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This article is the first generalizable empirical analysis of dual branding, that is, the supply of private labels (PLs) by national brand (NB) manufacturers. The authors compile a unique data set combining the identity of PL suppliers in over 260 packaged goods categories with multiple years of scanner data in the Spanish grocery market to offer several contributions. First, they provide new descriptive insights on the prevalence of dual branding in categories where the manufacturer does and does not have NBs, the longevity of PL supply arrangements, and the differences in PL sourcing across retailers. Second, they integrate the literature on motivators and dissuaders of dual branding and test the impact of relevant manufacturer, retailer, and dyad characteristics on PL supply in NB and non-NB categories. The results reveal a more nuanced empirical reality than is evident from prior research regarding the role of multicategory scope, fighter brands, NB differentiation, and size and positioning of the retailer's PL. Third, they examine the outcomes of PL supply for the NBs of dual branders and find that starting (terminating) PL supply to a retailer significantly benefits (hurts) the relative distribution depth but not the relative share of the dual brander's NBs at that retailer. [ABSTRACT FROM AUTHOR]

In the present work, mechanical, physical, wettability, and microstructure characterization were employed to evaluate the performance of composite material. Uniform distribution with a good interfacial layer of SAC and Si3N4 was observed for PET/8.75%Si3N4+8.75% wasteSAC powder composite material. Results showed significant improvements in tensile (60.55%), hardness (150.99%), bending (228.47%), and compressive strength (105.56%) compared to neat PET-based composites after the addition of 8.75%Si3N4+8.75% wasteSAC powder. Therefore, studied composite could be a replacement of metals and ceramics, in various structural and functional applications due to their improved mechanical and thermal properties. [ABSTRACT FROM AUTHOR]

A thermal management method for data centers from the server level is proposed in this paper. This paper explores the high energy consumption of the server itself, proposing a principle of microchannel shape design for a cooling plate based on the principal stress lines (PSLs). Under the condition of a uniform load, the influence of the cross-sectional shape of the microchannel on the contact thermal resistance was found to be insignificant. From the analysis of cooling plate plane, the uniformity of the stress distribution of the microchannel determined the uniformity of the contact thermal resistance, which determined the heat dissipation ability. Experiments were developed to test and verify the cooling performance of the cooling plate as well as the performance differences with the microchannels along and away from the PSLs under deformation. It was concluded that compared with the conventional serpentine microchannel, the cooling performance of the cooling plate proposed in this paper was 4.5 $^\circ {\rm{C}}$ ∘ C reduction in average temperature. Furthermore, when the ultra-thin cooling plate was subjected to a uniform load distribution, the microchannel along a single PSL had the best stress distribution, that is, the contact thermal resistance had the least variation. [ABSTRACT FROM AUTHOR]

Leakages are very prevalent in water distribution networks in China, particularly in urban areas. District metering areas (DMAs) provide a practical approach for monitoring the operation of water supply systems and a robust scientific basis for controlling leakages. Therefore, in this study, we assessed leakages in the water distribution network at the city scale using DMAs, with Wuhu, Anhui Province, China as an example. Residential districts in the urban area were divided into four DMA levels. The minimum night flow (MNF) method was used to investigate the extent of the leakages from the urban water distribution network in Wuhu. Finally, we summarize how DMAs could support the management of the urban water supply network during the planning, implementation, and evaluation stages. The results from this case study showed that DMAs and MNF could be integrated to assess the water network leakages in Wuhu. [ABSTRACT FROM AUTHOR]

Issues of fairness relate to distributive justice (DJ), which is concerned with how a society distributes benefits and burdens. Whilst marketing theory has considered fairness in the distribution of basic goods, we lack insight into stakeholder perspectives on fairness for goods such as alcohol, which can be problematic, due to their global health and social burden. This study examines stakeholder perspectives on fairness from the Kenyan alcohol marketplace. Using longitudinal ethnographic data, we draw on two DJ theories, (egalitarianism and prioritarianism), to examine fairness perspectives of different stakeholders, their problem diagnosis, and proposed solutions to fairness challenges in the alcohol marketplace. By so doing, we give voice to previously unheard stakeholders, and expose some of the potential theoretical foundations for competing notions of fairness. The study also exemplifies the linkages between different forms of fairness and proposes a fairness chain as a framework for evaluating fairness in the marketplace. [ABSTRACT FROM AUTHOR]

The Plains Spotted Skunk (Spilogale interrupta) historically occurred across parts of the eastern and midwestern United States west of the Mississippi River. Following perceived population declines across much of its range, efforts to assess populations of this species have increased. Here, we report novel records of S. interrupta from this area of south Texas for the first time since 1961. This rediscovery demonstrates the persistence of this species in an underreported portion of its range. This information is useful for better understanding the distribution and population status of S. interrupta in Texas and broadly throughout North America and is crucial for future applied conservation policy efforts. [ABSTRACT FROM AUTHOR]

It has been widely acknowledged that probability-based methodologies play a crucial role in guiding decision-making processes when faced with uncertain or vague circumstances. To ensure that decisions made under such conditions are both meaningful and productive, a variety of methodologies centered around probability have been developed, endorsed, and put into practice. This study also aims to contribute to the existing body of knowledge by introducing a new probability model known as the weighted sine generalized Rayleigh distribution. For the proposed model, certain properties are derived. Furthermore, the parameter estimation technique and simulation studies based on the proposed model are also presented. Finally, the proposed model is successfully implemented on three distinct data sets sourced from sports and different engineering disciplines. By evaluating the statistical significance through the p -value and other various tests, it is evident that the proposed model consistently outperforms its competitor distributions. The practical illustration, emphasizes the efficacy and potential of the proposed model for widespread applications in sports and engineering research and practice. [ABSTRACT FROM AUTHOR]

With the increasing demand for fresh food markets, refrigerated transportation has become an essential component of logistics operations. Currently, fresh food transportation frequently faces issues of high energy consumption and high costs, which are inconsistent with the development needs of the modern logistics industry. This paper addresses the optimization problem of multi-vehicle type fresh food distribution under time-varying conditions. It comprehensively considers the changes in road congestion at different times and the quality degradation characteristics of fresh goods during distribution. The objectives include transportation cost, dual carbon cost, and damage cost, subject to constraints such as delivery time windows and vehicle capacity. A piecewise function is used to depict vehicle speeds, proposing a dynamic urban fresh food logistics vehicle routing optimization method. Given the NP-hard nature of the problem, a hybrid Tabu Search (TS) and Genetic Algorithm (GA) approach is designed to compute an optimal solution. Comparison with TS and GA algorithm results shows that the TS-GA algorithm provides the best optimization efficiency and effectiveness for solving large-scale distribution problems. The results indicate that using the TS-GA algorithm to optimize a distribution network with one distribution center and 30 delivery points resulted in a total cost of CNY 12,934.02 and a convergence time of 16.3 s. For problems involving multiple vehicle types and multiple delivery points, the TS-GA algorithm reduces the overall cost by 2.94–7.68% compared to traditional genetic algorithms, demonstrating superior performance in addressing multi-vehicle, multi-point delivery challenges. [ABSTRACT FROM AUTHOR]

In situ X-ray photoelectron spectroscopy (XPS) techniques have proven to be powerful tools for the characterisation of the solid electrolyte interphase (SEI) formed between the anode and solid electrolyte (SE) in solid-state batteries. XPS offers access to time and operational condition-resolved information on the SEI's chemical composition in the absence of destructive sample preparation. Here we present a Virtual Electrode Plating XPS (VEP-XPS) investigation of the composition and stability of the SEI formed between lithium metal and two different solid electrolytes: Li10GeP2S12 (LGPS) and Li1.5Al0.5Ge1.5(PO4)3 (LAGP). LAGP shows slower SEI formation kinetics, as proven by the emergence of a metallic lithium signal, while LGPS exhibits rapid SEI growth that prevents metallic lithium from plating. We attribute these observations to the SEI composition, distribution and physical properties of secondary decomposition products and in particular to the mixed ion-electron conductive Li3P which can be observed in LGPS and not in LAGP. [ABSTRACT FROM AUTHOR]

A growing and increasingly globalised human population, requiring the movement of goods and commodities, is placing increasing demands on the maritime industry, resulting in a concurrent increase in global shipping activities. This has consequences for the marine environment, particularly for species vulnerable to the impacts of vessel traffic. For example, vessel collisions can result in sub‐lethal or fatal injuries for marine mammals, whilst vessel noise can cause acoustic masking that effectively reduces an animal's listening space, potentially impacting their communication, navigation and foraging capacity.While a number of parallel approaches to mapping collision risk to large whales have arisen, these methods vary in their focus, usually on either co‐occurrence, collision probability, or probability of mortality. However, little attention has been given to the implications of methodological choice and data selection on subsequent risk predictions.To assess differences between these approaches, we used a standardised input dataset comprised of telemetry‐point data from tagged bowhead whales, and satellite‐based Automated Identification System (AIS) data of spatial vessel movements covering the Davis‐Baffin Arctic Marine Area. We applied this data to eight different, previously published analyses for deriving areas of vessel risk.We found that the choice of risk mapping approach affected the location, and total area, identified as 'high risk', and that more computationally complex approaches did not necessarily equate to different predictions. There was considerable variation in the total area of 'high risk' predicted within each map (range = 20–42,246 km2).Synthesis and Applications. The results underscore the importance of methodological transparency, informed data selection and careful interpretation when predicting collision risk. We provide practical recommendations for enhancing transparency when predicting risk, and discuss choice of approach suitable for different situations or management applications. It is critical that managers and policy makers are aware of the implications of applying different approaches when interpreting risk evaluation outputs. [ABSTRACT FROM AUTHOR]

Decision trees built with data remain in widespread use for nonparametric prediction. Predicting probability distributions is preferred over point predictions when uncertainty plays a prominent role in analysis and decision-making. We study modifying a tree to produce nonparametric predictive distributions. We find the standard method for building trees may not result in good predictive distributions and propose changing the splitting criteria for trees to one based on proper scoring rules. Analysis of both simulated data and several real datasets demonstrates that using these new splitting criteria results in trees with improved predictive properties considering the entire predictive distribution. [ABSTRACT FROM AUTHOR]

Introduction: The objective of this experiment was to investigate the thermal effects on hyaluronic acid fillers, PCL fillers, and PDO threads when exposed to controlled heat. This study aims to provide insights into how these materials respond to thermal energy, which is crucial for safe and effective cosmetic procedures involving combined modalities. Materials and Methods: Cadaveric tissue was utilized to simulate clinical conditions. Hyaluronic acid fillers were injected at approximately 1 mm and 5 mm thicknesses, with variations in G' value (high and low). PCL fillers were similarly injected in 1 mm and 5 mm thicknesses. PDO threads were also inserted. All materials were injected at a depth of 2 cm. A thermometer was used to measure heat penetration, and a multi‐wavelength laser was applied to the tissue. The temperature was maintained at 60°C for 5 min to assess whether heat penetrated more than 3 cm in thickness. Observations were made regarding the heat distribution and any physical changes in the fillers and threads. Results: In thick layers, heat accumulated above the PCL filler without penetrating deeper layers. In thin layers, heat penetration was observed. For the HA fillers, heat energy was not blocked, regardless of the G' value or thickness. For the threads, no significant heat blockage effect was observed. For all materials, no visual changes were detected in any of the materials due to temperature exposure. Discussion: The findings suggest that the thickness and composition of fillers significantly influence heat penetration. Thick PCL fillers act as a thermal barrier, whereas thin PCL fillers allow deeper heat penetration. Hyaluronic acid fillers do not impede heat transfer, regardless of their physical properties. PDO threads do not exhibit any notable thermal resistance. These insights are essential for optimizing the safety and efficacy of combined filler and energy‐based device treatments in esthetic medicine. [ABSTRACT FROM AUTHOR]

A pressure correction method is proposed considering the influence of a dual factor. The applicability of a pressure correction method coupled with a drag model is discussed along with the accuracy of the simulation results obtained by such a pressure correction method. It is found that the present pressure correction method combined with the DBS (dual bubble size) drag model can accurately reflect the changing trend of gas holdup distribution with pressure. It is also established that results from this model applied to a bubble column match well with the experimental data. Finally, when compared with other pressure correction models, the proposed model shows better robustness in three‐dimensional simulations and can predict radial gas holdup distributions with better accuracy. [ABSTRACT FROM AUTHOR]

This article draws attention to the reproduction of logistical power in what is identified as the eroticism of logistics. Eroticism here describes the way that transmission is understood as a seamless conveyance of goods or an intimate communication across a surface. It is first argued that this eroticism is found in attempts to define modernity as logistical that ought to be rejected in favor of a more grounded account of rerouting. It is then demonstrated that this latter account best accommodates the findings of critical logistics studies, where logistical spaces are shown to be fractious and the movement of goods far from smooth. It is finally argued that while an erotic principle of transmission lends itself to a reductive account of logistics that sustains its violence, a postal account of transmission better captures the experience of getting the goods while situating logistics within a critical space. [ABSTRACT FROM AUTHOR]

Improvement in color uniformity of white light-emitting diodes (WLED) is one of the imperative goals for high-quality solid-state illumination. The conventional WLED model with a single yellow phosphor YAG:Ce3+ and blue LED chip does not provide good color distribution and often has a yellow-ring phenomenon. Using a diffusing layer to induce scattering light efficiency can address this issue for such a conventional WLED package. A diffusing layer comprised of graphene quantum dots (GDs) and TiO2 (TiO2@GD) is proposed to fulfill this goal. The TiO2@GD composites prove to possess excellent biocompatibility, low toxicity, and thermal and chemical stability, holding great potential in high-power WLED production. By maintaining a constant GDs content of 10 wt%, the research explores the impact of varying TiO2 doping concentrations on the lighting performance of the WLEDs via the mean of light scattering. The TiO2@GD layer also induces a red-shift in the emitted light spectrum, contributing to a reduction in color variation. While a decline in luminosity and color rendering performance becomes evident with excessive TiO2 content, the study underscores the potential of TiO2@GD as a viable diffusing layer for LEDs to obtain improved angular uniformity of color distribution. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to determine the effect of clothing fabrics, sizes and air ventilation rate on the volume and thickness of the air gap under the air ventilation garments (AVGs). Design/methodology/approach: The geometric models of the human body and clothing were obtained by using a 3D body scanner. Then the distribution of the volume and thickness of the air gap for four clothing fabrics and three air ventilation rates (0L/S, 12L/S and 20L/S) were calculated by Geomagic software. Finally, a more suitable fabric was selected from the analysis to compare the distribution of the air gap entrapped for four clothing sizes (S, M, L and XL) and the three air ventilation rates. Findings: The results show that the influence of air ventilation rate on the air gap volume and thickness is more obvious than that of the clothing fabrics and sizes. The higher is the air ventilation rate, the thicker is the air gap entrapped, and more evenly distributed is the air gap. It can be seen that the thickness of the air gap in the chest does not change significantly with the changes of the air ventilation rates, clothing fabrics and sizes, while the air gap in the waist is affected significantly. Originality/value: This research provides a better understanding of the distribution of the air gap entrapped in ventilated garments, which can help in designing the optimal air gap dimensions and thus provide a basis and a reference for the design of the AVGs. [ABSTRACT FROM AUTHOR]

With a large footprint size, multiple scattering measurements of clouds from spaceborne lidar provide useful information about cloud physical properties, such as cloud optical depths and cloud droplet size, both during daytime and nighttime. A neural network algorithm, with a subset of cloud backscatter profiles of dualpolarization and dual-wavelength Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar measurements during daytime as input variables and cloud physical properties derived from collocated Moderate Resolution Imaging Spectroradiometer (MODIS) multi-spectral measurements as output, is developed and evaluated with an independent subset of the collocated CALIPSO and MODIS measurements. The study suggests that with a receiver footprint size of 110 m, CALIPSO lidar measurements are sensitive to liquid-phase cloud optical depth variations from0 to 25. A larger footprint size, thus more multiple scattering, is required for lidar to have sensitivities to all liquid-phase clouds. The technique can be applied to all 17 years of CALIPSO daytime and nighttime measurements and, thus, provides useful information about global distributions of cloud physical properties both during day and night. [ABSTRACT FROM AUTHOR]

This study analyzes and determines the impact of financial performance, audit quality, corporate social responsibility (CSR) on firm value through cost of capital as mediation. This type of quantitative research uses an explanatory approach and causality of as many as 565 samples for 5 years in the ASEAN banking industry. The results of this study show that financial performance, audit quality, and CSR affect the company value . Banks with good financial performance, quality audits, and good CSR distribution will be able to improve the company’s image. For this reason, banking companies must improve financial performance, audit quality, and properly carry out responsibilities. Cost of capital becomes a mediating variable that strengthens the relationship of financial performance, audit quality, and CSR to company value. For banks when their financial performance is good, checks are carried out with good audit quality, channeling social responsibility well will be a trigger for increasing company value and will indirectly reduce capital costs that must be incurred by the company. [ABSTRACT FROM AUTHOR]

This study investigates the role of the probability distribution in forecasting the volatility and value-at-risk (VaR) of cryptocurrency returns using generalized auto-regressive conditional heteroskedasticity (GARCH)-type models. We consider GARCH, EGARCH, GJR-GARCH, TGARCH and Realized GARCH models and show that the role of the probability distribution varies across different situations. A skewed and heavy-tailed distribution contributes to better performance in forecasting the VaR; however, it does not improve the accuracy of volatility forecasting. The results help us to better understand the role of the probability distribution in GARCH-type models. [ABSTRACT FROM AUTHOR]

With the proliferation of vehicular mobility traces because of inexpensive on-board sensors and smartphones, utilizing them to further understand road movements have become easily accessible. These huge numbers of vehicular traces can be utilized to determine where to enhance road infrastructures such as the deployment of electric vehicle (EV) charging stations. As more EVs are plying today's roads, the driving anxiety is minimized with the presence of sufficient charging stations. By correctly extracting the various transportation parameters from a given dataset, one can design an adequate and adaptive EV charging network that can provide comfort and convenience for the movement of people and goods from one point to another. In this study, we determined the possible EV charging station locations based on an urban city's vehicular capacity distribution obtained from taxi and ride-hailing mobility GPS traces. To achieve this, we first transformed the dynamic vehicular environment based on vehicular capacity into its equivalent urban single snapshot. We then obtained the various traffic zone distributions by initially utilizing k-means clustering to allow flexibility in the total number of wanted traffic zones in each dataset. In each traffic zone, iterative clustering techniques employing Density-based Spatial Clustering of Applications with Noise (DBSCAN) or clustering by fast search and find of density peaks (CFS) revealed various area separation where EV chargers were needed. Finally, to find the exact location of the EV charging station, we last ran k-means to locate centroids, depending on the constraint on how many EV chargers were needed. Extensive simulations revealed the strengths and weaknesses of the clustering methods when applied to our datasets. We utilized the silhouette and Calinski–Harabasz indices to measure the validity of cluster formations. We also measured the inter-station distances to understand the closeness of the locations of EV chargers. Our study shows how CFS + k-means clustering techniques are able to pinpoint EV charger locations. However, when utilizing DBSCAN initially, the results did not present any notable outcome. [ABSTRACT FROM AUTHOR]

The automobile cable systems for loading and unloading operations between the vehicle-support and vehicle-vehicle points are presented in this paper. The scheme of the automobile mobile cable system is based on a two-cable cableway. Based on this scheme, the parameters of the movement of goods by automobile cable systems are substantiated at variable parameters: mass m artificial loads together with the mass of the frame structure: 35 ≤ m ≤ 95 (kg); the angle of inclination α pairs of parallel ropes to the horizon: 10 ≤ α ≤ 15 (°); tension force ropes 2400 ≤ TD ≤ 5000 (N). It has been established that the dominant effect on the minimum time T has angle α, then mass m and tension forces TD ropes. At the same time, it is worth noting that the range of discrepancy of the quantities T values, when the above factors change, is insignificant and is close to 5,7% for (m/α) and 6,6% for (TD/α). [ABSTRACT FROM AUTHOR]

When the member states imposed unilateral restrictions on the cross-border movement of persons and goods in their initial response to the COVID-19 pandemic, the EU appeared to relapse into the 'politics trap' of earlier integration crises. However, our analysis of entry restrictions for persons in France, Germany, Italy, the Netherlands, and Poland from the end of 2019 to the summer of 2022 shows no systematic relationship between domestic politicisation and national border closures. Rather, border closures followed the course of the pandemic as well as EU recommendations. Our findings suggest that the EU was able to escape the 'politics trap' thanks to the exogenous and symmetrical nature of the crisis and effective EU-level policy coordination. [ABSTRACT FROM AUTHOR]

The transportation of goods in South Sulawesi remains predominantly reliant on road networks, with truck transport playing a central role in the region's logistics. Despite the introduction of rail infrastructure, its effectiveness as an alternative freight transportation mode has not been comprehensively evaluated. This study assesses the reliability, costefficiency, and environmental impact of both truck and rail transport along the Makassar-Parepare route, employing both qualitative and quantitative methodologies. The findings reveal that road transport demonstrates superior reliability and operational flexibility, with trucks offering lower transportation costs at IDR 404 per ton-kilometre, compared to IDR 511 per ton-kilometre for rail. However, rail transport exhibits increasing cost efficiency over longer distances, particularly beyond 360 kilometres. A modal shift of 30% from trucks to rail has the potential to reduce carbon emissions by 22.6%, contributing to environmental sustainability through a 29.5% reduction in fuel consumption. These results highlight the need for an integrated approach to transport infrastructure development, where economic, environmental, and social dimensions are holistically addressed. Future efforts to enhance rail infrastructure could provide significant long-term benefits by alleviating road congestion and supporting sustainable freight transportation. [ABSTRACT FROM AUTHOR]

Deep learning models demonstrate impressive performance in rapidly predicting urban floods, but there are still limitations in enhancing physical connectivity and interpretability. This study proposed an innovative modeling approach that integrates convolutional neural networks with weighted cellular automaton (CNN-WCA) to achieve the precise and rapid prediction of urban pluvial flooding processes and enhance the physical connectivity and reliability of modeling results. The study began by generating a rainfall-inundation dataset using WCA and LISFLOOD-FP, and the CNN-WCA model was trained using outputs from LISFLOOD-FP and WCA. Subsequently, the pre-trained model was applied to simulate the flood caused by the 20 July 2021 rainstorm in Zhengzhou City. The predicted inundation spatial distribution and depth by CNN-WCA closely aligned with those of LISFLOOD-FP, with the mean absolute error concentrated within 5 mm, and the prediction time of CNN-WCA was only 0.8% that of LISFLOOD-FP. The CNN-WCA model displays a strong capacity for accurately predicting changes in inundation depths within the study area and at susceptible points for urban flooding, with the Nash-Sutcliffe efficiency values of most flood-prone points exceeding 0.97. Furthermore, the physical connectivity of the inundation distribution predicted by CNN-WCA is better than that of the distribution obtained with a CNN. The CNN-WCA model with additional physical constraints exhibits a reduction of around 34% in instances of physical discontinuity compared to CNN. Our results prove that the CNN model with multiple physical constraints has significant potential to rapidly and accurately simulate urban flooding processes and improve the reliability of prediction. [ABSTRACT FROM AUTHOR]

Organizations yearn to develop effective distribution of goods and services. This is also true for agricultural produce such as tea. A significant proportion of the local tea is distributed by Kenya Tea Packers Limited (KETEPA), which is a subsidiary of the Kenya Tea Development Agency (KTDA). KETEPA is expected to develop mechanisms to guarantee increased sales performance of tea products. Exclusive distribution is expected to improve the sales performance of tea products. Therefore, the study sought to examine the role of an entire distribution strategy on sales performance. The study adopted a correlation research design and collected quantitative data using a structured questionnaire. Data was collected from 221 sales staff dealing with tea product distribution. The theory of Channel Control was used to explain the relationship between study constructs. Findings revealed that exclusive distribution significantly influences the performance of tea products. It is against this backdrop, that managers are expected to develop strategies, policies and procedures for exclusive distribution to foster the sale performance of tea products. [ABSTRACT FROM AUTHOR]

Three triangular friction block configurations are commonly employed in high-speed train brake systems, namely, unperforated, perforated configuration with one circular hole, and perforated with three circular holes. In this study, we adopted these friction block types to investigate the effect of perforated friction block configurations on the brake performance of high-speed trains based on a self-developed brake test rig. The results indicate the significant impact of the number of the holes on the wear behavior, temperature distribution, and vibration characteristics of the brake interface. The friction surface of the unperforated block is covered by wear debris, while the perforated blocks produce less wear debris. Furthermore, the one-hole block exhibits a more uniform temperature distribution and better vibration behavior than that with three holes. The friction brake is a dynamic process, during which separation and attachment between the pad and disc alternatively occur, and the perforated structure on the friction block can both trap and expel the wear debris. [ABSTRACT FROM AUTHOR]

This paper estimates the impact of the government-mandated intensity of the lockdown across various zones on the period product consumption for menstruating women in India. We have used the national-level panel data from the Centre for Monitoring Indian Economy (CMIE)'s Consumer Pyramids Household Survey (CPHS) database across 510 districts of India, along with the lockdown zones data. Our study uses a robust strategy to exploit the temporal (non-lockdown vs. during lockdown) and spatial (across red, orange, and green zones) variation by using difference-in-difference estimates by exploring the impact of lockdown policy on period product expenditure over households from January 2020 to December 2020. Our results show a reduction of 16% in period products consumption in red zone districts compared to the green zone districts. When restricting attention to rural districts only, this reduction was 25%. Travel restrictions on consumers combined with restrictions on movements of goods led to severe shortages in period products. There is a strong need to start interventions to improve the period products usage and create robust infrastructure to ensure deliveries, especially during emergencies. [ABSTRACT FROM AUTHOR]

Firms and other entities provide category-level product attribute information via attribute filters and other tools to aid consumers in filtering, evaluating, comparing, and choosing products. This research examines how displaying this information as a range with or without the distribution of values systematically affects judgments involving attribute value comparisons. Specifically, distributions draw attention away from attribute values, reducing the importance of attribute value differences. With this reduced importance, consumers are less sensitive to attribute value differences; thus, consumers evaluate individual products more positively as they seem more similar to the best available option. Likewise, wider bands of attribute values are selected when filtering product options, as the minimum and maximum values chosen seem less different. Reduced sensitivity to differences also has implications for choices involving tradeoffs between attributes. Importantly, the presence of a distribution itself is the primary driver of this effect, more so than distribution type, as the effect is largely independent of the type of distribution displayed (e.g. normal, bimodal, skewed, uniform). Across six main and seven supplemental experiments , this research highlights a novel consideration for how consumers filter options, form preferences, and choose products. These findings have practical implications and highlight important topics for future research. [ABSTRACT FROM AUTHOR]

Maintaining the quality and integrity of frozen goods throughout the supply chain necessitates a robust and efficient cold chain logistics network. This research proposes a machine learning-based method for optimizing such networks, resulting in significant cost reduction and resource utilization improvement. The method employs a three-phase approach. First, K-means clustering groups sellers based on their geographical proximity, simplifying the problem and enabling more accurate demand prediction. During the second phase of the proposed method, Gaussian Process Regression models predict future sales volume for each seller cluster, leveraging historical sales data. Finally, the Capuchin Search Algorithm simultaneously optimizes distributor location and resource allocation for each cluster, minimizing both transportation and holding costs. This multi-objective approach achieved a 34.76% reduction in costs and a 15.6% reduction in resource wastage compared to the existing system. This novel method offers a valuable tool for frozen goods distribution networks, with advantages such as considering multiple goals for optimization, focusing on demand prediction, potential for reduced complexity, and focusing on managerial insights over compared methods. [ABSTRACT FROM AUTHOR]

Simple and commercially available Lewis acids (LAs) are commonly used catalysts in anionic ring‐opening polymerization (AROP) reactions. In particular, for the AROP of epoxides, the addition of a Lewis acid allows the transition from a so‐called end‐chain mechanism to a monomer‐activated mechanism. The presence of the LA simultaneously leads to a decrease in the reactivity of active centers through the formation of a three‐species ate complex and to the activation of the monomer by LA coordination to the oxygen atom of the oxirane ring. These two effects result in both an increase in propagation kinetics and a decrease in transfer reactions, which has enabled the synthesis of high molecular weight polyethers. However, the impact of Lewis acids goes far beyond these classic effects. They have indeed enabled the polymerization of new functional monomers as well as the synthesis of heterotelechelic macromolecules. Also widely used as catalysts in copolymerization reactions (statistical, sequential, and alternating) Lewis acids can strongly influence the composition and sequence of monomer units in macromolecules. Finally, Lewis acids can also significantly influence the architecture of the obtained macromolecules. This review aims to list the various contributions of Lewis acids to macromolecular engineering and illustrate them with well‐chosen examples. [ABSTRACT FROM AUTHOR]

Combining the public JWST /NIRCam imaging programs CEERS, PRIMER, and JADES, spanning a total area of |$\sim 500\, {\rm arcmin}^2$|⁠ , we obtain a sample of |$\gt $| 30 000 galaxies at |$z_{\rm phot}\sim 4\!-\!9$| that allows us to perform a complete, rest-optical-selected census of the galaxy population at |$z\gt 3$|⁠. Comparing the stellar mass |$M_*$| and the UV-slope |$\beta$| distributions between JWST - and HST -selected samples, we generally find very good agreement and no significant biases. Nevertheless, JWST enables us to probe a new population of UV-red galaxies that was missing from previous HST -based Lyman-break galaxy (LBG) samples. We measure galaxy stellar mass functions (SMFs) at |$z\sim 4\!-\!9$| down to limiting masses of |$10^{7.5}\!-\!10^{8.5}\, {\rm M_\odot }$|⁠ , finding steep low-mass slopes over the entire redshift range, reaching values of |$\alpha \approx -2$| at |$z\gtrsim 6$|⁠. At the high-mass end, UV-red galaxies dominate at least out to |$z\sim 6$|⁠. The implied redshift evolution of the SMF suggests a rapid build-up of massive dust-obscured or quiescent galaxies from |$z\sim 6$| to |$z\sim 4$| as well as an enhanced efficiency of star formation towards earlier times (⁠|$z\gtrsim 6$|⁠). Finally, we show that the galaxy mass density grows by a factor |$\sim 20\times$| from |$z\sim 9$| to |$z\sim 4$|⁠. Our results emphasize the importance of rest-frame optically selected samples in inferring accurate distributions of physical properties and studying the mass build-up of galaxies in the first 1.5 Gyr of cosmic history. [ABSTRACT FROM AUTHOR]

Purpose: The paper discusses the application of outsourcing in logistics in companies operating online. Companies operating in the global market are constantly seeking new solutions that will enable them to gain and maintain a competitive advantage. One of the solutions used to increase the efficiency of an organisation is outsourcing. The main objective is to identify the advantages and disadvantages of outsourcing in the online market. The application of outsourcing tools in business activities, such as using external companies for the distribution of goods, influences the increase in the competitiveness of companies in the online market through the rapid delivery of products. The use of external logistics operators increases the capacity of companies and helps them strengthen their competitive position in the market. Design/methodology/approach: The objective of the paper was achieved through the theoretical and empirical analysis of the research presented. The paper presents the results of research into online sales behaviour and identifies the advantages and disadvantages of outsourcing. The optimal solution to satisfy customer needs is the use of outsourcing, which is characterised by the high quality of services offered by external entities supporting the operation of companies. Findings: Nowadays, an organisation's success factor is increasingly the ability to manage relationships with customers and suppliers, which refers to both the level of customer service and cooperation with contractors. Many companies, including those operating online, are considering using special logistics outsourcing, the aim of which is to reduce costs and limit unnecessary investments. For example, using courier companies is a good practice of modern companies. This means they do not need to have their own transport, which often generates considerable costs. Research limitations/implications: The topic discussed in the paper is very important in the modern context due to the significant development of online sales. To meet public expectations, cooperation with suppliers is a must. Companies, by signing contracts with external operators, can ensure the quick delivery of shipments without the need to have their own transport. During the research, certain limitations were observed in accessing information, both domestic and international, which could have facilitated a more thorough analysis. Another limitation is finding a larger group of respondents willing to participate in the research. Despite these limitations, the author plans to continue research in this area. Practical implications: Outsourcing is currently used in most industries, regardless of their specifics, which gives this tool a universal character. Companies deciding to use outsourcing should be aware that its implementation requires the execution of many complex actions and detailed analyses, often preceded by significant changes in the organisation's operations. Nowadays, cooperation with contractors is one of the most important factors determining the success of an organisation. Social implications: The paper raises public awareness regarding the use of outsourcing in logistics. The described issues enable the audience to seek new solutions in the activities of various industries. Despite the widespread use of outsourcing, many relationships based on this type of cooperation do not end in success. Originality/value: The added value of the article is to increase the efficiency of companies through the introduction of outsourcing. The issues presented make it possible to increase competitive advantage in a dynamic market. Many companies are considering the use of special logistics outsourcing to reduce costs and unnecessary investments. [ABSTRACT FROM AUTHOR]

The primary purpose of this article is to examine the issue of estimating the finite population distribution function from auxiliary information, such as population mean and rank of the auxiliary variables, that are already known. In order to better estimate the distribution function (DF) of a finite population, two improved estimators are developed. The bias and mean squared error of the suggested and existing estimators are derived up to the first order of approximation. To improve the efficiency of an estimators, we compare the suggested estimators with existing counterpart. Based on the numerical outcomes, it is to be noted that the suggested classes of estimators perform well using six actual data sets. The strength and generalization of the suggested estimators are also verified using a simulation analysis. Based on the result of actual data sets and a simulation study, we observe that the suggested estimator outperforms as compared to all existing estimators which is compared in this study. [ABSTRACT FROM AUTHOR]

In 2001, I experienced severe radial neuropathy, leading to permanent dysfunctions in the fingers of my left hand. In this personal account of nerve damage, medical and surgical treatment, and adaptation, I first describe the sequence of neuropathies, then turn to how through serendipity, the brace enabled other connections—nervelines—with individuals and places. The brace is a metonym of a severed nerve with its associated loss of movement and capacity; it is also both a functional orthotic and an affordance. It carries me forward, activating other nervelines and networks to make things happen. I write of literal and metaphoric nerves, evolving connectivities, and fiber lines, thought lines and collaborations. I then extend the idea of nervelines and networks to global connnections; I emphasize how the organization of labor, the distribution of goods and services, and the expectations of workers shape body histories and life outcomes. [ABSTRACT FROM AUTHOR]

The growth of cities is very fast and is often very difficult for urban development. Pedestrian dynamics in this context are very important when designing a facility in terms of the safety, economics, and level of service to its users. The present study focuses on analysing the effect of density and width of a bottleneck on the behaviour of a crowd under emergency conditions. The inclusion of a buffer space for the dispersion of a crowd through a bottleneck is analysed. A relationship is established between total times, the flow and, a specific flow versus the width of a bottleneck. The formation of a dynamic layer was observed during capacity conditions due to the zipper effect. Among the five types of distributions, a semi-random distribution was the better fit for the headway data observed. It was observed that capacity increases in a stepwise manner as the width of a bottleneck increases, which can be attributed to the formation of dynamic lanes. The distance between these lanes is independent of the width of the bottleneck. [ABSTRACT FROM AUTHOR]

The mainstream compartmental models require stochastic parameterization to estimate the transmission parameters between compartments, whose calculation depend upon detailed statistics on epidemiological characteristics, which are expensive, economically and resource-wise, to collect. In addition, infectious diseases spread in three dimensions: temporal, spatial, and mobile, i.e., they affect a population through not only the time progression of infection, but also the geographic distribution and physical mobility of the population. However, the parameterization process for the mainstream compartmental models does not effectively capture the spatial and mobile dimensions. As an alternative, deep learning techniques are utilized in estimating these stochastic parameters with greatly reduced dependency on data particularity and with a built-in temporal–spatial–mobile process that models the geographic distribution and physical mobility of the population. In particular, we apply DNN (Deep Neural Network) and LSTM (Long-Short Term Memory) techniques to estimate the transmission parameters in a customized compartmental model, then feed the estimated transmission parameters to the compartmental model to predict the development of the Omicron epidemic in China over the 28 days for the period between 4 June and 1 July 2022. The average levels of predication accuracy of the model are 98% and 92% for the number of infections and deaths, respectively. We establish that deep learning techniques provide an alternative to the prevalent compartmental modes and demonstrate the efficacy and potential of applying deep learning methodologies in predicting the dynamics of infectious diseases. [ABSTRACT FROM AUTHOR]

In real-life data, count data are considered more significant in different fields. In this article, a new form of the one-parameter discrete linear-exponential distribution is derived based on the survival function as a discretization technique. An extensive study of this distribution is conducted under its new form, including characteristic functions and statistical properties. It is shown that this distribution is appropriate for modeling over-dispersed count data. Moreover, its probability mass function is right-skewed with different shapes. The unknown model parameter is estimated using the maximum likelihood method, with more attention given to Bayesian estimation methods. The Bayesian estimator is computed based on three different loss functions: a square error loss function, a linear exponential loss function, and a generalized entropy loss function. The simulation study is implemented to examine the distribution's behavior and compare the classical and Bayesian estimation methods, which indicated that the Bayesian method under the generalized entropy loss function with positive weight is the best for all sample sizes with the minimum mean squared errors. Finally, the discrete linear-exponential distribution proves its efficiency in fitting discrete physical and medical lifetime count data in real-life against other related distributions. [ABSTRACT FROM AUTHOR]

This paper presents a new variational phase-field model aimed at investigating the fully coupled hydro-mechanical-damage processes in saturated quasi-brittle rocks. Biot's poroelasticity theory is incorporated with a damage-dependent permeability to describe both Darcy-type seepage in the solid matrix and Poiseuille-type flows in the cracked domain. The fluid pressure is introduced into a unified phase-field framework modified with a mixed-mode crack driving force. This framework can predict and reproduce complex crack initiation and propagation while explicitly determining the apparent crack width. First, the formulations of the proposed model are given. Then the fully coupled system is implemented using the finite element method and an alternate minimization Newton–Raphson iterative solver, facilitated by user subroutines provided by the software ABAQUS. The proposed model is validated by three benchmark problems. Subsequently, fracture propagation, pressure diffusion, and mechanical behavior are simulated and discussed in homogenous and heterogeneous quasi-brittle rock samples with pre-existing flaws under uniaxial compression, respectively. Finally, the proposed model is applied to simulate the damage evolution and investigate the fluid pressure distribution in jointed rock structures. The practicality and computational efficiency of the proposed model in accurately simulating realistic crack propagation and fluid pressure distribution in rock engineering problems are demonstrated. Highlights: A new variational phase-field model for predicting hydro-mechanical coupled fracture propagation is developed. A modified mixed-mode crack driving force is well incorporated in the proposed model. The three-field coupled system is efficiently solved by the implementation of a dual-layer numerical structure in ABAQUS. The model is well validated and then applied to simulate crack propagation in rock samples and jointed rock structures. [ABSTRACT FROM AUTHOR]

Bridges are fundamental links for the movement of goods and people and bridge damage can thus have significant impacts on society and the economy. Earthquakes can be extremely destructive and can compromise bridge functionality, which is essential for communities. Evaluation of bridge functionality is thus fundamental in the planning of emergency responses and socioeconomic recovery procedures. It is especially useful to define parameters to assess investments in bridge infrastructure. Resilience is a key parameter that can identify decision making procedures necessary for recovery investments. In this regard, resilience can be defined as the rapidity of a system to return to pre-disaster levels of functionality. This aim of this work was to assess the lack of robust analytical procedures for quantifying systematic restoration for earthquake-damaged bridges, to provide a link between the assessment of resilience and its application in decision making approaches. The proposed methodology (called seismic resilience for recovery investments of bridges) uses functionality–time curves that allow quantification of resilience along with readable findings for a wider range of stakeholders. The results presented in this paper should be of interest to multi-sectorial actors (i.e. bridge owners, transportation authorities and public administrators) and could drive interdisciplinary applications such as the assessment of recovery techniques and solutions. [ABSTRACT FROM AUTHOR]

We establish a novel family of Kumaraswamy-X probability distributions in the present investigation. We discussed the KumaraswamyExponential univariate probability distribution. The new distribution with three parameters possesses density function with unimodal and reverse J-shape and hazard rate function of bathtub shaped. We study various statistical properties for it and derive the expressions for its density function, distribution function, survival and hazard rate function, Probability weighted Moments, lth moment, moment generating function, quantile function and Shannon entropy. For the derived distribution order statistics is also discussed. The parameters are estimated using the maximum likelihood estimation approach, and the performance of the estimators was evaluated using a Monte Carlo simulation. Through extensive Monte Carlo simulations and comparative analyses, we assess the performance of the Kumaraswamy-X distribution against other common probability distributions used in engineering contexts. When we apply it to real datasets, it offers a more suitable fit than other existing distributions. We explore the characteristics and potential applications of the Kumaraswamy-X distribution in the context of engineering problems through a comprehensive simulation-based investigation. [ABSTRACT FROM AUTHOR]

Introduction. Studies on physical activity among adults are rarely conducted. The main reason is the limited availability of study groups, as employers do not always grant permission for their employees to participate in research. The aim of this study was to assess the self-reported level of physical activity among car dealership employees using the IPAQ questionnaire. Material and Methods. The study involved 32 car dealership employees aged 18-50 years. Physical activity was assessed using the short version of the IPAQ questionnaire. Based on the self-reported data, employees were classified into one of four categories of physical activity. The correlation between time spent sitting and the age of the participants versus their weekly physical activity, due to the lack of significant differences between men and women, was presented collectively for all participants. The distribution of weekly physical activity was strongly skewed to the right, so statistical calculations were performed on logarithmic values. The Student's t-test for independent samples was used for the calculations. Results. No statistically significant differences were found in the level of physical activity between men and women after they were classified into one of four categories of physical activity. Weekly physical activity of car dealership employees was negatively correlated with time spent sitting and the age of the participants. Conclusions. The physical activity assessment of employees at the car dealership indicate a trend towards higher average values of calculated weekly physical activity for men compared to women. No statistically significant differences were found in the level of physical activity between women and men after classifying them into one of four physical activity categories; 60% of women and 68% of men were highly physically active. [ABSTRACT FROM AUTHOR]

Smuggling of goods is a complex and multifaceted phenomenon that has a significant impact on the economic and social situation in the country. The illegal movement of goods across the border not only undermines economic stability, but also poses a threat to national security, contributing to the development of the shadow economy and corruption. In the conditions of globalization and the growing integration of international markets, the issue of combating smuggling becomes especially urgent, because this phenomenon is becoming more and more large-scale and difficult for state control. Criminalization of the smuggling of goods is a key mechanism for countering this negative phenomenon, which involves the establishment of stricter liability for violations of customs rules. It should be noted that the effectiveness of the criminalization of customs offenses depends on a number of factors, including the presence of effective legislation capable of adequately responding to new challenges, the effectiveness of law enforcement agencies, as well as the level of international cooperation in this area, etc. However, given the specifics of the Ukrainian economy and the country's geopolitical location, the smuggling of goods remains a serious problem that requires a deeper analysis and an effective solution. One of the key aspects of the fight against the smuggling of goods, which became one of the main reasons for its criminalization, is its impact on filling the state budget. Illegal trade in goods deprives the state of significant financial revenues that could be directed to the development of social programs, infrastructure projects and ensuring economic growth. The loss of these resources weakens the country's economic potential, undermines trust in state institutions, and contributes to growing social tensions. The article examines the impact of smuggling on the state's economy, emphasizing the negative consequences for filling the state budget. Examples of significant economic losses incurred by the state due to smuggling are considered, as well as the role of customs authorities in combating this phenomenon is highlighted. A set of measuresis proposed to reduce the level of smuggling, including changes to legislation, increasing the effectiveness of customs control and strengthening international cooperation. [ABSTRACT FROM AUTHOR]

Distribution centres play a key role in the supply chain and their insufficient number will entail supply chain disruptions--supply delays and problems, as well as lost profits. Currently, distribution centres in Kazakhstan are located only in large cities. However, the growth of e-commerce (from 11729.9 billion tenge to 15763.7 billion tenge in 2022 compared to 2020) determines the need to solve this problem. Our study establishes the dependence between the areas of distribution centres and the e-commerce volume. This analysis, unlike the existing ones, examines the effect of the growth of e-commerce volumes on the increasing number of distribution centres as a prerequisite for the development of the logistics infrastructure of Kazakhstan against the background of the increase in the volume of goods supplied from China in transit to Europe. The aim of the study was to analyse the role of distribution centres in the logistics infrastructure of Kazakhstan. We justify the need to increase the number of distribution centres due to the increase in both turnover in goods transit, wholesale, and retail trade. The introduction of distribution centres ensures the continuous movement of goods and strengthens the connection between suppliers and consumers. The emergence of new technologies and innovations, combined with favourable market conditions and political support, creates favourable conditions for the development of reliable distribution centres that can improve logistics efficiency and contribute to the overall economic development of Kazakhstan. [ABSTRACT FROM AUTHOR]

Self-operated and third-party products in platform retailing can be delivered by self-operated or third-party physical distribution. However, how choosing the best logistics service mode represents a difficult problem for online platforms and sellers in practice. This paper analyzes the strategic logistics service mode choices for retail sales on an online platform. Equilibrium strategies are derived by solving game models considering three logistics service modes: self-operated, third-party and agency. The results show that the platform has an incentive to provide self-operated physical distribution to the seller when the distribution commission exceeds a threshold. More specifically, it achieves a win–win situation with the seller in the self-operated logistics service mode when the distribution commission falls within a moderate range. The platform is unwilling to provide any logistics services with a sufficient low distribution commission. Except in the case of high distribution commission and low self-operated distribution quality, the seller would prefer to accept the platform's self-operated physical distribution. In addition, the third-party physical distribution company generally prefers the agency logistics service mode unless the self-operated distribution quality and commission are high. [ABSTRACT FROM AUTHOR]

Over the past two decades, the surge in warehouse construction near seaports and in economically lower‐cost land areas has intensified product transportation and e‐commerce activities, particularly affecting air quality and health in nearby socially disadvantaged communities. This study, spanning from 2000 to 2019 in Southern California, investigated the relationship between ambient concentrations of PM2.5 and elemental carbon (EC) and the proliferation of warehouses. Utilizing satellite‐driven estimates of annual mean ambient pollution levels at the ZIP code level and linear mixed effect models, positive associations were found between warehouse characteristics such as rentable building area (RBA), number of loading docks (LD), and parking spaces (PS), and increases in PM2.5 and EC concentrations. After adjusting for demographic covariates, an Interquartile Range increase of the RBA, LD, and PS were associated with a 0.16 μg/m³ (95% CI = [0.13, 0.19], p < 0.001), 0.10 μg/m³ (95% CI = [0.08, 0.12], p < 0.001), and 0.21 μg/m³ (95% CI = [0.18, 0.24], p < 0.001) increase in PM2.5, respectively. For EC concentrations, an IQR increase of RBA, LD, and PS were each associated with a 0.021 μg/m³ (95% CI = [0.019, 0.024], p < 0.001), 0.014 μg/m³ (95% CI = [0.012, 0.015], p < 0.001), and 0.021 μg/m³ (95% CI = [0.019, 0.024], p < 0.001) increase. The study also highlighted that disadvantaged populations, including racial/ethnic minorities, individuals with lower education levels, and lower‐income earners, were disproportionately affected by higher pollution levels. Plain Language Summary: Over the past 20 years, more warehouses have been built near ports and in areas where land is cheaper. This has increased truck traffic and goods movement, which has worsened air quality and affected the health of nearby communities that often lack resources and health services. From 2000 to 2019, our study in Southern California examined how this rise in warehouses has impacted air pollution, focusing on very small pollution particles known as PM2.5 and a harmful component of these particles called elemental carbon. Using satellite data to analyze pollution levels across different areas, we discovered that larger warehouses, more loading docks, and increased parking spaces are associated with higher levels of pollution. We also found that this rise in pollution particularly affects disadvantaged groups in these communities, including racial/ethnic minorities, those with less education, and those with lower incomes. This research underscores the long‐term trend of warehouse expansion and its effects on air pollution. It highlights the urgent need for careful planning in warehouse construction and better protection for vulnerable communities, particularly those most at risk from increased pollution. Key Points: Warehouse expansion over the last two decades was associated with elevated PM2.5 and elemental carbon concentrations in their ZIP code regionsDisadvantaged populations living near warehouses are disproportionately exposed to higher levels of air pollutionTargeted emission control interventions and protective measures are especially needed for vulnerable populations near warehouses [ABSTRACT FROM AUTHOR]

The floral supply chain is one of the most delicate logistics networks in existence, requiring intensive monitoring of temperature, humidity, and product conditions to ensure the quality of flowers. However, the traditional centralized supply chain involves exchanging information back and forth, and therefore wastes time, money, and resources. In addition, centralized data control without security verification exposes logistics information to the risk of hacking, information tampering, and counterfeiting. Blockchain technology offers decentralization, immutability, and encryption security. Furthermore, application of Internet of Things (IoT) technology has made it easier to monitor goods and the distribution process. Combining the above two new technologies can effectively improve the transparency and traceability of the floral supply chain and make transactions more secure through the digital signature verification process. In this research, we propose a smart contract for the floral supply chain. Through contract compilation, the proposed contract defines the delivery process status, delivery specifications, and other conditions in the shipping process so that the status of the goods is updated on the blockchain, giving the supply chain higher traceability. This study also defines the process for and conditions of money transactions between buyers and sellers using such smart contracts, thereby automating the transaction, improving its efficiency, and saving on labor costs and paper waste. This research also compares the proposed smart contract to traditional transaction methods. The pros and cons are thoroughly discussed, offering significant benefit to decision makers. [ABSTRACT FROM AUTHOR]