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The purpose of the study is to comprehensively and extensively investigate food waste in hospitals and the environmental and economic effects it imposes on society. By knowing the reasons and factors affecting the creation of such wastes, it is possible to help reduce these effects.

This study was conducted by systematic search in PubMed, Scopus, Science Direct and Web of Science databases.

The results suggest that high-income countries such as Denmark, Sri Lanka, Portugal and Spain have more significant amounts of food waste than low-income countries like Turkey. The main reasons for food waste include inadequate food quality, low patient appetite and issues with the dining environment. Therefore, it is crucial to address these root causes to minimize food waste and alleviate the burden on the environment.

The study relies primarily on secondary data analysis and does not include original food waste audits or interviews with hospital staff. While the literature review provides useful background, conducting first-hand audits and interviews could strengthen the study by providing more current, contextualized and granular insights into food waste in hospitals. The lack of primary data collection limits the ability to make hospital-specific recommendations or quantify the opportunity for improvement. Additionally, the focus is on general frameworks rather than interventions tailored to the needs and constraints of hospitals in low- and middle-income regions. More region-specific research would be needed to understand nuanced challenges and develop customized solutions suitable for implementation in those settings.

Effective measures can be taken to make policies and determine appropriate solutions to reduce these wastes. By recognizing the problems in structures, one can take steps to reduce global warming and waste of national funds.

This study aims to systematically review the health risk assessment of the concentration of heavy metals in Pistacia species globally.

The authors systematically searched PubMed, Science Direct, Scopus and Google Scholar to identify all articles published between 1 January 2002 and 20 August 2022. A total of 33 studies met the authors’ inclusion criteria, and their data were extracted. Additionally, the potential risk to human health was assessed by calculating the target hazard quotient and hazard index for both child and adult consumers.

The estimated daily intake for heavy metals in the included studies ranged from 9.72 × 10^–9 to 7.35 (mg/day) in the following order: zinc (Zn) > mercury (Hg) > iron (Fe) > lead (Pb) > copper (Cu) > aluminum (Al) > nickel (Ni) > chromium (Cr) > manganese (Mn) > cadmium (Cd) > arsenic (As) > selenium (Se) > cobalt (Co). Among the studies that investigated heavy metals in Pistacia species around the world, the non-carcinogenic risk for all species of Pistacia was determined to be less than 1, except for Pb and Hg in Pistacia lentiscus.

The soil near the industrial area contained excessive amounts of heavy metals, which led to the transfer of heavy metals to plants. Owing to the insufficiency of the number of studies that examined heavy metals in Pistacia species, further monitoring and investigations were recommended.

In recent decades, the student population in dormitories has increased notably, primarily attributed to the growing number of international students. Dormitories serve as pivotal environments for student development. The coordination and compatibility among students can significantly influence their overall success. This study aims to introduce an innovative method for roommate selection and room allocation within dormitory settings.

In this study, initially, using multi-attribute decision-making methods including the Bayesian best-worst method and weighted aggregated sum product assessment, the incompatibility rate among pairs of students is calculated. Subsequently, using a linear mathematical model, roommates are selected and allocated to dormitory rooms pursuing the twin objectives of minimizing the total incompatibility rate and costs. Finally, the grasshopper optimization algorithm is applied to solve large-sized instances.

The results demonstrate the effectiveness of the proposed method in comparison to two common alternatives, i.e. random allocation and preference-based allocation. Moreover, the proposed method’s applicability extends beyond its current context, making it suitable for addressing various matching problems, including crew pairing and classmate pairing.

This novel method for roommate selection and room allocation enhances decision-making for optimal dormitory arrangements. Inspired by a real-world problem faced by the authors, this study strives to offer a robust solution to this problem.

The purpose of this paper is to explore how the acquisition experience – an acquiring firm’s experience of acquiring and integrating the resources of an acquired firm – affects the production resource efficiency of the acquiring firm.

The authors used data obtained from US manufacturing industries over the 1992–2014 period. The sample includes 784 acquisitions by 417 firms. The proposed hypotheses were tested through econometric analysis.

Results show that the acquisition experience has a positive association with production resource efficiency. The acquisition experience is most positively associated with acquiring firms’ production efficiency when they successfully accomplished previous performance outcomes. While the literature has recognized the relatedness of acquiring and acquired firms as a contextual moderator, the interaction of the related acquisition and the acquisition experience has no impact on efficiency benefits.

This study enhances the understanding of how prior acquisition experience can be leveraged by acquiring firms to gain efficiency benefits in the manufacturing industry.

The present study aims to construct ensemble machine learning (EML) algorithms for groundwater potentiality mapping (GPM) in the Teesta River basin of Bangladesh, including random forest (RF) and random subspace (RSS).

The RF and RSS models have been implemented for integrating 14 selected groundwater condition parametres with groundwater inventories for generating GPMs. The GPM were then validated using the empirical and bionormal receiver operating characteristics (ROC) curve.

The very high (831–1200 km²) and high groundwater potential areas (521–680 km²) were predicted using EML algorithms. The RSS (AUC-0.892) model outperformed RF model based on ROC's area under curve (AUC).

Two new EML models have been constructed for GPM. These findings will aid in proposing sustainable water resource management plans.

The purpose of this study is to examine the effects of using discrete and continuous porous layers on the convective heat transfer improvement for multiple slot jet impingement onto a flat surface under magnetic field.

In the domains which are separated by the porous layers, uniform magnetic field with different strengths is used and as the solution technique finite element method is used. The numerical study is conducted considering different values of parameters: Reynolds number (250–1000), strength of magnetic field in different domains (Hartmann number between 0 and 20), permeability of discrete or continuous layers (Darcy number between 105 and 102) and number of layers in discrete case (2–10). Artificial neural network is used for performance estimation of systems equipped with different types of porous layers.

It is observed that significant differences occur in the local Nu between the discrete and continuous layer case, especially at lower Re, while peak Nu value is 77% higher in discrete layer configurations as compared to continuous one at Re = 250. Upper domain magnetic field results in average Nu enhancement, while the trend is opposite for the lower domain magnetic field strength. The increment amount becomes 10%, while the reduction amount is obtained as 38% at the highest magnetic field strengths. The permeability of layers in both cases and number of layers in discrete porous layer case provide effective solution for the cooling performance control. A modeling approach based on artificial neural networks provides fast thermal performance estimations of multiple impinging jets equipped with discrete and continuous porous layers.

Outcomes of the study are useful in development and optimization of new cooling systems in many thermal engineering systems encountered in photovoltaic panels, micro-electro-mechanical systems, metal processing and many others.

This paper aims to study numerically the steady natural convective heat transfer of a hybrid nanosuspension (Ag-MgO/H2O) within a partially heated/cooled trapezoidal region with linear temperature profiles at inclined walls under an effect of uniform Lorentz force. This investigation is useful for researchers studying in the area of cavity flows to know features of the flow structures and nature of hybrid nanofluid characteristics. In addition, a detailed entropy generation analysis has been performed to highlight possible regimes with minimal entropy generation rates.

The governing equations formulated using the Oberbeck–Boussinesq approach and single-phase nanoliquid model are transformed to a non-dimensional form by using non-dimensional variables. The obtained equations with appropriate boundary conditions are resolved by the finite difference technique. The developed code has been validated comprehensively. Analysis has been performed for a wide range of governing parameters, including Rayleigh number (Ra = 105), Prandtl number (Pr = 6.82), Hartmann number (Ha = 0–100), magnetic field inclination angle (φ = 0–?/2) and nanoparticles volume fraction (φ_hnf = 0 and 2%).

It has been shown that inclined magnetic field can be used to manage the energy transport performance. An inclusion of nanoparticles without Lorentz force influence allows forming more stable convective regime with descending heat plume in the central zone, while such a regime was performed for clear fluid only for moderate and high Hartmann numbers. Moreover, the average overall entropy generation can be decreased with a growth of the Hartmann number, while an addition of hybrid nanoparticles allows reducing this parameter for Ha = 30 and 50. The average Nusselt number can be increased with a growth of the nanoparticles concentration for low values of the magnetic field intensity.

Governing equations written using the conservation laws and dimensionless non-primitive variables have been resolved by the finite difference approach. The created numerical code has been verified by applying the grid independence test and computational outcomes of other researchers. The comprehensive analysis for various key parameters has been performed.