre exhibit poorer stability than those at the edges of the cavern field. However, with a narrower pillar width, the highest displacements occur at the field's edges. The findings of this study provide a valuable date in the planning, design, and operation of new cavern fields for the underground storage of energy sources such as oil, natural gas, hydrogen, and compressed air in rock salt deposits.

Assessment of Cyber Resilience in the Maritime Domain Using System Dynamics and Analytical Hierarchy Process (AHP)

The maritime industry is increasingly dependent on digital technology, making it vulnerable to cyber threats. Every stakeholder is exposed to cybersecurity risks and challenges. This research aims to provide an assessment and simulation model of cyber resilience in the maritime domain, supported by qualitative descriptive statistical methods. This study is also supported by system dynamics and Analytical Hierarchy Process (AHP). The data were obtained from an eight-member expert panel (academics and practitioners) and pertain to the research period January 2022 – February 2023 and the Indonesian Sea area. Research results on global weight revealed the threat (MC-1), vulnerability (MC-2), and technologies (MC-3) sub-criteria as the most important, with the global weight of 0.102 each, followed by the navigation (MO-2), and governance and compliance (CR-6) sub-criteria with the global weight of 0.072 and 0.065, respectively. Maritime cyber resilience evaluation is based on three main criteria. The maritime operation criterion has the highest resilience value, with overall evaluated value of maritime cyber resilience being in the acceptance resilience category level 4, with the value of 3.535 (70.701%). Furthermore, changes in the maritime cyber resilience value in the 2022-2025 period are still at level 4 (acceptance resilience). Maritime cyber resilience is expected to stagnate at its current level 4 in 2023. In the third and fourth year (2024-2025), a downward trend in the maritime cyber resilience value is expected.

Cost-Efficient Oceanographic Instrument with Microfabricated Sensors for Measuring Conductivity, Temperature and Depth of Seawater.

The design, fabrication and characterization of a cost-efficient oceanographic instrument with microfabricated sensors for measuring conductivity, temperature and depth of seawater are presented. Conductivity and temperature sensors were fabricated using MEMS technology, which allows for customized small footprints and low production costs. Dedicated electronics for reading, processing and storing acquired sensor data are described. The developed instrument enables the measurement of seawater conductivity in a range from 4 mS/cm to 70 mS/cm. The conductivity measurement is temperature-compensated in the range from 2 °C to 40 °C, with an accuracy of ±0.1 mS/cm. The temperature sensor's stability is 0.025 °C. The depth/pressure measurement range is up to 2000 m/200 bar, with a resolution of 0.1 bar. Temperature and conductivity sensor performance was assessed using laboratory equipment and designed electronics. The conductivity sensor was temperature-compensated to 0.01 mS/cm. The conductivity sensor electrode corrosion effect is presented below and was eliminated through adaptation of a signal acquisition circuit. Custom software was developed for monitoring critical conductivity sensor parameters (currents, voltages). A variation of 0.4% between cell conductance currents and voltages was established as a criterion for stable conductivity sensor operation.

Consulting the map: Bil Keane’s dotted-line drawings in The Family Circus and comics cartography

ABSTRACT This essay gives much-needed attention to what are commonly known as ‘the dotted-line’ comics in Bil Keane’s popular newspaper series, The Family Circus. These beloved instalments traced the pathway of one of the child characters – usually Billy but occasionally Jeffy – as they travelled through the house, around the yard, or across the neighbourhood to complete some task. Together with being a delightful Sunday feature, Keane’s dotted-line compositions contain complicated critical, cultural, and creative operations. From their largely unknown historical touchstone to their complex narratological, temporal, and semiotic workings, these instalments both merit and reward close critical analysis. The dotted-line drawings are noteworthy for another reason, though: they call attention to the role of maps and mapping in comics as a whole. Visual charts, topographic drawings, and schematic diagrams have a long, rich, and important history in sequential art in the United States. The Family Circus offers both a poignant example of this tradition and an equally productive entry point for examining it. The dotted-line drawings in the series ostensibly document Billy or Jeffy’s pathway around the neighbourhood. As this essay demonstrates, however, what these drawings ultimately do is chart a pathway into new critical territory: comics cartography.

Persuasive Strategies in Email Marketing: An Analysis of Appeal and Influence in Business Communication

This study examines the employment of persuasive strategies in informational emails that market products and/or services, illustrating how these strategies influence target customers and persuade them to make purchases. A corpus of 850 emails, encompassing over a million words, was compiled and analyzed using a mixed-method approach that integrated both quantitative and qualitative measures. The emails were collected between 2020 and 2021. The categorization of persuasive strategies was directed by predefined operational definitions and criteria, informed by Aristotle's model of persuasion. The analysis identified 11 persuasive strategies utilized within the email corpus. Notably, the findings revealed that the offering appeal and the appeal to authority are the most commonly used strategies, whereas the contrasting appeal and romantic expressions are the least employed. These results underscore the importance of persuasive strategies in business communication, especially within informational emails. The insights derived from this study carry significant implications for businesses in crafting compelling marketing messages. Furthermore, the findings contribute to English for Business Purposes courses, particularly in English as a Foreign Language (EFL) contexts, by offering guidance on constructing persuasive business emails.

Symmetry-Enabled Resource-Efficient Systolic Array Design for Montgomery Multiplication in Resource-Constrained MIoT Endpoints

In today’s TEST interconnected world, the security of 5G Medical IoT networks is of paramount concern. The increasing number of connected devices and the transmission of vast amounts of data necessitate robust measures to protect information integrity and confidentiality. However, securing Medical IoT edge nodes poses unique challenges due to their limited resources, making the implementation of cryptographic protocols a complex task. Within these protocols, modular multiplication assumes a crucial role. Therefore, careful consideration must be given to its implementation. This study focuses on developing a resource-efficient hardware implementation of the Montgomery modular multiplication algorithm over GF(2l), which is a critical operation in cryptographic algorithms. The proposed solution introduces a bit-serial systolic array layout with a modular structure and local connectivity between processing elements. This design, inspired by the principles of symmetry, allows for efficient utilization of resources and optimization of area and delay management. This makes it well-suited for deployment in compact Medical IoT edge nodes with limited resources. The suggested bit-serial processor structure was evaluated through ASIC implementation, which demonstrated substantial improvements over competing designs. The results showcase an average area reduction of 24.5% and significant savings in the area–time product of 26.2%.

Factor analysis of lifetime psychopathology and its brain morphometric and genetic correlates in a transdiagnostic sample

There is a lack of knowledge regarding the relationship between proneness to dimensional psychopathological syndromes and the underlying pathogenesis across major psychiatric disorders, i.e., Major Depressive Disorder (MDD), Bipolar Disorder (BD), Schizoaffective Disorder (SZA), and Schizophrenia (SZ). Lifetime psychopathology was assessed using the OPerational CRITeria (OPCRIT) system in 1,038 patients meeting DSM-IV-TR criteria for MDD, BD, SZ, or SZA. The cohort was split into two samples for exploratory and confirmatory factor analyses. All patients were scanned with 3-T MRI, and data was analyzed with the CAT-12 toolbox in SPM12. Psychopathological factor scores were correlated with gray matter volume (GMV) and cortical thickness (CT). Finally, factor scores were used for exploratory genetic analyses including genome-wide association studies (GWAS) and polygenic risk score (PRS) association analyses. Three factors (paranoid-hallucinatory syndrome, PHS; mania, MA; depression, DEP) were identified and cross-validated. PHS was negatively correlated with four GMV clusters comprising parts of the hippocampus, amygdala, angular, middle occipital, and middle frontal gyri. PHS was also negatively associated with the bilateral superior temporal, left parietal operculum, and right angular gyrus CT. No significant brain correlates were observed for the two other psychopathological factors. We identified genome-wide significant associations for MA and DEP. PRS for MDD and SZ showed a positive effect on PHS, while PRS for BD showed a positive effect on all three factors. This study investigated the relationship of lifetime psychopathological factors and brain morphometric and genetic markers. Results highlight the need for dimensional approaches, overcoming the limitations of the current psychiatric nosology.

An Ergonomic Risk Assessment System Based on 3D Human Pose Estimation and Collaborative Robot

Human pose estimation focuses on methods that allow us to assess ergonomic risk in the workplace and aims to prevent work-related musculoskeletal disorders (WMSDs). The recent increase in the use of Industry 4.0 technologies has allowed advances to be made in machine learning (ML) techniques for image processing to enable automated ergonomic risk assessment. In this context, this study aimed to develop a method of calculating joint angles from digital snapshots or videos using computer vision and ML techniques to achieve a more accurate evaluation of ergonomic risk. Starting with an ergonomic analysis, this study explored the use of a semi-supervised training method to detect the skeletons of workers and to estimate the positions and angles of their joints. A criticality index, based on RULA scores and fuzzy rules, is then calculated to evaluate possible corrective actions aimed at reducing WMSDs and improving production capacity using a collaborative robot that supports workers in carrying out critical operations. This method is tested in a real industrial case in which the manual assembly of electrical components is conducted, achieving a reduction in overall ergonomic stress of 13% and an increase in production capacity of 33% during a work shift. The proposed approach can overcome the limitations of recent developments based on computer vision or wearable sensors by performing an assessment with an objective and flexible approach to postural analysis development.

HARNESSING ARTIFICIAL INTELLIGENCE FOR REAL-TIME QUALITY ASSURANCE IN MEDICAL DEVICE MANUFACTURING

The production process for medical devices must precisely follow quality assurance (QA) procedures to comply with the sector's stringent regulatory requirements. Although conventional QA procedures are generally effective, they can be time-consuming and resource-intensive, which can lead to problems and increased costs. With its unprecedented potential for increased productivity, accuracy, and scalability, Artificial Intelligence (AI) has revolutionized quality assurance (QA) approaches across industries since its inception. In this study, we look at how artificial intelligence (AI) could improve medical device quality assurance procedures. Artificial intelligence (AI) methods such as computer vision, machine learning, and natural language processing can automate and optimize critical QA operations, allowing manufacturers to expedite production workflows, while improving product quality. Systems powered by artificial intelligence can sift through mountains of data in search of irregularities, defects, and faults, and they can do it in real-time. This lessens the likelihood of non-compliance problems and enables proactive response. Furthermore, QA systems driven by AI offer the ability to learn and adapt, which allows them to continuously improve performance by analyzing input and meeting evolving regulatory requirements.

Sensitivities of critical operating conditions on proton exchange membrane fuel cell performance: an experimental study

Abstract The performance of the Proton Exchange Membrane Fuel Cell (PEMFC) is subject to substantial influence from operating conditions. In this paper, the impact of various operating conditions on the polarization curves is examined through single-variable polarization curve tests. Notably, it is observed that the polarization curves intersected within the range of 1.0 A/cm2 to 1.5 A/cm2 under varying cathode inlet humidity and working pressure conditions. Expanding upon this observation, an orthogonal experiment is designed and executed to evaluate the sensitivity of PEMFC performance to diverse operating conditions under two distinct current densities of 1.0 A/cm2 and 2.0 A/cm2. Consequently, it is revealed that the output voltage, charge transfer impedance, and mass transfer impedance are most significantly influenced by three specific operating conditions: temperature, cathode inlet humidity, and stoichiometric ratio. Furthermore, as the current density increases, the dominant factors governing PEMFC performance have evolved from temperature-dominated charge transfer impedance to cathode stoichiometric ratio-dominated mass transfer impedance.

Space Division and WGAN-GP Based Fast Generation Method of Practical Dynamic Security Region Boundary

Fast and accurate transient stability analysis is crucial to power system operation. With high penetration level of wind power resources, practical dynamic security region (PDSR) with hyper plane expression has outstanding advantages in situational awareness and series of optimization problems. The precondition of obtaining accurate PDSR boundary is to locate sufficient points around the boundary (critical points). Therefore, this paper proposes a space division and Wasserstein generative adversarial network with gradient penalty (WGAN-GP) based fast generation method of PDSR boundary. First, the typical differential topological characterizations of dynamic security region (DSR) provide strong theoretical foundation that the interior of DSR is hole-free and the boundaries of DSR are tight and knot-free. Then, the space division method is proposed to calculate critical operation area where the PDSR boundary is located, tremendously compressing the search space to locate critical points and improving the confidence level of boundary fitting result. Furthermore, the WGAN-GP model is utilized to fast obtain large number of critical points based on learning the data distribution of the small training set aforementioned. Finally, the PDSR boundary with hyperplanes is fitted by the least square method. The case study is tested on the Institute of Electrical and Electronics Engineers (IEEE) 39-bus system and the results verify the accuracy and efficiency of the proposed method.

Colon Cancer Detection of Colonoscopy Images Using CNN

Abstract: Colon cancer requires an early and precise diagnosis in order to be effectively treated because it is a common and potentially fatal condition. This study proposes a reliable framework for colon cancer detection through image preprocessing and transfer learning approaches. The research compares the performance of three popular convolutional neural networks (CNNs) for this task: VGG-15, ResNet-50, and AlexNet.The image preprocessing phase entails a number of critical operations, including image conversion, resizing, and contrast enhancement using histogram equalization. In order to guarantee that the neural networks receive high-quality inputs, these preprocessing techniques strive to improve the quality and consistency of the colon cancer dataset. Pre-trained models extract and classify features from big datasets using transfer learning. The preprocessed colon cancer dataset is used to fine-tune the chosen CNN architectures VGG-15, ResNet-50, and AlexNet and adapt them for this particular medical imaging purpose.The results of this study show that transfer learning has the potential for colon cancer detection and provides helpful direction for researchers and practitioners looking for the best deep-learning architectures for early cancer diagnosis. This study adds to continuing efforts to improve the precision and effectiveness of colon cancer detection techniques, ultimately leading to better patient outcomes.

A data-driven multi-objective optimization approach for enhanced methanol yield and exergy loss minimization in direct hydrogenation of CO[formula omitted]

The stability and control of process industries have historically faced obstacles due to inherent uncertainties in their operations. This work focuses on leveraging machine learning models as a surrogate to facilitate real-time optimization of operating conditions of the direct hydrogenation of CO2 to enhance methanol production while minimizing exergy losses. Numerous studies have explored steady-state exergy analysis and the impact of various process conditions on methanol production. However, there is a notable absence of research investigating the influence of exergy destruction on methanol production under dynamic conditions. Initially, a commercial software Aspen HYSYS was utilized to design, model, and simulate the methanol synthesis plant. Exergy analysis was conducted to quantify the process exergy losses, exergy efficiency, and potential areas for improvement. The process model transitioned to a dynamic mode by incorporating ±5% uncertainty into critical operating conditions, i.e., temperature, pressure, and molar flow rate, simulating real-world variability and resulting in a dataset of 370 samples. Two machine learning models; the Gaussian process regression (GPR) and artificial neural network (ANN) model were developed using the data samples to predict the process exergy losses and molar flow rate of methanol produced. To enhance the predictive capabilities of these deployed models, Bayesian optimization was employed for hyperparameter tuning. The developed models were employed as surrogates in multi-objective genetic algorithm (MOGA) environments with the aim of maximizing the production of methanol with minimum exergy losses under uncertainty. The optimized process conditions, derived from MOGA-based methods, underwent cross-validation using the Aspen model. This analysis revealed that the methanol synthesis plant exhibited an exergy loss of 2425 kW, an exergy efficiency of 97.78%, and an improvement potential of 53.74 kW. The GPR and ANN models demonstrated high correlation coefficients (R2) of 0.9928 and 0.9375, and root mean square error (RMSE) values of 38.93 and 1.122, respectively. Incorporating these machine learning models as surrogates within the optimization frameworks notably outperformed the base case, achieving maximum methanol production while minimizing exergy losses in the process.

Influence of operating conditions in nonlinear vibration of a drill-string

In this work, the vibrational analysis of an oil drill-string is carried out concerning nonlinear and nonsmooth dynamical model with coupled axial-torsional modes. This study aims to investigate the influence of operating conditions to avoid bit-bounce and stick-slip phenomena. A discrete lumped dynamic model with two degrees of freedom is considered. Dry friction and the possibility of loss of contact between the bit and the formation introduce two nonsmooth conditions into the system. Numerical simulations are performed to obtain the dynamic responses and to evaluate critical operating conditions. A parametric analysis is carried out to determine the influence of the weight on the drill bit and the angular velocity of the rotary table on the severity of critical vibrations for different drilling depths. A map is obtained, where normal operation, stick-slip, and bit-bounce responses are defined, allowing a choice of suitable parameters so that the drill-string does not experience critical vibration issues. The results indicate that the safety region, where normal operation is realized, depends on the drilling depth. However, when the angular velocity of the rotary table is equal to 10 rad/s and the weight on the drill bit is greater than 55 kN ensures normal operation for any drilling depth.

Analysis of temperature stress and critical heating temperature for hydronic airport pavement

The hydronic heated pavement system has been widely used for snow and ice removal in airports and other transportation infrastructure facilities because of its good snow melting efficiency and environment friendliness. However, the study of the temperature stress distribution in hydronic pavement and the corresponding critical operation method shows many insufficiencies, which allows security problems during system operation. This paper investigated the temperature stress of the hydronic pavement near the pipes with a 3D finite element model. This model was validated by the measured temperature stress in a full-scale hydronic snow melting experiment system. The locations of the maximum compressive and the time-varying tensile stress in the hydronic pavement were reported. The maximum temperature stresses of the hydronic pavement with various design and operation parameters were compared, and the sensitive parameters were proposed. In particular, the critical fluid temperature with various design and weather parameters was presented by comparing the maximum temperature stress in the pavement with the failure strength of concrete material. The findings show the maximum compressive stress appears near the hydronic pipe, and the maximum tensile stress appears at the midpoint between two adjacent pipes after heating for 2 h. Maximum tensile stress increases with pipe embedded depth, pipe diameter, fluid temperature, and pavement capacity, but decreases with air temperature and pipe spacing. To ensure the safety of hydronic pavement, the critical fluid temperature under different conditions is controlled within the range from 38 °C to 77 °C.