Search Results (236)

Reducing setup costs and improving product quality are critical objectives in a sustainable production processes. The significance of these goals lies in their direct impact on efficiency. It affects competitiveness and customer satisfaction. Businesses can reduce setup costs to maximize resource usage. It can reduce downtime between production runs and improve overall operational agility. Sustained performance and expansion in contemporary manufacturing environments focus on setup cost reduction and product quality improvement. The present paper discusses a production inventory model for the product, which produces by-products as secondary products from the same manufacturing process. Setup cost is reduced for the setup of production and refining processes. A production process may change from being under control to an uncontrolled one. As a result of this, imperfect products are formed. This paper considers product quality improvement for both produced and processed items. The outcome shows that dealing with by-products helps make the system more profitable. Sensitivity analysis is performed for various costs and parameters. Mathematica 11 software was used for calculation and graphical work. Full article

Housing prices have recently risen sharply in many countries, primarily linked to the global credit cycle. Although various factors play a role, the ability of developing countries to navigate this cycle and maintain autonomous monetary policies is crucial. This paper introduces a dynamic macroeconomic model featuring a housing production sector within an imperfect banking framework. It captures key housing and economic dynamics in advanced and emerging economies. The analysis shows domestic liquidity policies, such as bank capital requirements, reserve ratios, and currency devaluation, can stabilize investment and production. However, their effectiveness depends on foreign interest rates and liquidity. Stabilizing housing prices and risk-free bonds is more effective in high-interest environments, while foreign liquidity shocks have asymmetric impacts. They can boost or lower the effectiveness of domestic policy, depending on the country’s level of financial development. These findings have several policy implications. For example, foreign capital controls would be adequate in the short term but not in the long term. Instead, governments would try to promote the development of local financial markets. Controlling debt should be a target for macroprudential policy as well as promoting saving instruments other than real estate, especially during low interest rates. Full article

The rapid growth of ultra-dense mobile edge computing (UDEC) in 5G IoT networks has intensified energy inefficiencies and latency bottlenecks exacerbated by dynamic channel conditions and imperfect CSI in real-world deployments. This paper introduces POTMEC, a power optimization framework that combines a channel-aware adaptive power allocator using real-time SNR measurements, a MATLAB-trained RL model for joint offloading decisions and a decaying step-size algorithm guaranteeing convergence. Computational offloading is a productive technique to overcome mobile battery life issues by processing a few parts of the mobile application on the cloud. It investigated how multi-access edge computing can reduce latency and energy usage. The experiments demonstrate that the proposed model reduces transmission energy consumption by 27.5% compared to baseline methods while maintaining the latency below 15 ms in ultra-dense scenarios. The simulation results confirm a 92% accuracy in near-optimal offloading decisions under dynamic channel conditions. This work advances sustainable edge computing by enabling energy-efficient IoT deployments in 5G ultra-dense networks without compromising QoS. Full article

Manufacturing industries are increasingly focused on enhancing energy efficiency while maintaining high levels of production throughput and product quality. However, most existing energy-saving control (EC) methods overlook the influence of production quality on overall energy performance. To address this challenge, this paper proposes a dynamic EC method for multistage manufacturing systems with product quality scrap. The method utilizes a Markov decision process (MDP) framework to dynamically control the operational states of all machines based on real-time system conditions. Specifically, for two-stage manufacturing systems, the dynamic EC problem is formulated as an MDP, and the optimal EC policy is obtained by a dynamic programming algorithm. For multistage manufacturing systems, to address the curse of dimensionality, an aggregation procedure is proposed to approximate the optimal EC policy for each machine based on the results of two-stage manufacturing systems. Finally, numerical experiments are performed to demonstrate the effectiveness of the proposed dynamic EC method. For a five-stage manufacturing system, the proposed dynamic EC policy achieves a 13.55% reduction in energy consumption costs and a 3.02% improvement in system throughput compared to the baseline. Extensive case studies demonstrate that the dynamic EC policy consistently outperforms three well-studied methods: the station-level EC policy, the upstream-buffer EC policy, and the energy saving opportunity window policy. Moreover, the results confirm the effectiveness of the proposed method in capturing the influence of product quality scrap on the system energy efficiency. This study presents a sensor-integrated methodology for EC, contributing to the advancement of smart manufacturing practices in alignment with Industry 4.0 initiatives. Full article

Merchant vessels often feature numerous perforations in their web frames. To enhance the buckling resistance of these perforated panels, it is customary to install local reinforcements around the openings. This research introduces a novel approach that segments perforated panels into separated unstiffened panels (SUPs) and applies recently updated classification rules for buckling strength assessment, supplemented by inelastic FEA. This research aims to show a case study on how to reduce shipbuilding expenses by conducting a quantitative analysis of the buckling strength of such panels. The study treated perforated panels as separated unstiffened panels (SUPs) in accordance with Common Structural Rules (CSR). The authors examined web frames from various types of carriers, including those for liquefied petroleum gas, containers, products, and crude oil. They gathered data on dimensions, materials, and applied loads for 96 SUPs in total. To assess the buckling strength of these SUPs, IACS rules, eigenvalue finite element analysis (FEA), and inelastic FEA were employed. We performed element size convergence analyses on a square unstiffened panel with simple support on all four edges, ultimately deciding on a 10 mm element size for both eigenvalue and inelastic FEAs. Additionally, inelastic FEAs were performed on the rectangular, unstiffened panels with various aspect ratios, and it was decided to use the average level of initial imperfection for the inelastic FEAs. The SUPs under investigation were classified into Method A and Method B based on CSR recommendations. The ultimate buckling strengths of the categorized SUPs were evaluated by CSR and inelastic FEA. CSR rules provided more conservative ultimate buckling strengths for SUPs corresponding to Method A, while inelastic FEA did for SUPs that were classified into Method B. On the other hand, the inelastic FEAs and CSR rules provided similar ultimate buckling strengths for SUPs requiring Method B. The eigenvalue FEA confirmed that Method B can be an alternative method to inelastic FEA and CSR rules. Significant cost savings were demonstrated by selectively applying CSR and inelastic FEAs for SUPs requiring Method A. The originality of this work lies in its application of the latest classification rule logic, detailed finite element validation using real ship data, and a cost-benefit analysis of reinforcement strategies. Full article

We present an alternative process for production of binary Nb_1−βSn_β superconducting phases using pre- and post-treatment of arc-melted Nb + Sn ingots. This process combines sequential sintering, arc melting, and annealing procedures that provide dense, bulk samples of Nb_1−βSn_β with varying stoichiometry between 0.18 < β < 0.25 depending on annealing time and temperature. We show, through magnetization measurements of these Nb_1−βSn_β bulks, that annealing of arc-melted samples at 900 °C for 3 h significantly enhances J_c values compared with arc-melted Nb_1−βSn_β samples without annealing. Microstructural analyses show that optimum grain size and orientation are achieved by sintering and annealing at lower temperatures (i.e., 720 °C and 900 °C, respectively) with short annealing times (i.e., <10 h). Processing at higher temperatures and for longer times enhances grain growth and results in fewer pinning centres. The optimum process creates effective pinning centres that deliver a J_c = 6.16 × 10⁴ A/cm² at 10 K (and ~0.2 T), compared with J_c = 3.4 × 10⁴ A/cm² for Nb_1−βSn_β subjected to a longer annealing time at a higher temperature and J_c = 775 A/cm² for an arc-melted sample without post-annealing. We suggest that further work addressing post-treatment annealing times between 3 h < t_post < 60 h at temperatures between 900 °C and 1000 °C will provide the opportunity to control stoichiometric and microstructural imperfections in bulk Nb_1−βSn_β materials. Full article

This study investigates the use of the bisection algorithm in inventory models to obtain an approximated optimal solution for the economic manufacturing quantity (EMQ) problem under imperfect production conditions. The objectives are threefold. First, we utilize refined estimations of exponential functions to provide tighter lower and upper bounds for the bisection algorithm. Second, we propose three analytical improvements that simplify the solution process, each supported by rigorous proofs. Third, we incorporate recent results from the literature to further enhance the accuracy of exponential function approximations within the EMQ model. Our improved bounding approach significantly reduces the search interval needed by the bisection method and yields an approximate solution that attains a total cost very close to the true optimum. In a numerical example, the proposed method shrinks the initial search range by over 99% compared to prior methods and achieves a production run length that produces a near-minimal average total cost. These findings demonstrate the effectiveness of the enhanced bounds and provide practical insights for inventory models with imperfect processes. Full article

This study investigates the applicability of finite mixture models (FMMs) for accurately modeling yarn quality parameters in 28/1 Ne ring-spun polyester/viscose yarns, focusing on both yarn imperfections and mechanical properties. The research addresses the need for advanced statistical modeling techniques to better capture the inherent heterogeneity in textile production data. To this end, the Poisson mixture model is employed to represent count-based defects, such as thin places, thick places, and neps, while the gamma mixture model is used to model continuous variables, such as tenacity and elongation. Model parameters are estimated using the expectation–maximization (EM) algorithm, and model selection is guided by the Akaike and Bayesian information criteria (AIC and BIC). The results reveal that thin places are optimally modeled using a two-component Poisson mixture distribution, whereas thick places and neps require three components to reflect their variability. Similarly, a two-component gamma mixture distribution best describes the distributions of tenacity and elongation. These findings highlight the robustness of FMMs in capturing complex distributional patterns in yarn data, demonstrating their potential in enhancing quality assessment and control processes in the textile industry. Full article

The implementation of theranostics in oncologic nuclear medicine has exhibited immense potential in improving patient outcomes in prostate cancer with the implementation of [⁶⁸Ga]Ga-PSMA-11 PET and [¹⁷⁷Lu]Lu-PSMA-617 into clinical practice. However, the correlation between radiopharmaceutical biodistributions seen with [⁶⁸Ga]Ga-PSMA-11 PET imaging and downstream [¹⁷⁷Lu]Lu-PSMA-617 therapy remains imperfect. This suggests that prostate cancer theranostics could potentially be further refined through the implementation of true theranostics, tandem pairs of diagnostic and therapeutic radiopharmaceuticals that utilize the same ligand and element, thus yielding identical pharmacokinetics. The radioscandiums are one such group of true theranostic radiopharmaceuticals. The radioscandiums consist of two β+ emitting scandium isotopes (⁴³Sc/⁴⁴Sc), as well as a β⁻ emitting therapeutic isotope (⁴⁷Sc), which can all conjugate with PSMA-targeting PSMA-617. This potential has led to extensive investigations into the production of the radioscandiums as well as pre-clinical assessments with several ligands; however, there is a lack of literature extensively describing the complete synthesis of scandium radiopharmaceuticals. which therefore limits the accessibility of radioscandium research in theranostics. As such, this work aims to present an easily translatable protocol for the synthesis of [⁴³Sc]Sc-PSMA-617 from a [⁴²Ca]CaCO₃ starting material, including target formation, nuclear production via ⁴²Ca(d,n)⁴³Sc reaction, chemical separation, radiolabeling, solvent reformulation, and target recycling. Full article

The study constructed a supply chain inventory model for sellers and buyers that integrates payment-time-dependent demand, product defects, misclassification risks, and carbon emission tax considerations. The model was designed to optimize payment time, replenishment time, and order quantities to maximize the seller’s profit per unit time. Theoretical analysis showed that profit exhibited joint concavity with respect to both payment time and replenishment time. An algorithm was also formulated to derive optimal solutions. Finally, numerical experiments and sensitivity analyses validated the model and offered practical insights for managing inventories involving imperfect products. Results indicated that higher responsiveness of demand to payment timing, greater demand coefficients, better product prices, and higher scrap values led to increased seller profits, while greater misclassification, credit default risks, and carbon tax rate reduced it. These insights help decision-makers select suitable parameter values for efficient operations. Full article

Uganda is gradually transitioning from communal to private land tenure systems. However, establishing privatized land rights has faced ongoing criticism, particularly concerning their impact on vulnerable groups. Despite the enactment of a national land policy, its benefits have not fully reached rural populations. Issues of land tenure insecurity and unclear ownership continue to generate confusion and have reportedly weakened traditional communal land systems, undermining sustainable agricultural production and long-term investment. This paper examines rural land rights, land markets, and the broader structural transformation of Uganda’s land sector, drawing on the existing literature and published reports. This review reveals that land tenure and administrative challenges persist, largely due to the dominance of customary tenure systems. Although land markets are active, they remain imperfect due to tenure insecurity and legal ambiguities. The findings highlight the need for increased public sensitization regarding land policy, gender-sensitive policies that promote joint ownership, continuous incentives for formalization, the acquisition of land documents, and the harmonization and strengthening of relevant land governance institutions. Full article

This model extends the classical economic production quantity (EPQ) model to address the complexities within a two-echelon supply chain system. The model integrates the cost of raw materials necessary for production and takes into account the presence of imperfect quality items within the acquired raw materials. Upon receipt of the raw material, a thorough screening process is conducted to identify imperfect quality items. Combining imperfect raw material and the concept of shifting production rate, two different inventory models for deteriorating products are formulated under imperfect production with demand dependent on the stock level. In the first model, the imperfect raw materials are sold at a discounted price at the end of the screening period, whereas in the second one, imperfect items are kept in stock until the end of the inventory cycle and then returned to the supplier. Numerical analysis reveals that selling imperfect raw materials yields a favourable financial outcome, with an optimal inventory level $I_1$ = 11,774 units, optimal cycle time $T=2140$ h, and a total profit per hour of USD 183, while keeping the imperfect raw materials to return them to the supplier results in a negative profit of USD $-4.44\times {10}^3$ per hour, indicating an unfavourable financial outcome with the optimal inventory level $I_1$ and optimal cycle time T of 26,349 units and 4702.6 h, respectively. The findings show the importance of selling imperfect raw materials rather than returning them and provide valuable insights for inventory management in systems with deteriorating products and imperfect production processes. Sensitivity analysis further demonstrates the robustness of the model. This study contributes to satisfying the need for inventory models that consider both the procurement of imperfect raw materials, stock-dependent demand, and deteriorating products, along with shifts in production rates in a multi-echelon supply chain. Full article

This paper aims to propose a hybrid collaborative development model based on multi-dimensional behavioral information (HCDMB) to deal with systemic problems in modern software engineering, such as the low efficiency of cross-stage collaboration, the fragmentation of the intelligent tool chain, and the imperfect human–machine collaboration mechanism. This paper focuses on the stages of requirements analysis, software development, software testing and software operation and maintenance in the process of software development. By integrating the multi-dimensional characteristics of the development behavior track, collaboration interaction record and product application data in the process of project promotion, the mixture of experts (MoE) model is introduced to break through the rigid constraints of the traditional tool chain. Reinforcement learning combined with human feedback is used to optimize the MoE dynamic routing mechanism. At the same time, the few-shot context learning method is used to build different expert models, which further improve the reasoning efficiency and knowledge transfer ability of the system in different scenarios. The HCDMB model proposed in this paper can be viewed as an important breakthrough in the software engineering collaboration paradigm, so as to provide innovative solutions to the many problems faced by dynamic requirements and diverse scenarios based on artificial intelligence technology in the field of software engineering involving different project personnel. Full article

Lanthanide biosorption is important for recycling value-added materials. Previously, we analyzed dysprosium (Dy) absorption in screening strains of the unpopular yeast species Schizoblastosporion sp. However, it would be more desirable to use the well-known yeast Saccharomyces cerevisiae to make an easy-to-breed and efficient Dy-absorbing strain. Thus, we analyzed the physiological response and gene regulation of S. cerevisiae under Dy-absorbing conditions. The Dy content was measured using an inductively coupled plasma atomic emission spectrometer (ICP-AES). Transcriptional regulation was compared under Dy-absorbing and non-absorbing conditions through mRNA analysis and quantitative real-time polymerase chain reaction (qRT-PCR). In the yeast cells, approximately 40% of the Dy was located in the cell wall fraction, and the remaining 60% was located in the intracellular fraction. qRT-PCR analysis showed that the expression of four genes, NCW2, PIR1, CRH1, and OLE1, was upregulated, and that of ATP14 was downregulated. These results suggest that NCW2, PIR1, and CRH1 were responsible for cell wall rearrangement; OLE1 initiated repair of the oxidative damage to the membrane lipids; and intracellular oxidation was caused by an imperfect ATP14 product. Full article

Tracing the root cause of defective products in liquid storage tank (LST) production poses a formidable challenge due to the complex dependencies between production and inspection processes. With associated coupling existing among multiple production processes, and the correspondence between the faults in production processes and inspection links being non-unique, these faults are usually difficult to be directly located via a single inspection process. In this paper, the problem of tracing the root cause of defective LST products, which is caused by process parameter deviations or human operation errors during production, is studied. A root cause tracing method that is based on the dynamic multi-fault diagnosis (DMFD) framework is proposed. First, a factorial hidden Markov model (FHMM) is established to depict the state transition process of the LST product, where its status changes over time and across production processes. This is achieved by considering the product state at each production process as a hidden state and the outcomes of each inspection process as an observation state. Then, the Viterbi algorithm is employed to solve the hidden state transition matrix and diagnostic matrix within the framework of the FHMM. Finally, experimental verification is carried out on a real LST assembly production line, and the influence of imperfect testing on the model accuracy is also considered. The experiment is carried out on an LST assembly line that encompasses three discrete links, including the welding of the upper and lower bodies, the installation of check valves, and the installation of sensors. Experimental results demonstrate that the proposed method achieves significantly more superior performance when compared to existing algorithms. Full article