Lightweight structures tend to be susceptible to vibration, which can lead to fatigue and failure. Piezoelectric shunt damping is an efficient technology that converts mechanical energy into electrical energy and dissipates it through shunt circuits without requiring external power. This study models several piezoelectric patches coupled to different shunt circuits on a thin plate, utilizing Rayleigh-Ritz modal analysis to examine patch parameters including size, position, and distribution. Optimization demonstrates that effective patch-circuit topologies correspond with structural mode shapes, resulting in maximum vibration reduction. The findings introduce a fresh technique to enhance vibration control which improves structural reliability and performance.

This article presents a novel algorithm based on the cluster first-route second method, which executes a solution through K-means and Optics clustering techniques and Nearest Neighbor and Local Search 2-opt heuristics, for the solution of a vehicle routing problem with time windows (VRPTW). The objective of the problem focuses on reducing distances, supported by the variables of demand, delivery points, capacities, time windows and type of fleet in synergy with the model's taxonomy, based on data referring to deliveries made by a logistics operator in Colombia. As a result, good solutions are generated in minimum time periods after fulfilling the agreed constraints, providing high performance in route generation and solutions for large customer instances. Similarly, the algorithm demonstrates efficiency and competitiveness compared to other methods detailed in the literature, after being benchmarked with the Solomon instance data set, exporting even better results.

This study reports on microstructure evolution and mechanical properties of weld joints of two ASTM A335 P92 steels with varying chromium and tungsten content influenced by heat treatment. Physical simulation samples for fine-grain and intercritical heat-affected zones were done using the Gleeble® 3500 equipment. A peak temperature of 900°C (intercritical zone) and 950°C (fine-grained zone) simulated different heat-affected zones. After physical simulation, the test samples underwent two heat treatment schedules: post-weld heat treatment (PWHT) and normalisation, followed by tempering. The results show that the two steels had similar martensite microstructure. The microstructure further exhibited the presence of M23C6 carbides along the grain and lath boundaries. The P92-B steel had the highest hardness values after heat treatment except at FGHAZ + PWHT condition, which had a lower hardness value (271.9 ± 5.0 HV0.5). In this condition (FGHAZ + PWHT), P92-B steel had a higher Charpy toughness value (180J), slightly higher than the base metal (178J) due to fully formed martensite microstructure. ICHAZ + heat treatment, P92-B steel had the lowest toughness values (74J for r-PWHT and 83J for PWHT), but these values were higher than the minimum toughness value of 47J of the weld joint required for hydro-testing of the vessels. The study revealed no marked significant differences between the two steels. The heat treatment method (r-PWHT) is applicable in the industry for Type IV crack mitigation of the weld joint.

Hybrid flowshop scheduling problem (HFSP) is a mixture of two classical scheduling problems as parallel machine scheduling (PMS) and flowshop scheduling (FS). In the HFSP, a series of jobs are processed respectively in a set of stages that at least one of these stages has more than one parallel machine (identical, uniform or unrelated). HFSP is a widespreadly studied subject in the literature and there are various application areas such as transportation, healthcare management, agricultural activities, cloud computing, and the most common manufacturing. Therefore, it will be useful to present a review study including recent papers and developments related to this problem for researchers. For this aim, in this paper, a systematic literature survey has been conducted with respect to HFSPs by means of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology which enables to realize systematic review and meta-analyses in a specified subject. 172 articles which are published in the 2010-2020 year interval, related to production scheduling and including a mathematical programming model to express scheduling problems have been determined as a result of this methodological review process. These articles have been statistically analyzed according to many features such as year, country, journal, number of stages, type of parallel machines, constraints, objective functions, solution methods, test instances and type of parameters. The results of statistical analyses have been presented through charts so as to provide a visual demonstration to researchers. Furthermore, it has been aimed to answer 14 predetermined research questions by means of analyses realized in the scope of this review study. Consequently, it has revealed the existing literature, recent developments and future research suggestions related to HFSP and therefore it is possible to say that this review paper provides a beneficial road map for researchers studying in this field.

Reinforced concrete deep beams (RCDBs) investigations are often complex because of the highly disturbed zones which may aggravate the shear performance under variable loadings. The shear capacity enhancement of RCDB using different aggregate sizes of 19 mm, 25 mm, and 50 mm has been investigated under three-point monotonic loading. Nine RCDBs with 750 × 170 × 225 mm dimensions were considered, and the beam was loaded at a 1.4 shear span to depth ratio. Three of the beams were designed without web reinforcement, and six were designed for web reinforcement with varied aggregate sizes. There was no significant difference in the shear strength of RCDBs considered however, a 50 mm aggregate beam was found capable of reducing the multiple crack propagations when compared to other aggregate-size beams. Additionally, the shear reinforcement increased the ductility and strength by over 30% and 20%, respectively. The applicability of 3-dimensional FEM extended to the investigation acceded with the shear response of the experiment exercising shear stiffening behavior. The estimated model from the modified ACI 318:05 can predict the shear capacity of the RCDB with higher accuracy. Since aggregate resists the load by aggregate interlock action, it is crucial to choose the right aggregate when building concrete structural components. The results of this study will help engineers choose the best aggregate for a certain structural element.

This study evaluates the impact of project procurement strategies on supply chain efficiency in the Jordanian construction industry, with vendor relationship management as a mediating variable. The target population was the 25000 construction industry specialists in Jordan. According to purposeful sampling technique and more specifically, stratified random sampling, 454 subjects were asked to complete questionnaires. Partial least squares structural equation modeling was employed for the analysis of data for the test of the hypotheses. This research also revealed that the project procurement strategies which are Just-In-Time, Multiple Sourcing, and Single Sourcing have positive impacts with a statistical significance to the supply chain efficiency dimensions of Demand Forecasting, Supply Chain Integration and Collaboration and Communication. Vendor relationship management was also a partial mediator to these relations. These implications can be summed up for the construction firms and policymakers in Jordan to understand how strategic procurement approach and proper relations with the vendors can improve the performance of the supply chain. This work aims to benefit the academia as it fills a gap in the existing literature by analysing the relationship between procurement strategies, its actors (vendors), and supply chain performance within a developing country environment. The analysis with the help of PLS-SEM enables us to look at both the direct and the indirect effects in such relationships.

This research pioneers the development of an innovative approach for refining dental implant fixture surfaces using the SLA+Anodizing method. Leveraging a rich dataset encompassing 68 distinct implant surface treatment states, the study employs an Artificial Neural Network (ANN) to predict crucial parameters such as surface roughness and cellular viability. Through meticulous training and validation, the ANN demonstrates a remarkable 3% error rate in comparison to experimental results, underscoring its precision. The methodology extends beyond prediction, facilitating the optimization of implant surfaces for enhanced osseointegration. Experimental validation, including Atomic Force Microscopy and Molecular Cytotoxicity Tests, corroborates the accuracy of the ANN predictions. The study pioneers a transformative era in dental implantology, introducing a tailored and adaptable approach that bridges gaps in understanding the intricate interplay between surface modifications and biological responses. This work sets the stage for a paradigm shift in dental science, emphasizing precision, personalization, and elevated standards of care.

This paper aims at presenting a customer order scheduling environment in which the setup times are explicit and depend on the production sequence. The considered objective function is the total tardiness minimization. Since the variant under study is NP-hard, we propose a mixed-integer linear programming (MILP) model, an adaptation of the Order-Scheduling Modified Due-Date heuristic (OMDD) (referred to as Order-Scheduling Modified Due-Date Setup (OMMD-S)), an adaptation of the Framinan and Perez-Gonzalez heuristic (FP) (hereinafter referred to as Framinan and Perez-Gonzalez Setup (FP-S)), a matheuristic with Same Permutation in All Machines (SPAM), and the hybrid matheuristic SPAM-SJPO based on Job-Position Oscillation (JPO). The algorithms under comparison have been compared on an extensive benchmark of randomly generated test instances, considering two performance measures: Relative Deviation Index (RDI) and Success Rate (SR). For the small-size evaluated instances, the SPAM is the most efficient algorithm, presenting the better values of RDI and SR. For the large-size evaluated instances, the hybrid matheuristic SPAM-JPO and MILP model are the most efficient methods.

The export development of small and medium enterprises (SMEs) in Indonesia is still very low, resulting in their contribution to national exports being very small as well. Government bodies and relevant stakeholders are actively pursuing initiatives to enhance the export performance of SMEs. These efforts include improving the quality of human and social capital and promoting the integration of digital technology into SME operations. This examination evaluates the impact of human capital (HC), social capital (SC), and the utilization of digital technology on the export performance of SMEs. The investigation adopted a survey approach on all export-oriented SMEs listed on the Bank Indonesia website. Data was obtained through the distribution of questionnaires to 614 SMEs. Data analysis was conducted using PLS SEM. The research findings indicate that human capital, social capital, and digital technology have a positive and significant influence on the export performance of SMEs in Indonesia.

In the current decade, a number of industries have moved their attention towards emerging sustainable technologies in order to better support socio-economic and environmental considerations. The present research investigates a unique hybrid composite developed by the amalgamation of natural kenaf-coir fibers, with resin of epoxy, incorporated with titanium carbide (TiC) nanoparticles. This study also presents the development process involved in manufacturing the composites, along with mechanical testing and optimization of these composite samples. The nanofillers of TiC are utilized in wt. percentages of 0%, 3%, 4%, and 5%, while coir and kenaf fibers are incorporated at 0%, 3%, 4%, and 5% by weight, and the thickness of the samples is varied at 2, 3, 4, and 5mm. The mechanical attributes of composites are evaluated using a vacuum bag molding process, with subsequent testing and optimization performed through Taguchi and ANOVA analysis to discover the optimal sample combination. The findings indicate that the most effective composite formulation includes 4% TiC, 5% kenaf, 5% coir, and a thickness of 3 mm, which provides the highest tensile modulus and strength among all tested samples. The integration of kenaf fibers with coir fibers and TiCs as fillers significantly improves the tensile and flexural attributes of the hybrid composite in contrast to composites made with coir or kenaf fibers alone.