Time Savings Research Articles

Computational analysis of free-corner-induced failure of thick angle-ply composite laminates established upon a global-local model

The complex three-dimensional (3D) state of interlaminar stresses in the vicinity of a free corner in composite laminates brings about delamination, matrix cracking, and overall failure. This study focuses on devising a straightforward and efficient computational framework for analyzing the free-corner-induced failures in mechanically and thermally-loaded thick angle-ply composite laminates based on an innovative global-local finite element model. In so doing, the global region is dealt with by the first-order shear deformation theory, and the local sub-model region near the free corner is scrutinized using the 3D layer-wise theory. Distinctive risk parameters associated with the meso-level damage modes, including ply failure, delamination, and transverse matrix cracking are well-defined under the corresponding criteria. The precision of the model is ensured through verifications in which satisfactory alignment between the current outcomes and those of other available works in the literature substantiates the efficacy of the proposed approach. Furthermore, the impacts of the configuration parameters such as fiber orientation and thickness of plies upon the risk parameters are fully examined. It is found that, generally, by increasing the angle of fibers between two adjacent layers and diminishing the ply thickness, the composite laminate becomes more critical. The matrix cracking mode is dominated regarding the delamination and is in close agreement with the ply failure analysis. The proposed computational infrastructure, occasioning time and financial savings is valuable and enlightening for designing and manufacturing composite structures.

The Role of Artificial Intelligence in Integrated Marketing Communication: An Evaluation of ChatGPT

Today, the use of Artificial Intelligence (AI) systems in various fields is increasing due to technological developments. In integrated marketing communications, the use of AI in particular stands out as an important advance. It has become an important tool for marketing communications, such as creating content, providing personalized services and optimizing marketing processes. This study is a compilation article that compiles and synthesizes existing studies on the effects of AI and ChatGPT, a widely used tool of AI, on four dimensions (inform, incent, persuade, remind) of integrated marketing communications. The study comprehensively presents the key findings collected from various sources. As a result of the study, AI applications and ChatGPT increase customer interaction, participation, satisfaction, attitude, and purchase intention in integrated marketing communication. It also offers businesses advantages such as new customers, increased sales and profits, lower costs, automation in business processes, increased efficiency and time savings. However, ethical aspects of AI such as data privacy, security and transparency pose obstacles to marketing communication and also require human intervention in some aspects of marketing communication. The study may be one of the first studies for future studies by shedding light on the in-depth understanding of the use of AI in marketing communications.

Development of an Algorithm and Software for Optimal Route Selection

The paper deals with developing an optimal route selection algorithm and software, which, taking into account various criteria, ensures the adoption of efficient and economical route selection decisions. The algorithm is based on permutations for defining routes. It utilizes multi-criteria analysis methods such as TOPSIS and CRITIC, which enable the selection of optimal routes based on various factors, including cost optimization, time savings, safety measures, etc. The paper also presents the proposed algorithm's software, which helps to solve real-world problems related to route selection.

THE IMPACT OF URBAN MORPHOLOGY ON WIND FLOW IN A SEMI-ARID CLIMATE. CASE STUDY: THE OLD CITY OF CONSTANTINE, ALGERIA

Wind flow studies using wind tunnels can be very costly due to their nature and implementation. An alternative method involves using numerical methods, specifically CFD (Computational Fluid Dynamics), which offers significant time and cost savings, along with quicker and more flexible results for meaningful research outcomes. To conduct this research effectively, two software tools were required: Sketch Up for modeling the various urban morphologies, and the Fluent module of Ansys for simulations, which is recognized by the scientific community. The objective of this study was to provide an evaluation of airflow behavior in three samples of different morphologies within the old city of Constantine, namely: the traditional vernacular fabric (Zone A), the hybrid fabric (Zone B), and the colonial fabric (Zone C), based on a series of CFD simulations. The study aimed to assess the various effects of wind flow and velocity through the different typologies of the old city and identify the resulting aerodynamic effects that impact pedestrian comfort.

The double-edged sword: how cryptocurrency investments could undermine the anxiety-reducing benefits of rainy-day savings in times of economic turbulence

PurposeRainy-day savings have been an effective measure for maintaining financial stability in times of emergency. Motivated by the rapid expansion of cryptocurrencies, the present study examines how crypto investments could moderate the beneficial outcomes of rainy-day savings for alleviating financial anxiety during the most recent economic turbulence caused by the COVID-19 pandemic.Design/methodology/approachThe present study carries out multivariate logistic regression with interaction effects on the most recent 2021 cohort data from the National Financial Capability Study (NFCS).FindingsWhile rainy-day savings relate to less financial anxiety, the effect varies depending on whether an individual has invested in cryptocurrencies. Specifically, this paper finds that crypto investors experience less relief in financial anxiety from rainy-day savings than non-crypto investors. Additionally, crypto investors are more susceptible to financial stressors like job loss and financial fragility, likely due to the financial loss from investing in cryptocurrencies.Practical implicationsThe findings highlight the necessity of implementing policies and regulations, such as the newly approved Markets in Crypto-Assets (MiCA) regulation, that could raise people’s awareness of the high-risk nature of cryptocurrencies as well as offering targeted financial education for crypto investors, especially during times of market downturn.Originality/valueThis is the first attempt to study how crypto investments may weaken the benefits of rainy-day savings in reducing financial anxiety. The findings offer new insights into the beneficial outcomes of rainy-day savings for emergencies in light of individual crypto investment backgrounds. Additionally, findings from the present study also contain important implications given the rapid expansion of the cryptocurrency market as well as future economic turbulence.

MTAF–DTA: multi-type attention fusion network for drug–target affinity prediction

BackgroundThe development of drug–target binding affinity (DTA) prediction tasks significantly drives the drug discovery process forward. Leveraging the rapid advancement of artificial intelligence, DTA prediction tasks have undergone a transformative shift from wet lab experimentation to machine learning-based prediction. This transition enables a more expedient exploration of potential interactions between drugs and targets, leading to substantial savings in time and funding resources. However, existing methods still face several challenges, such as drug information loss, lack of calculation of the contribution of each modality, and lack of simulation regarding the drug–target binding mechanisms.ResultsWe propose MTAF–DTA, a method for drug–target binding affinity prediction to solve the above problems. The drug representation module extracts three modalities of features from drugs and uses an attention mechanism to update their respective contribution weights. Additionally, we design a Spiral-Attention Block (SAB) as drug–target feature fusion module based on multi-type attention mechanisms, facilitating a triple fusion process between them. The SAB, to some extent, simulates the interactions between drugs and targets, thereby enabling outstanding performance in the DTA task. Our regression task on the Davis and KIBA datasets demonstrates the predictive capability of MTAF–DTA, with CI and MSE metrics showing respective improvements of 1.1% and 9.2% over the state-of-the-art (SOTA) method in the novel target settings. Furthermore, downstream tasks further validate MTAF–DTA’s superiority in DTA prediction.ConclusionsExperimental results and case study demonstrate the superior performance of our approach in DTA prediction tasks, showing its potential in practical applications such as drug discovery and disease treatment.

Ambient artificial intelligence scribes: physician burnout and perspectives on usability and documentation burden.

This study evaluates the pilot implementation of ambient AI scribe technology to assess physician perspectives on usability and the impact on physician burden and burnout. This prospective quality improvement study was conducted at Stanford Health Care with 48 physicians over a 3-month period. Outcome measures included burden, burnout, usability, and perceived time savings. Paired survey analysis (n = 38) revealed large statistically significant reductions in task load (-24.42, p <.001) and burnout (-1.94, p <.001), and moderate statistically significant improvements in usability scores (+10.9, p <.001). Post-survey responses (n = 46) indicated favorable utility with improved perceptions of efficiency, documentation quality, and ease of use. In one of the first pilot implementations of ambient AI scribe technology, improvements in physician task load, burnout, and usability were demonstrated. Ambient AI scribes like DAX Copilot may enhance clinical workflows. Further research is needed to optimize widespread implementation and evaluate long-term impacts.

BYOD use and perception among hospital clinicians – A qualitative study

BackgroundThe increasing trend of healthcare professionals using personal devices for work, termed Bring-Your-Own-Device (BYOD), is becoming prevalent in hospitals. Despite its growing importance, there is limited empirical research addressing the real-world experiences of clinicians in using BYOD. ObjectiveThis study seeks to explore clinicians' perception and behavior with respect to BYOD use in Australian hospitals, focusing on socio-technical aspects and its impact on clinical and administrative work. MethodsFourteen semi-structured interviews were conducted with clinicians in Australian hospitals. The framework method was applied to perform a thematic analysis, identifying key socio-technical themes related to BYOD usage. ResultsClinicians reported using BYOD for various tasks, with smartphones being the most preferred device over tablets or laptops. While smartphones were favored for their convenience, laptops were used for complex tasks like viewing radiological images, and tablets were rarely utilized. The multifunctionality and convergence of BYOD devices were seen as facilitators for mobility and productivity, contributing to cost and time savings. However, significant barriers were identified, including increased risks of patient information leaks, challenges in maintaining work-life balance, and concerns about hospital-acquired infections. The study also found that doctors rated BYOD highly for its efficiency across various settings, while nurses experienced more issues with distractions and boundary management. Allied health professionals' use varied by specialty. ConclusionFindings from the study provide crucial insights into the advantages and challenges of BYOD usage in hospitals. These insights can help shape policies and management strategies to support secure and effective BYOD practices in healthcare settings.

From a nurse into a dental assistant

In medicine, a doctor, a specialist, performing any important manipulations regarding the life and health of a person, is faced with the fact that he needs additional hands. Nearby should be the one who is always at the ready - the doctor’s personal assistant. Is the assistant the same physician assistant, or a nurse practitioner? Turning to the etymology of the word «assistant,» it becomes obvious that he really should be there. From the above, it can be concluded that an assistant, namely a dental assistant, is not a nurse, but the same doctor’s assistant who helps the doctor do four-handed work to ensure high-quality and effective dental treatment. However, in the qualification directory of medical professions, such a position as «assistant dentist» is absent. Understanding the functionality of the dental assistant, it turns out that the assistant must know everything so that the doctor can fully focus on the treatment process and not be distracted by minor trifles. At first glance, it may seem that this is the usual business of a nurse, but the range of activities of a dental assistant during the reception of a patient is much wider. It is also worth noting the time savings: the minimum cumulative saving of the dentist’s working time when working with an assistant on the four-handed system at the therapeutic reception of one patient is 11.8 minutes. And do not forget about the importance of the human factor. When choosing a profession as an assistant dentist, you need to understand that it is necessary to act according to the clear instructions of the doctor with whom the assistant works in tandem. In the dentist’s office there are three main characters and each performs its own, individual, important function.

An approach to the mechanics of pantographic structures through swarm dynamics

This work concerns mechanical systems known as “pantographic structures”, which are considered a family of beams connected by pivots. We propose a computationally efficient and predictive model capable of providing a plausible description of deformation, using a discrete two-dimensional (2D) “kinematic” model. This model assigns the displacements of certain points, called leaders, while the other points, called followers, are left free to rearrange themselves according to the lattice structure and assigned flocking rules. The advantages of the kinematic model include savings in calculation time and easier handling of complicated structures and fracture phenomena. Moreover, extending the model to encompass non-local interactions, as exemplified by second-gradient materials, is a more straightforward process than solving partial differential equations. The model has previously been employed to elucidate the behaviour of simple materials. The principal innovation of this paper is the essential delineation of the constraint between the pantograph beams, which is contingent upon the presence of the pivot. This was accomplished by imposing an ”average” displacement on the component points of the pivot belonging to two separate beams, thereby introducing an additional parameter into the model. This also necessitated a significant alteration to the calculation algorithm. The model is still constrained by several limitations, including the absence of a physical interpretation for its parameters and the necessity of connecting it to the CUDA (Compute Unified Device Architecture, by NVIDIA) architecture for the calculation of the developed algorithm. The principal limitation of the present approach, however, is the absence of a correlation between the parameters of the ’kinematic’ model and the constitutive parameters of the material. This aspect will be the subject of future research.

Community Perception on the Effect of Cultural Livelihoods on the Environment in Kogi State, Nigeria

This study examines the cultural livelihood of Kogi State and its effects on the environment. The study describes some of the cultural livelihood practices found in Kogi State, considering the contemporary condition of cultural livelihood and its effects on the environment. Secondary and primary data were employed, which include archives and internet search engines. Using a 4-stage sampling procedure, data were collected from a 120-person sample through an interview, field observation, a focus group discussion, and a questionnaire. Descriptive statistics using frequencies, percentages, and charts were used for the analyses. The results were compiled using the Statistical Package for Social Sciences (SPSS). Findings show that about 85% of the participants discovered crop farming, arable farming, weaving, blacksmithing, fishing, and festivals of harvest, such as the New Yam Festival, among others, as the predominant cultural livelihoods. The local farming implements were made of local materials, like stones and wood. They have indigenous crop production, protection, and harvest techniques. The farming tools were economical in terms of labor, affordability, and time savings in the subsistence farming system. The study discovered that cultural livelihoods are 4% very efficient and 56% on the verge of extinction. Analyses of the effect of cultural livelihood show that 78% have a high negative effect on the economic environment, 57% have a moderate negative effect on the social environment, 51% hurt the political environment, and 22% have a low negative effect on the political environment. The intervention of the various tiers of government with the cooperation of the various communities is needed for the provision of a conducive environment for the practice of cultural livelihood, particularly in the aspect of insecurity. Adequate provision of modern equipment, funding, and social welfare services is also recommended to enhance cultural livelihoods.

An economic evaluation of sensor-assisted health monitoring in dairy farming using the example of a rumen bolus.

The study investigates the economics of sensor-assisted dairy health management and indicates a certain economic potential in the use of a commercial rumen bolus capable of tracking activity and core body temperature. The economic evaluation was performed applying a stochastic model with the net return (NR) of investment of the sensor system as the target variable. The calculated NR considers the gross margin (GM) for both sensor-assisted and visual health monitoring, time savings through sensor-assisted monitoring, additional time spent addressing false positive messages from the sensor system, labor costs, and all costs associated with the investment in the sensor system. The analysis relies on a data set acquired from a dairy research and demonstration farm on which 65 dairy cows were equipped with the sensor system. A comparison of health-related messages issued by the rumen bolus with disease diagnoses shows that the sensor system issued a message in 7 of 11 cases of retained placenta (sensitivity = 64%), in 19 of 31 cases of clinical hypocalcemia (sensitivity = 61%), in 30 of 70 cases of mastitis (sensitivity = 43%), in 6 of 24 cases of metritis (sensitivity = 25%), and in 2 of 42 cases of diseases of the locomotor system (sensitivity = 5%) in a defined observation period, in many cases several days before the visual diagnosis. SimHerd (A/S Viborg, Denmark) was applied to determine the GM as a function of incidence, somatic cell count, risk of a mild case of disease, and days of milk withdrawal. In a workshop, veterinarians (n = 9) used the data set to assess the impact of using the sensor system on these parameters. The empirical distributions given by the veterinarians' individual assessments were used to model the parameters considered in the calculation of the "sensor-assisted" GM. For the modeled Holstein herds with a milk yield of 9,000 kg, simulation results show that average NR of investment ranges from +€23 to +€119 per cow per year for a herd of poor health, from -€12 to +€84 per cow per year for a herd of average health, and from -€33 to +€63 per cow per year for a herd of good health, depending on the scenario. The assumptions made regarding changes in labor had a strong influence on the calculated NR of investment. For a full economic evaluation of the sensor system, other functions (estrus detection, calving detection) and functional extensions (e.g., monitoring rumination) have to be considered.

PROSTHESIS REPAIR OF ORAL IMPLANTS BASED ON ARTIFICIAL INTELLIGENC`E FINITE ELEMENT ANALYSIS

Dentists often suggest dental implants to replace missing teeth; nevertheless, mechanical issues can develop with these implants, which could lead to prosthesis replacement or repairs. When investigating implant systems' mechanical characteristics and stress distribution, finite element analysis (FEA) is a popular computational tool. In biomechanical investigations, this strategy is widely used. However, traditional FEA methods can be tedious and require expert expertise for accurate simulation and translation of results. To automate and simplify the process of mending oral implant prostheses, the article suggests a new framework called AI-FEA. The three primary parts that make up the suggested AI-FEA framework are 1. An AI-powered model creation module that utilizes data from medical imaging to autonomously construct 3D finite element designs that are unique to each patient. Utilizing deep learning approaches, this module segments and reconstructs three-dimensional geometries from computed tomography (CT) or cone-beam CT data using material properties and boundary conditions. 2. A FEA solver that runs simulations to test the way the implant system handles different loads. This component uses state-of-the-art numerical methods to model the implant and bone interface and determine stress distributions. 3. An AI-based decision support system that takes all that data and recommends the best way to fix the prosthesis. Combining FEA findings with patient-specific variables, this decision support system uses machine learning algorithms educated on an extensive dataset of implant failure instances and repair results to provide the optimal repair strategy. For patients experiencing issues with oral implants, the suggested AI-FEA framework might mean huge time and skill savings in prosthesis repair planning, leading to better, more individualized care.

Multidisciplinary quantitative and qualitative assessment of IDH-mutant gliomas with full diagnostic deep learning image reconstruction

Rationale and Objectives: Diagnostic accuracy and therapeutic decision-making for IDH-mutant gliomas in tumor board reviews are based on MRI and multidisciplinary interactions. Materialsand Methods: This study explores the feasibility of deep learning-based reconstruction (DLR) in MRI for IDH-mutant gliomas. The research utilizes a multidisciplinary approach, engaging neuroradiologists, neurosurgeons, neuro-oncologists, and radiotherapists to evaluate qualitative aspects of DLR and conventional reconstructed (CR) sequences. Furthermore, quantitative image quality and tumor volumes according to Response Assessment in Neuro-Oncology (RANO) 2.0standards were assessed. ResultsAll DLR sequences consistently outperformed CR sequences (median of 4 for all) in qualitative image quality across all raters (p < 0.001 for all) and revealed higher SNR and CNR values (p < 0.001 for all). Preference for all DLR over CR was overwhelming, with ratings of 84% from the neuroradiologist, 100% from the neurosurgeon, 92% from the neuro-oncologist, and 84% from the radiation oncologist. The RANO 2.0 compliant measurements showed no significant difference between the CR and DRL sequences (p = 0.142). ConclusionThis study demonstrates the clinical feasibility of DLR in MR imaging of IDH-mutant gliomas, with significant time savings of 29.6% on average and non-inferior image quality to CR. DLR sequences received strong multidisciplinary preference, underscoring their potential for enhancing neuro-oncological decision-making and suitability for clinical implementation.

Comparison of hearing-aid effectiveness based on user-operated versus traditional audiometry: a randomised clinical trial

Objective To investigate whether hearing-aid fitting based on user-operated audiometry is non-inferior to hearing-aid fitting based on traditional audiometry. Design This non-inferiority randomised clinical trial, took place at Odense University Hospital, Odense, Denmark. In a first visit, participants were tested with traditional audiometry as well as user-operated audiometry and filled in the Speech, Spatial and Qualities of Hearing Scale (SSQ12) at baseline. Next, they were randomly divided to receive hearing aids fitted based on either the user-operated (UAud group) or the traditional audiometry (control group) hearing thresholds. After three months of hearing-aid use, participants underwent an aided hearing-in-noise test and completed the SSQ12 again, as well as an additional battery of outcome measures. Study sample A total of 215 adults with sensorineural hearing impairment referred for bilateral hearing-aid fitting. Results The before-after change in SSQ12 total scores was not different between the groups as the interaction between test times and groups was −0.06 (95% CI −0.56 to 0.44, p = 0.811). Conclusions The findings suggest that the HA effectiveness in the UAud group was non-inferior to those observed in the control group. This implies that integrating user-operated audiometry into the clinical system is feasible, offering potential personnel time savings without compromising patient outcomes. Trial Registration The trial was registered at ClinicalTrials.gov (NCT05043207).

Evaluation of the advanced air mobility potential for organ transplantation in Austria and Germany

Solid organ transplantation continues to be the only or most efficient therapeutic solution for several end-stage diseases. The success of such transplantation is largely dependent on the swift transportation of organs from donors to recipients, as Cold Ischemia Time (CIT) plays a critical role in determining the recipient’s medical outcome. This study explores the potential of Advanced Air Mobility (AAM) in the context of organ transplantation in Austria and Germany. AAM, in the healthcare sector, is associated with potential overall process time savings via air transportation, thereby reducing CIT. However, the application of AAM for organ transplantation has not yet been implemented in Europe. This study employs a Monte Carlo simulation to derive the trip length distributions for organ transplantation in Austria and Germany. By utilizing data from Eurotransplant (2018–2021) and ÖBIG (2017–2021), it was found that 48% of organ transports within Germany, and 80% of organ transports within Austria, fall within a trip length of less than 150 km. This distance is within the capabilities of today’s AAM technology. Anticipated time benefits of up to 30 min compared to ground-based transport can be expected. Furthermore, the optimization of the organ transport process, facilitated by AAM, promises greater potential for CIT reduction.

Informing Modern Models of Care: A Randomized, Sequential Trial of In-Person, Telehealth, and Telephone Appointments for Patients with Inflammatory Bowel Disease.

A significant shift toward virtual care has occurred for many patients with inflammatory bowel disease (IBD). To date, there are no prospective studies assessing patients visits across different styles of appointments. We randomized IBD patients' appointments to in-person, video-assisted virtual, or telephone and subsequent appointments to the alternate style of visit in a single-center study in Victoria, Canada. Participants completed surveys after each appointment. Demographic data were collected and average scores were analyzed for potential associations. Forty-one patients were randomized to appointments, and 86 post-appointment surveys were completed, 30 in-person, 29 telephone, and 27 telehealth. The average age was 46.5 ± 18years and 59% were female. The overall patient score (out of ten) by appointment type was 9.1 ± 1.0 for in-person, 7.8 ± 2.1 for telephone, and 8.0 ± 2.6 for telehealth without a statistically significant difference. While there was only a near statistically significant preference for in-person appointments compared with telehealth p = 0.055, it was statistically significant when comparing with phone appointments p = 0.014. The highest rated factors for preference of an in-person appointment were optimal communication and interaction with care provider (86%). For patients who preferred telehealth or telephone appointments, the highest rated factors were time and cost savings (71%, 43% and 58%, 33%, respectively). In-person appointments were associated with a significantly higher cost (p < 0.01), and longer time commitment. Despite the increased cost and time commitment, in-person appointments were highly rated particularly in comparison to phone appointments. Based on this research, providers can be better informed on factors contributing to patients' preferred appointment style.

Mastering seismic time series response predictions using an attention-Mamba transformer model for bridge bearings and piers across varied testing conditions

This research introduces an advanced method for predicting seismic responses and hysteresis curves of instrumented bridge piers and bearings under various loading conditions, leaning solely on a single deep learning architecture and the same hyperparameters tuning. Test specimens are subjected to ground accelerations including vertical seismic loads and axial forces. To accurately capture peak values, particularly on the negative side of the hysteresis loop (unloading region), the model employs a stacked deep architecture. A key component to overcome the challenges is the self-attention-Mamba-driven transformer layer, which enhances the model’s ability to capture long-range dependencies in seismic data. This layer works in conjunction with other deep learning techniques to ensure robust and precise predictions. Implemented with Python’s Keras functional API, the model processes inputs like ground accelerations, actuator loads, effective height, moment of inertia, and superstructure mass. The model is evaluated with a dataset of 95 real-time hybrid simulation (RTHS) tests for lead rubber bearings, 29 RTHS tests for bridge piers, and 17 cyclic tests (10 fast and 7 slow). Extensive hyperparameter tuning demonstrates the model’s proficiency to capture hysteresis and residual deformations accurately. Achieving an impressive correlation with experimentally measured values, ranging from 88.1 to 98.9%, and a reasonable dissipated energy error ratio are notable. The deep learning model reduces the need for additional tests, offering time and cost savings, and provides rapid, and accurate insights into bridge behavior. This supports timely and precise bridge design and aids decision-makers during emergencies.

Hierarchical Nearest Neighbor Gaussian Process models for discrete choice: Mode choice in New York City

Standard Discrete Choice Models (DCMs) assume that unobserved effects that influence decision-making are independently and identically distributed among individuals. When unobserved effects are spatially correlated, the independence assumption does not hold, leading to biased standard errors and potentially biased parameter estimates. This paper proposes an interpretable Hierarchical Nearest Neighbor Gaussian Process (HNNGP) model to account for spatially correlated unobservables in discrete choice analysis. Gaussian Processes (GPs) are often regarded as lacking interpretability due to their non-parametric nature. However, we demonstrate how to incorporate GPs directly into the latent utility specification to flexibly model spatially correlated unobserved effects without sacrificing structural economic interpretation. To empirically test our proposed HNNGP models, we analyze binary and multinomial mode choices for commuting to work in New York City. For the multinomial case, we formulate and estimate HNNGPs with and without independence from irrelevant alternatives (IIA). Building on the interpretability of our modeling strategy, we provide both point estimates and credible intervals for the value of travel time savings in NYC. Finally, we compare the results from all proposed specifications with those derived from a standard logit model and a probit model with spatially autocorrelated errors (SAE) to showcase how accounting for different sources of spatial correlation in discrete choice can significantly impact inference. We also show that the HNNGP models attain better out-of-sample prediction performance when compared to the logit and probit SAE models, especially in the multinomial case.

Estimation of Influence of Target Material Type on Penetration Capability of 12.7 mm Armor-Piercing Projectile

In the study an estimation of the influence of target material type on the penetration capability of the 12.7 mm API-T (armor-piercing incendiary with tracer) projectile is performed. The literature review contains 14 references regarding 12.7 mm ammunition studies. A relatively large number of metallic targets are taken into consideration in a computer simulation that assesses the influence of the target type on the penetration capabilities of the given projectile. By comparing the acquired findings with the data of other authors, the numerical model was first successfully validated. Simulation results indicate that bainitic steels are by far the most resilient steels. Because of their hardness, these steels displayed significant erosion of the penetrator tip and significant projectile fragmentation upon impact. Iron targets, as expected, performed worse than steel targets, with more penetration seen. Mild steel had a significantly deeper penetration compared to armor steel. The performance of other steels employed in the study was comparable. Numerical simulations have shown to be a very effective technique in ammunition and armor design, resulting in substantial time and cost savings (fewer experimental experiments). Furthermore, by utilizing simulations, researchers can estimate certain physical processes that would be challenging to detect in actual testing.