Physical Failure Research Articles

Interface Degradation of LaCl3-Based Solid Electrolytes Coupled with Ultrahigh-Nickel Cathodes.

Despite competitive compatibility with high-nickel cathodes, chloride solid electrolytes (SEs) still experience inevitable side reactions at the cathode/SE interface, causing capacity decay in all-solid-state lithium batteries (ASSLBs) during cycling. Herein, a three-electrode ASSLB testing device is developed to comprehensively reveal the interface failure mechanisms of the ultrahigh-nickel LiNi0.92Co0.05Mn0.03O2 (NCM92) cathode paired with LaCl3-based chloride SE Li0.447La0.475Zr0.059Ta0.179Cl3 (LLZTC). Distribution of relaxation time (DRT) analysis clearly shows the ASSLB degradation accompanied by a significant NCM92/LLZTC interface impedance increase, which becomes more pronounced at the higher cutoff charging voltage of 4.8 V vs Li+/Li. Furthermore, time-of-flight secondary ion mass spectrometry (ToF-SIMS) and focused ion beam scanning electron microscopy (FIB-SEM) analysis also confirm the deterioration arising from active lattice oxygen and loss of physical contact at the NCM92/LLZTC interface. These findings reveal both electrochemical degradation and physical contact failure at the cathode/SE interface as key causes of the ASSLBs' capacity decay.

Unraveling Magnet Structural Defects in Permanent Magnet Synchronous Machines—Harmonic Diagnosis and Performance Signatures

Rare-earth-based permanent magnets (PMs) have a vital role in numerous sustainable energy systems, such as electrical machines (EMs). However, their production can greatly harm the environment and their supply chain monopoly presents economic threats. Alternative materials are emerging, but the use of rare-earth PMs remains dominant due to their exceptional performance. Damage to magnet structure can cause loss of performance and efficiency, and propagation of cracks in PMs can result in breaking. In this context, prolonging the service life of PMs and ensuring that they remain damage-free and suitable for re-use is important both for sustainability reasons and cost management. This paper presents a new harmonic content diagnosis and motor performance analysis caused by various magnet structure defects or faults, such as cracked or broken magnets. The proposed method is used for modeling the successive physical failure of the magnet structure in the form of crack formation, crack growth, and magnet breakage. A surface-mounted permanent magnet synchronous motor (PMSM) is studied using simulation in Ansys Maxwell software (Version 2023), and different cracks and PM faults are modeled using the two-dimensional finite element method (FEM). The frequency domain simulation results demonstrate the influence of magnet cracks and their propagation on EM performance measures, such as stator current, distribution of magnetic flux density, back EMF, flux linkage, losses, and efficiency. The results show strong potential for application in health monitoring systems, which could be used to reduce the occurrence of in-service failures, thus reducing the usage of rare-earth magnet materials as well as cost.

Intelligent Fault Diagnosis in Industrial Machinery: Leveraging AI with LSTM Autoencoder for Enhanced Fault Detection

Machinery Fault Detection (MFD) is an important process in contemporary industrial systems, where it predicts possible physical failures before they lead to a serious problem. This uses multiple technologies to monitor machine statuses (algorithms, data gathering systems and sensors) Using a servo-motor driven actuator for deployment, the Locking Mechanism is pre-assembled into an OEM ATE and will enable predictive failure mode identification (via monitoring and warnings of operational parameters i.e., vibration, temperature or auditory signals in-built to MFD systems) leading to Prophylactic maintenance before critical bottlenecks can occur. The dataset we used in our study was collected from Kaggle and it is called the SpectraQuest Machinery Fault Simulator (MFS) Alignment-Balance-Vibration (ABVT). We used LSTM Autoencoder, KNN, SVM and DNN to analyzed the data. Our LSTM Autoencoder model was very accurate and achieved a precision, recall, accuracy and F-score of 99%. We worked on very large scale datasets. It will help the system detect faults and predict their evolution over time, so you save maintenance costs and increase production in your factory. More research on the practical efficiency of these models in real-time across different industrial settings can create a path towards improved and scalable MFD solutions.

Supramolecular Association of a Block Copolymer via Strong Hydrogen Bonding to Form Self-Healable Ionogels.

The drive to enhance the operational durability and reliability of stretchable and wearable electronic and electrochemical devices has led to the exploration of self-healing materials that can recover from both physical and functional failures. In the present study, we fabricated a self-healable solid polymer electrolyte, referred to as an ionogel, using reversible hydrogen bonding between the ureidopyrimidone units of a block copolymer (BCP) network swollen in an ionic liquid (IL). The BCP consisted of poly(styrene-b-(methyl acrylate-r-ureidopyrimidone methacrylate)) [poly(S-b-(MA-r-UPyMA)], with the IL-phobic polystyrene forming micellar cores that were interconnected via intercorona hydrogen bonding between the ureidopyrimidone units. By precisely regulating the molecular weight and the composition of the hydrogen-bondable motifs, the mechanical, electrical, and self-healing characteristics of the ionogel were systematically evaluated. The resulting ionogel samples exhibited suitable stretchability, ionic conductivity, and room-temperature self-healability due to reversible hydrogen bonding. To highlight the applicability of the self-healing ionogel as a high-capacitance gate insulator, an electrolyte-gated transistor (EGT) was fabricated using a poly(3-hexylthiophene-2,5-diyl) semiconductor, and the performance of the EGT was fully recovered from a complete cut without any external stimuli.

Influence of phospholipid head and tail molecular structures on cell membrane mechanical response under tension.

Biological cell membranes are primarily comprised of a diverse lipid bilayer with multiple phospholipid (lipid) types, each of which is comprised of a hydrophilic headgroup and two hydrophobic hydrocarbon tails. The lipid type determines the molecular structure of head and tail groups, which can affect membrane mechanics at nanoscale and subsequently cell viability under mechanical loading. Hence, using molecular dynamics simulations, the current study investigated seven membrane phospholipids and the effect of their structural differences on physical deformation, mechanoporation damage, and mechanical failure of the membranes under tension. The inspected phospholipids showed similar yield stresses and strains, as well as pore evolution and damage, but significantly different failure strains. In general, failure occurred at a lower strain for lipids with a larger equilibrium area per lipid. The obtained results suggest that larger headgroup structure, greater degree of unsaturation, and tail-length asymmetry influenced the phospholipids' ability to pack against each other, increased the fluidity and equilibrium area per lipid of the membrane, and resulted in lower failure strain. Overall, this study provides insights on how different phospholipid structures affect membrane physical responses at the molecular level and serves as a reference for future studies of more complex membrane systems with intricate biophysical properties.

Experimental study on the comparison of mechanical properties of different types of glutenite in the Ma'nan area

This study delves into the mechanical properties of various rock types found in glutenite reservoirs in the Ma'nan area of the Xinjiang oilfield. It bridges a knowledge gap by exploring the mechanical deformation and failure patterns among different glutenite types. Employing porosity-permeability tests, ultrasonic wave velocity measurements, and triaxial compression tests, this research scrutinizes physical parameters, mechanical properties, deformation, and failure modes of dolomitic sandstone, calcareous coarse sandstone, calcareous fine siltstone, and glutenite. Results highlight a porosity increase from dolomitic sandstone to glutenite, with calcareous coarse sandstone having the lowest permeability and glutenite the highest. Shear wave velocity is greater in dolomitic sandstone and calcareous coarse sandstone compared to calcareous fine siltstone, while longitudinal wave velocity is higher in dolomitic sandstone than in glutenite. Deformation behavior varies: dolomitic sandstone is primarily elastic, and calcareous sandstone and glutenite show elastoplastic characteristics. Dolomitic sandstone boasts the highest compressive strength, elastic modulus, and Poisson's ratio. Calcareous fine siltstone's compressive strength and elastic modulus fall below dolomitic sandstone, while the Poisson's ratio of calcareous coarse sandstone is three-quarters that of dolomitic sandstone. Main failure modes observed are shear failure in dolomitic sandstone, calcareous coarse sandstone, and glutenite, and axial splitting failure in calcareous fine siltstone. Microscopic analyses, including environmental scanning electron microscopy and mineral composition, shed light on the mechanical differences among the rocks. In sum, this research yields crucial insights into the mechanical traits of glutenite reservoir rocks, essential for optimizing hydraulic fracturing strategies in such reservoirs.

Physical Heart Failure Symptom Clusters Predictive of Delay in Seeking Treatment.

Recent research has focused on the relationship between heart failure (HF) symptom clusters and outcomes, including mortality, hospitalization, functional status, and quality of life. No known studies to date have explored the role of physical HF symptom clusters and delays in seeking treatment. Describe physical symptom clusters in a population of HF patients and determine if a specific cluster is predictive of delay in seeking treatment for HF symptoms. We analyzed combined data from two studies ( n = 406) collected during acute HF hospitalization. The Heart Failure Somatic Awareness Scale quantified physical HF symptoms. Delay, measured in days, was collected from the medical record and confirmed by interview. Hierarchical agglomerative clustering techniques determined physical HF symptom clusters. Hierarchical multiple regression analysis was computed to explore predictors of delay. Participants were primarily White, male sex older adults. Three physical HF symptom clusters were identified: discordant, edema-related, and dyspnea-related symptoms. Hierarchical multiple regression analysis revealed in Step 1 that age was a significant predictor of delay. Our findings provide valuable insight into the role of physical symptom clusters on delay in persons with HF. Through agglomerative hierarchical clustering techniques, we found three physical HF symptom clusters that were then used to determine differences in cluster membership by demographic and clinical variables. Significant age differences were noted by cluster membership with youngest older adults in a discordant symptom cluster.

Surface-to-bulk synergistic modification enables stable interface of Ni-rich oxide cathode for all-solid-state lithium batteries

All-solid-state lithium batteries (ASSLBs) are supposed to a highly forward-looking battery technology by reason of their conspicuous energy density and excellent safety. Ni-rich layered oxide cathodes have been extensively studied in ASSLB systems with sulfide electrolytes owing to their high voltage and preeminent specific capacity. However, the electrochemical performance of ASSLBs is severely degraded due to the interfacial physical contact failure, space charge layer effect, and chemical/electrochemical side reactions between sulfide electrolytes and Ni-rich oxide cathodes. In this work, the surface of LiNi0.92Co0.04Mn0.04O2 (NCM92) is coated with a nano-level CeO2 buffer layer and is simultaneously doped with Ce element (NCM92–1%Ce) by one-step wet chemical method. Ce doping is beneficial for maintaining structural stability and suppressing irreversible phase transition. The CeO2 buffer layer can efficaciously avoid the oxidation and decomposition phenomenon at the interface between NCM92 and sulfide electrolyte. As expected, NCM92–1%Ce cathode shows the discharge specific capacity of 141.46 mAh g−1 after 80 cycles at 0.2C with 79.32% capacity retention rate, which is much higher than that of the unmodified NCM92 cathode. The results indicate that the surface-to-bulk synergistic modification strategy can successfully improve the structure toughness and interface stability of Ni-rich oxide cathode for ASSLBs with sulfide electrolytes.

Comparison of Spectral and Temporal Processing Abilities between Adults with Stroke and Healthy Peers

Background and Aim: Perception of speech in noise (SIN) is based on the accurate extraction of spectral and temporal cues. Disruption of this process can reduce the productivity of the spectral and temporal features of the target stimulus and speech recognition in noise. Auditory processing disorder is one of the main challenges of people with stroke, which leads to social, mental, and even physical failures. This study aimed to investigate the spectral and temporal processing abilities of people with stroke compared to healthy peers. Methods: In this study, participants were 15 patients with stroke referred to the neurology clinic of Imam Khomeini Hospital and 30 healthy people aged 20–60 years. Spectral Modulation Detection Test (SMDT), Pitch Pattern Sequencing Test (PPST), Random Gap Detection Test (RGDT) and QuickSIN tests were performed for all participants. The mean scores of the two groups were compared. Results: Patients with stroke had poorer performance in SMDT, PPST, RGDT and QuickSIN tests (p<0.001) compared to healthy subjects. The results showed that there was a significant correlation between SIN test with temporal and spectral processing in the normal group, but the correlation pattern was different in people with stroke. The scores of all the tests were not significantly different between the right and left ears. Conclusion: The results showed that people with stroke have poorer performance in all tests compared to normal people and have more problems in speech perception in challenging areas. Keywords: Auditory processing disorder; speech perception in noise; spectral modulation; temporal processing; stroke

A systematic review of optimization methods for recovery planning in cyber–physical infrastructure networks: Current state and future trends

Cyber–Physical Systems are increasingly complex and frequently integrated into modern societies via critical infrastructure systems, products, and services. Consequently, there is a need for reliable functionality of these complex systems under various scenarios, from physical failures due to aging to cyber attacks. However, the development of effective strategies to restore disrupted cyber–physical infrastructure systems continues to be a major challenge. Even though there have been an increasing number of papers evaluating recovery planning in cyber–physical infrastructures networks, a comprehensive literature review focusing on mathematical modeling and optimization methods is still lacking. Thus, this study critically analyzes the literature on optimization techniques for recovery planning of cyber–physical infrastructure networks after a disruption, to synthesize key findings on the current methods in this domain. A total of 152 relevant research papers are reviewed following an extensive assessment of all major scientific databases. The main mathematical modeling practices and optimization methods are identified for both deterministic and stochastic formulations, categorizing them based on the solution approach (exact, heuristic, metaheuristic), objective function, and network size. Having identified the gaps, a set of future trends for both the methodology and application of optimization algorithms is presented. Overall, there is a need to shift toward scalable optimization solution algorithms, empowered by data-driven methods and machine learning algorithms, to provide reliable and computationally efficient decision-support systems for decision-makers and practitioners.

The suffering of students in physical education. Unsettling experiences and situational conditions

ABSTRACT Background The state of research on unsettling experiences of students in physical education can be characterized as fragmented. The available studies focus on select emotions (e.g. fear), specific forms of discrimination (e.g. ableism), or different types of social interactions (e.g. bullying). In this way, the state of research reveals different aspects of students’ suffering in physical education (PE) in a puzzle-like manner, while systematic investigations of the overall picture of PE as a complex meaningfully structured phenomenon remain open. Purpose The study presented in this paper aims to systematically capture the breadth of unsettling PE experiences by taking different perspectives of students into account, while also doing justice to PE in its situational logic. Based on a social constructivist approach, the following research questions will be addressed: (1) What kind of experiences are interpreted as unsettling by students in PE? (2) Which situational conditions are associated with students’ unsettling PE experiences? Methods A qualitative research approach based on written short narratives was employed to answer the research questions. Current and former secondary school students (N = 677) from Germany participated. Grounded theory coding methods and sequential analysis were used for the data analysis. Results Our data indicates a wide range of students’ unsettling PE experiences (e.g. public humiliation, social exclusion, and forms of violence). On a supra-individual level, two major categories of unsettling PE experiences were identified: (1) The vulnerability of the students perceived through revealed inadequacies and (2) the social oppression of the supposedly ‘lazy, weak and unfit’. Students’ experience of vulnerability can be traced back to situations of physical exposure and performance failure while their experience of social oppression draws back to suppressive actions by teachers and fellow students. For each of these categories, we identify typical ways in which students deal with their unsettling experience. On a structural level, a hierarchizing culture of PE that fosters unsettling experiences is revealed. Discussion As a central strength, our study can provide condensed explanations for the emergence and consequences of unsettling PE experiences from the students’ perspective. Our findings relate to and enrich existing discourses regarding the situational emergence of vulnerability, overarching mechanisms of marginalization, and the hierarchizing culture of PE as a basis for social oppression. It is particularly tragic that affected students feel left alone and can hardly escape harmful situations, which perpetuates their suffering. Thus, pedagogical concepts that emphasize students’ diversity and the dismantling of hierarchies are needed and should be advanced further.

Remaining Useful Life Prediction Of Precision Bearing Based on Multi-Head Attention Mechanism

The high-precision bearing is the core component of the space actuator, whose remaining useful life (RUL) prediction is an essential issue of constructing condition-based maintenance (CBM) system. To address the current challenges of establishing traditional physical failure models for high-precision bearings and dealing with long time-series vibration data, this paper proposes a precision bearing remaining useful life prediction model based on multi-head attention mechanism. The model utilizes a one-dimensional convolutional neural network (CNN) to compress the input features along the time dimension, then, an encoder-decoder architecture with a multi-head attention mechanism for further feature exploration. Experiments are conducted using vibration signals from the IEEE PHM 2012 Challenge dataset for training and validation. The results indicate that this algorithm exhibits relatively smaller relative errors compared to other algorithms, demonstrating good accuracy and strong stability. This approach shows promise in accurate and reliable precision bearing life prediction.

Chaos quantum optimization‐based layered diagnosis framework for faulty sensor node diagnosis and classification in wireless sensor networks

SummaryNode faults are often influenced by factors such as physical component failure, communication module errors, battery exhaustion, and environmental factors. Various researchers have contributed to addressing the problem of detecting, diagnosing, and classifying faults in sensor networks. However, computational complexity and fault occurrence probability increase with the size of the network. In this context, an enhanced optimized layered diagnosis framework (OLDF) is proposed for detecting, diagnosing, and classifying sensor node faults. OLDF utilizes a deep belief network (DBN) with chaos quantum‐behaved particle swarm optimization (CQ‐PSO) for efficient diagnosis and classification of various sensor faults in the network. The proposed OLDF consists of two layers. In the first layer, optimized DBN with CQ‐PSO algorithm is employed to determine the fault type of the sensor nodes. Then, the fault type of sensor nodes that are classified in the first layer is given as input into the second layer of the OLDF algorithm to determine the fault severity in the network. Experimentation has been carried out by using the NS3 simulator for the OLDF framework with performance metrics such as classification accuracy, diagnosis accuracy, false positives, fault alarm rate, and average energy consumption. The validity of the proposed framework has been verified through comparisons with existing works such as MFD, INSA, FDRFC, and HFD. From the comparative analysis carried out, it is apparent that the proposed framework achieves a high classification accuracy of 89.94% and diagnosis accuracy of 93.21%, with less false positive rate, and utilizes minimum energy when compared with its counterparts.

New insights into the catastrophic slope failures at Sau Mau Ping: the role of antecedent rainfall pattern

Climate change is increasing the frequency and intensity of extreme rainfall events, which aggravate the threat to the safety of natural and man-made slopes. There is growing interest in the role of rainfall characteristics in these slope failures. Most of previous studies treated the rainfall individually or as cumulative value and used hypothetical rainfall temporal patterns with no association with actual physical failures. In this study, the two deadly landslides in Sau Mau Ping, Hong Kong, in June 1972 and August 1976, which caused 165 casualties, are revisited. An intriguing question that has long been overlooked is posed: why the slopes that withstood the 1972 rainfall failed in the 1976 rainfall, given that the rainfall intensity of the latter event was only half of the former? Based on an extensive review of the forensic reports and relevant studies on the failure events, numerical modeling is carried out by a combination of seepage analysis with stability analysis and unsaturated shear strength theory. Focus is placed on the geological and hydrological settings and the rainfall characteristics, particularly the temporal pattern of the antecedent and main rainfall, to look into the causes and mechanisms for these failures. Implications of the new findings for future research and practice are also highlighted.

Testing nanometer memories: a review of architectures, applications, and challenges

Newer defects in memories arising from shrinking manufacturing technologies demand improved memory testing methodologies. The percentage of memories on chips continues to rise. With shrinking technologies (10 nm up to 1.8 nm), the structure of memories is becoming denser. Due to the dense structure and significant portion of a chip, the nanometer memories are highly susceptible to defects. High-frequency specifications, the complexity of internal connections, and the process variation due to newer manufacturing technology further increased the probability of the physical failure of memories to a great extent. Memories need to be defect-free for the chip to operate successfully. Therefore, testing embedded memories has become crucial and is taking significant test costs. Researchers have proposed multiple approaches considering these factors to test the nanometer memories. They include using new fault models, march algorithms, memory built-in self-test (MBIST) architectures, and validation strategies. This paper surveys the methodologies presented in recent times. It discusses the core principles used in them, along with benefits. Finally, it discusses key opens in each and offers the scope for future research.

Research on Implementation and Management Evaluation System for Protection and Utilization of Traditional Villages:A Case Study of Traditional Chinese Villages in Lingnan Waterfront

The protection and utilization of traditional villages have seen an increase around China since the country issued the catalogue of tra-ditional village protection. However, the effect of the protection and utilization of traditional villages has not been remarkable, as physical decline and constructive failure of traditional villages occur frequently. Consequently, traditional villages are faced with challenges through a lack of evalua-tion and poor construction management. As an important feedback tool, conducting an evaluation can shed light on achievements in implementation of protection and utilization of traditional villages and effectively reveal problems impeding such implementation. Establishing a scientific evalua-tion system for traditional village protection and utilization is of great significance to the sustainable protection and utilization of traditional villages. Currently, China has not introduced an evaluation system for the implementation of traditional village protection and utilization, and only limited re-search has been conducted in this area. Existing studies have emphasized the evaluation of the implementation of protection and utilization but pay only limited attention to the implementation process. Based on a comprehensive analysis of the characteristics of implementation planning and man-agement, and of relevant studies on this topic, the current study examined the methods used to evaluate the effect of protection and utilization, the protection measures and supervision structures from the perspective of achievements and processes around implementation. Moreover, a case study based on the traditional villages in Lingnan Waterfront was used in the current study, with a focus on the local characteristics of the implementation and management of traditional village protection and utilization. On this basis, an evaluation index system is established, which comprises settlement protection and planning implementation, an active utilization degree, new-old settlement collaboration, protection measures, implementation supervi-sion and management. In Lingnan Waterfront, 44 national-level traditional villages are chosen as samples to examine the implementation and man-agement of traditional village protection and utilization. The results shows that the implementation of traditional village protection and utilization in Lingnan Waterfront poses obvious non-equilibrium challenges. Concerning the implementation of traditional village protection and utilization, there are three levels: Good, moderate and poor. Government policy support, urbanization and industrialization represent important factors that influence the implementation and management of traditional village protection and utilization. For traditional villages that are protected, utilized and managed well, the implementation and management of their protection and utilization are aligned with the early attention paid by the Chinese Government to the situation, as well as the impacts of both industrialization and urbanization. Traditional villages which exhibit moderate implementation and man-agement of protection and utilization are often influenced by industrialization and urbanization in rural areas in the Pearl River Delta. Driven by demolition of old houses and the construction of new housing sites for villagers facing a land shortage, the rural landscape and appearance of villages have undergone different degrees of damage. For traditional villages with poor implementation and management of protection and utilization, the locations of such villages are significantly influenced by urbanization or industrialization in the Pearl River Delta. As traditional villages are attrac-ted by the economic benefits, protection and utilization encounter severe impacts caused by urbanization and the construction of industrial parks in rural areas, and are stuck in a dilemma. Ultimately, the implementation and management evaluation system of traditional village protection and utili-zation is established in the current study based on the case study of Lingnan Waterfront. The results of the study not only deepen the existing body of knowledge on the implementation evaluation of traditional villages, but also provide a reference point for action to address the situation.

Medical device report analyses from MAUDE: Device and patient outcomes, adverse events, and sex-based differential effects

Post-market medical device-associated failures and patient problems are reported in Medical Device Reports (MDRs) to the US Food and Drug Administration. Reports are accessible through Manufacturer and User Facility Device Experience (MAUDE), a database including both required and voluntary submissions. We present an overview of >10 million MDRs received from 2011 to 2021. Approximately 92% of reporting issues represent medical device physical or functional failures, categorized from 1704 codes related to medical device integrity or function. ∼8% were coded adverse events (AEs). Patient outcomes are reported via 998 patient codes in 19 medical specialties (cardiovascular, orthopedic, etc.). ∼40% of patient reports indicated “no health consequences”; however, a small number of devices had consistently high AE reports. While overall reports did not exhibit a sex-based dichotomy, ∼9% of the reported AEs occurred more frequently in females, many of which were related to immune effects. The analyses are subject to uncertainties and potential bias based on data available and data selected for analysis. However, such an overview of post-market MDR data, not previously published, fills a gap in understanding medical device issues and patient-based outcomes related to medical device use. Trends identified may be subjects of additional hypotheses, analysis, and research.

Laboratory High-Contrast X-ray Microscopy of Copper Nanostructures Enabled by a Liquid-Metal-Jet X-ray Source.

High-resolution imaging of Cu/low-k on-chip interconnect stacks in advanced microelectronic products is demonstrated using full-field transmission X-ray microscopy (TXM). The comparison of two lens-based laboratory X-ray microscopes that are operated at two different photon energies, 8.0 keV and 9.2 keV, shows a contrast enhancement for imaging of copper nanostructures embedded in insulating organosilicate glass of a factor of 5 if 9.2 keV photons are used. Photons with this energy (Ga-Kα radiation) are generated from a Ga-containing target of a laboratory X-ray source applying the liquid-metal-jet technology. The 5 times higher contrast compared to the use of Cu-Kα radiation (8.0 keV photon energy) from a rotating anode X-ray source is caused by the fact that the energy of the Ga-Kα emission line is slightly higher than that of the Cu-K absorption edge (9.0 keV photon energy). The use of Ga-Kα radiation is of particular advantage for imaging of copper interconnects with dimensions from several 100 nm down to several 10 nm in a Cu/SiO2 or Cu/low-k backend-of-line stack. Physical failure analysis and reliability engineering in the semiconductor industry will benefit from high-contrast X-ray images of sub-μm copper structures in microchips.

Identifying failure types in cyber-physical water distribution networks using machine learning models

Abstract Water cyber-physical systems (CPSs) have experienced anomalies from cyber-physical attacks as well as conventional physical and operational failures (e.g., pipe leaks/bursts). In this regard, rapidly distinguishing and identifying a facing failure event from other possible failure events is necessary to take rapid emergency and recovery actions and, in turn, strengthen system's resilience. This paper investigated the performance of machine learning classification models – support vector machine (SVM), random forest (RF), and artificial neural networks (ANNs) – to differentiate and identify failure events that can occur in a water distribution network (WDN). Datasets for model features related to tank water levels, nodal pressure, and water flow of pumps and valves were produced using hydraulic model simulation (WNTR and epanetCPA tools) for C-Town WDN under pipe leaks/bursts, cyber-attacks, and physical attacks. The evaluation of accuracy, precision, recall, and F1-score for the three models in failure type identification showed the variation of their performances depending on the specific failure types and data noise levels. Based on the findings, this study discussed insights into building a framework consisting of multiple classification models, rather than relying on a single best-performing model, for the reliable classification and identification of failure types in WDNs.

Accelerometer-Measured Physical Activity, Sedentary Time, and Heart Failure Risk in Women Aged 63 to 99 Years

Heart failure (HF) prevention is paramount to public health in the 21st century. To examine incident HF and its subtypes with preserved ejection fraction (HFpEF) and reduced EF (HFrEF) according to accelerometer-measured physical activity (PA) and sedentary time. This was a prospective cohort study, the Objective Physical Activity and Cardiovascular Health (OPACH) in Older Women study, conducted from March 2012 to April 2014. Included in the analysis were women aged 63 to 99 years without known HF, who completed hip-worn triaxial accelerometry for 7 consecutive days. Follow-up for incident HF occurred through February 2022. Data were analyzed from March to December 2023. Daily PA (total, light, moderate to vigorous PA [MVPA], steps) and sedentary (total, mean bout duration) behavior. Adjudicated incident HF, HFpEF, and HFrEF. A total of 5951 women (mean [SD] age, 78.6 [6.8] years) without known HF were included in this analysis. Women self-identified with the following race and ethnicity categories: 2004 non-Hispanic Black (33.7%), 1022 Hispanic (17.2%), and 2925 non-Hispanic White (49.2%). There were 407 HF cases (257 HFpEF; 110 HFrEF) identified through a mean (SD) of 7.5 (2.6) years (range, 0.01-9.9 years) of follow-up. Fully adjusted hazard ratios (HRs) for overall HF, HFpEF, and HFrEF associated with a 1-SD increment were 0.85 (95% CI, 0.75-0.95), 0.78 (95% CI, 0.67-0.91), and 1.02 (95% CI, 0.81-1.28) for minutes per day total PA; 0.74 (95% CI, 0.63-0.88), 0.71 (95% CI, 0.57-0.88), and 0.83 (95% CI, 0.62-1.12) for steps per day; and 1.17 (95% CI, 1.04-1.33), 1.29 (95% CI, 1.10-1.51), and 0.94 (95% CI, 0.75-1.18) for minutes per day total sedentary. Cubic spline curves for overall HF and HFpEF were significant inverse for total PA and steps per day and positive for total sedentary. Light PA and MVPA were inversely associated with overall HF (HR per 1 SD: 0.88; 95% CI, 0.78-0.98 and 0.84; 95% CI, 0.73-0.97) and HFpEF (0.80; 95% CI, 0.70-0.93 and 0.85; 95% CI, 0.72-1.01) but not HFrEF. Associations did not meaningfully differ when stratified by age, race and ethnicity, body mass index, physical function, or comorbidity score. Results for sedentary bout duration were inconsistent. Higher accelerometer-measured PA (MVPA, light PA, steps per day) was associated with lower risk (and greater total sedentary time with higher risk) of overall HF and HFpEF in a racially and ethnically diverse cohort of older women. Increasing PA and reducing sedentary time for primary HFpEF prevention may have relevant implications for cardiovascular resilience and healthy aging in later life.