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The purpose of this study was to test a mobile application for mental health created for Filipinos. Specifically, it tested the Lusog-Isip mobile app vis-à-vis a mental health workbook as they affect psychosocial well-being and coping strategies of users.

This study used a randomized control trial design. Participants were assigned to two conditions: a treatment group using the mobile app and a control group that used an existing mental health workbook. Pre- and post-tests were conducted before and two weeks after.

Results revealed improved psychological well-being and use of cognitive reappraisal for both mobile app and workbook users. Users of the mobile app reported higher scores compared to those who used the workbook for emotional release.

This study adds to the dearth of knowledge on the use of a mobile application for mental health in low-resource countries. It highlights the potential of using digital technologies to provide access to mental health resources in underserved populations.

Previous research found that when exercise partners provided social support to each other, both participants engaged in higher levels of activity (Gellert et al., 2011). These results suggest that there may be benefits to providing inclusive physical activity (PA) programming to individuals with autism; however, little research has explored specific strategies. The purpose of this study is to explore the use of a behavioral intervention consisting of visual social stories and additional intervention components (i.e. prompting, checklists) to teach adolescents/young adults with autism and their workout partners without disabilities to provide social support to one another during partnered PA.

A multiple probe across dyads was used to explore the effects of the intervention on social support (i.e. verbal encouragement and feedback).

The results indicate the treatment was effective during the intervention phase. When partners and settings changed during generalization, results were maintained in all but one participant.

Areas for future research and implications for practice to support inclusive PA for autistics are discussed.

This study aims to delve into the intricate world of small retailers in India, seeking to understand the impediments/barriers they encounter when trying to embrace online-to-offline (O2O) platforms. It also investigates the potential impact of the digital ecosystem in moderating these barriers.

Data from 426 Indian retailers was collected, and structural equation modelling was used to validate the conceptual framework.

The findings highlight the importance of addressing distrust and technological anxiety as key barriers to O2O platform adoption. Psychological risk, low-tech orientation, privacy risk, financial risk and social risk were also identified as barriers. Interestingly, performance risk and infrastructure were found to be insignificant in this study. The study indicates that the digital ecosystem does moderate the relationship between psychological risk, performance risk, distrust and technological anxiety with attitude.

This research holds significant implications for technology adoption, retail management and aggregator platform development in developing nations, notably India. This research draws upon a conceptual framework to deepen the understanding of the O2O technology platform by providing an all-inclusive overview.

This study breaks new ground by investigating the distinctive obstacles to O2O adoption faced by small retailers in India. By validating the digital ecosystem’s moderating effect, this research yields insights that are context-specific and particularly relevant to the Indian retail landscape. Valuable guidance is offered for researchers, practitioners and policymakers navigating O2O strategy implementation in emerging markets.

This study aims to explore the effects of cat-assisted therapy (Ca-AT) on a patient in their homes, specifically investigating the effects on patient’s memory, behavioral pathology and ability to perform activities of daily living, independently.

A case study design was used in patient’s homes using three measuring scales, namely, Mini-Mental State Examination (MMSE), Barthel index (BI) and Behavioral Pathology in Alzheimer’s Disease (AD) Rating Scale.

The MMSE and BI mean scores were increased, whereas the Behavioral Pathology mean score was decreased. Patient negative behaviors were improved specifically, aggressiveness, anxieties, phobias, and caregiver burden was decreased.

Patients with AD could significantly benefit from Ca-AT in their own homes, and it could decrease caregiving burden.

Ca-AT is a newly developed type of animal-assisted therapy that uses cats to treat patients, especially elderly people with AD, in their homes.

Nowadays, in building structures, dampers are connected to the building structure to reduce the damages caused by seismicity in addition to enhancing structural stability, and to connect dampers with the structure, joints are used. In this paper, three different configurations of double-lap joints were designed, developed and tested.

This paper aims to analyze three different categories of double-lap single-bolted joints that are used in connecting dampers with concrete and steel frame structures. These joints were designed and tested using computational, numerical and experimental methods. The studies were conducted to examine the reactions of the joints during loading conditions and to select the best joints for the structures that allow easy maintenance of the dampers and also withstand structural deformation when the damper is active during seismicity. Also, a computational analysis was performed on the designed joints integrated with the M25 concrete beam column junction. In this investigation, experimental study was carried out in addition to numerical and computational methods during cyclic load.

It was observed from the result that during deformation the double-base multiplate lap joint was suitable for buildings because the deformations on the joint base was negligible when compared with other joints. From the computational analysis, it was revealed that the three double joints while integrated with the beam column junction of M25 grade concrete structure, the damages induced by the double-base multiplate joint was negligible when compared with other two joints used in this study.

To prevent the collapse of the building during seismicity, dampers are used and further connecting the damper with the building structures, joints are used. In this paper, three double-lap joints in different design configuration were studied using computational, numerical and experimental techniques.

The purpose of this research is to establish a robust numerical framework for the calibration of macroscopic constitutive parameters, based on the analysis of polycrystalline RVEs with computational homogenisation.

This framework is composed of four building-blocks: (1) the multi-scale model, consisting of polycrystalline RVEs, where the grains are modelled with anisotropic crystal plasticity, and computational homogenisation to link the scales, (2) a set of loading cases to generate the reference responses, (3) the von Mises elasto-plastic model to be calibrated, and (4) the optimisation algorithms to solve the inverse identification problem. Several optimisation algorithms are assessed through a reference identification problem. Thereafter, different calibration strategies are tested. The accuracy of the calibrated models is evaluated by comparing their results against an FE2 model and experimental data.

In the initial tests, the LIPO optimiser performs the best. Good results accuracy is obtained with the calibrated constitutive models. The computing time needed by the FE2 simulations is 5 orders of magnitude larger, compared to the standard macroscopic simulations, demonstrating how this framework is suitable to obtain efficient micro-mechanics-informed constitutive models.

This contribution proposes a numerical framework, based on FE2 and macro-scale single element simulations, where the calibration of constitutive laws is informed by multi-scale analysis. The most efficient combination of optimisation algorithm and definition of the objective function is studied, and the robustness of the proposed approach is demonstrated by validation with both numerical and experimental data.

In this research, the free vibration sensitivity analysis of cracked fiber metal laminated (FML) beams is investigated numerically and experimentally. The effects of single and double cracks on the frequency of the cantilever beams are simulated using the finite element method (FEM) and compared to the experimental results.

In FEM analysis, the crack defect is simulated by the contour integral technique without considering the crack growth. The specimens are fabricated with an aluminum sheet, woven carbon fiber and epoxy resin. The FML specimens are constructed by bonding five layers as [carbon fiber-epoxy/Al/carbon fiber-epoxy/Al/carbon fiber-epoxy]. First, the location and length of cracks are considered input factors for the frequency sensitivity analysis. Then, the design of the experiment is produced in the cases of single and double cracks to compute the frequency of the beams in the first and second modes using the FEM. The mechanical shaker is used to determine the natural frequency of the specimens. In addition, the predicted response values of the frequency for the beam are used to compare with the experimental results.

Consequently, the results of the sensitivity analysis demonstrate that the location and length of the crack have significant effects on the modes.

Effective interaction diagrams are introduced to investigate crack detection for input factors, including the location and length of cracks in the cases of single and double cracks.

This study aims to develop a novel thermal modeling strategy to simulate electron beam powder bed fusion at part scale with machine-varying process parameters strategy. Single-bead and part-scale experiments and modeling were studied. Scanning strategies were described by the process controlling functions that enabled modeling.

The finite element analysis thermal model was used along with the powder bed fusion with electron beam experiments. The proposed strategy involves dividing a part into smaller sections and creating meso-scale models for each subsection. These meso-scale models take into consideration the variable process parameters, including power and velocity of the moving heat source, during part building. Subsequently, these models are integrated to perform partscale simulations, enabling more realistic predictions of thermal accumulation and resulting distortions. The model was built and validated with single-bead experiments and bulky parts with different features.

Single-bead experiments demonstrated an average error rate of 6%–24% for melt pool dimension prediction using the proposed meso-scale models with different scanning control functions. Part-scale simulations for three different geometries (cantilever beams with supports, bulk artifact and topology-optimized transfer arm) showed good agreement between modeled temperature changes and experimental deformation values.

This study presents a novel approach for electron beam powder bed fusion modeling that leverages meso-scale models to capture the influence of variable process parameters on part quality. This strategy offers improved accuracy for predicting part geometry and identifying potential defects, leading to a more efficient additive manufacturing process.

This study aims to reveal the influence of milling process parameters on the surface roughness and morphology of superalloy GH4145.The groove milling mechanism and surface quality influence factors of superalloy GH4145 were studied experimentally.

This paper provides investigations on three-dimensional finite element model (FEM) and simulation of milling process for GH4145.The milling experiment uses Taguchi L16 experimental design and single factor experimental design. The surface morphology of the workpiece was observed by scanning electron microscopy, and the influence mechanism of milling parameters on surface quality is expounded.

The results show that the cutting force increases by 133% with the increase in milling depth. The measured minimum surface roughness is 0.035 µm. With the change in milling depth, the surface roughness increases by 249%. With the change in cutting speed, the surface roughness increased by 54.8%. As the feed rate increases, the surface roughness increases by a maximum of 91.1%. The milling experiment verifies that the error between the predicted surface roughness and the actual value is less than 8%.

The milling experiment uses a Taguchi L16 experimental design and a single-factor experimental design. Mathematical models can be used in research as a contribution to current research. In addition, the milling cutter can be changed to further test this experiment. Reveal the influence of milling process parameters on the surface roughness and morphology of superalloy GH4145.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2024-0080/

The aim of this paper is to review the literature on the prediction of cutting tool life. Tool life is typically estimated by predicting the time to reach the threshold flank wear width. The cutting tool is a crucial component in any machining process, and its failure affects the manufacturing process adversely. The prediction of cutting tool life by considering several factors that affect tool life is crucial to managing quality, cost, availability and waste in machining processes.

This study has undertaken the critical analysis and summarisation of various techniques used in the literature for predicting the life or remaining useful life (RUL) of the cutting tool through monitoring the tool wear, primarily flank wear. The experimental setups that comprise diversified machining processes, including turning, milling, drilling, boring and slotting, are covered in this review.

Cutting tool life is a stochastic variable. Tool failure depends on various factors, including the type and material of the cutting tool, work material, cutting conditions and machine tool. Thus, the life of the cutting tool for a particular experimental setup must be modelled by considering the cutting parameters.

This submission discusses tool life prediction comprehensively, from monitoring tool wear, primarily flank wear, to modelling tool life, and this type of comprehensive review on cutting tool life prediction has not been reported in the literature till now. The future suggestions provided in this review are expected to provide avenues to solve the unexplored challenges in this field.