Search

Firms that aim to close the loop via remanufacturing returned products face uncertainties on both the demand and supply side. Inspired by industrial circular business models, we study the capacity investment problem of a manufacturer. The manufacturer can invest in a flexible (shared) resource to share capacity across processes and/or less costly dedicated manufacturing and remanufacturing resources. We model the capacity investment problem as a two-stage stochastic programme and provide structural results. Our analysis shows how the optimal resource selection depends on margin (price) and cost differentials and highlights the focal role of capacity coefficients. We identify conditions under which an investment in flexibility is beneficial even if remanufacturing is a lower-margin process. Moreover, an investment in flexible capacity can be optimal if demand and returns are perfectly positively correlated, and thus, return risk is eliminated. Contrary to intuition, optimal profits may decrease in demand-return correlation if the optimal investment includes a flexible resource. The analysis of investment thresholds shows two benefits of resource flexibility: (a) mitigation of demand and return mismatches and (b) an ex-post revenue maximisation option to allocate capacity to the more profitable process. [ABSTRACT FROM AUTHOR]

In the current era of turbulence, demand uncertainty and significant supply delays have become recurrent challenges for every global supply chain, which are accentuated by geopolitical issues, regional conflicts, natural disasters, etc. Companies are keen to decide when and where nearshoring and offshoring their supply locations and to adapt their inventory control policies to cope with significant perturbations. To deal with these challenges, we investigate the integration of inventory planning at the strategic level through a novel setting of the location-inventory problem under uncertainty. This problem is characterised by inbound multi-sourcing, order splitting among multiple suppliers, and optimised inventory decisions from sourcing to customers under uncertain demand and lead time. A two-stage stochastic optimisation model is proposed and solved using a Benders decomposition algorithm, supported by a sample average approximation approach. The results show that both lead time and demand uncertainties impact strategic and tactical decisions of the global supply network. Specifically, multi-sourcing and inbound order splitting policies have proven beneficial when the network faces a high lead time uncertainty. Based on an illustrative case of a global automobile spare parts supply network under different supply-side scenarios, managerial insights are gained on the benefit of onshore, nearshore, and offshore decisions. When facing a high lead time uncertainty, as in the pandemic case or any geopolitical episode, our findings suggest the necessity to rely on nearshoring for the supply side. [ABSTRACT FROM AUTHOR]

A supply and demand mismatch, or imbalance of the amount of supplies in the market, is always an issue and can happen all the time. Capacity sharing is an effective way to address this problem, and the capacity sharing platform facilitates the optimal matching between multiple capacity buyers and sellers. In the context of Industry 4.0, many industries are adopting intelligent algorithms to assist in decision-making. This paper presents an optimal or near-optimal matching algorithm to cope with a large volume of capacity-sharing problems. The fairness of the matching solution is captured by including three objectives from platform, sellers and buyers. In this paper, a 2-dimensional crossover and an order-first mutation are developed and employed with genetic algorithms (GA), including GA and NSGA-II. Additionally, a novel repair mechanism is proposed by considering various constraints to transform infeasible solutions into feasible ones. Two matching schemes are studied based on whether orders from buyers can be split or not. The results show that both algorithms based on traditional GA and NSGA-II are effective for different schemes. In addition, it is found that GA has better performance in the case of 'more sellers' and NSGA-II shows better performance in the 'more buyers' case. [ABSTRACT FROM AUTHOR]

The paper uses bids submitted by primary dealer banks at auctions of sovereign bonds to quantify the price elasticity of demand. The price elasticity of demand correlates strongly with the volatility of returns of the same bonds traded in the secondary market but only weakly with their bid-ask spread. It predicts same-bond post-auction returns in the secondary market, even after controlling for pre-auction volatility. The evidence suggests that the price elasticity of demand is associated with the magnitude of price pressure in the secondary market around auction days and proxies for primary dealer risk-bearing capacity. [ABSTRACT FROM AUTHOR]

Production and service systems often rely on stationary queueing formulas to determine the number of agents that need to be scheduled. However, this results in over-staffing decisions when the system starts empty. To address this issue, we propose a solution called transient staffing, which involves providing appointment times for agents to ensure that the expected queue length remains below a certain threshold. We model the system as a transient Erlang-A queue. To compute the appointment times, we employ a uniformized approximation that discretizes the elapsed time, which can be made as accurate as desired. We prove that the appointment times decrease with the arrival rate and traffic intensity, while increasing with the abandonment rate. Through numerical investigations, we find that adopting a transient staffing strategy leads to substantial cost savings, especially in scenarios where demand is high, customers are patient, traffic intensity is low, and the service level objective is intermediate. Furthermore, we use a fluid approximation to derive closed-form expressions for the agents' appointment times, and prove the impact of the system parameters. We show that this approximation performs well in predicting the first appointment times in contexts with high demand, low traffic intensity, and lax service level objective. [ABSTRACT FROM AUTHOR]

Graphene/stainless steel (SUS) structures have received attention as an approach to enhancing the performance of SUS in various applications, such as energy storage and electrochemical devices. Despite many studies, the synthesis of graphene nanostructures with controllable growth orientation on SUS remains challenging. The present work demonstrates the selective synthesis of monolayer to bilayer graphene and vertical graphene (i.e., carbon nanowalls) on SUS by plasma enhanced chemical vapor deposition and also explains the associated growth mechanisms. This study indicates that the graphene nucleation density can be tuned by varying the growth temperature and CH4/H2 ratio during synthesis. It is also evident that graphene growth occurs within a mixed phase of γ-Fe and Fe3C at high temperatures, and a high carbon supply of above 900 °C triggers the transition of growth orientation from planar to vertical. [ABSTRACT FROM AUTHOR]

Yes. We show that aggregate stock returns predict aggregate U.S. employment, despite the industrial composition of publicly traded firms differing markedly from that of all firms, and the representativeness of public firms declining over time. We also show that appropriately reweighted stock returns predict industry and local labor market outcomes. We find the strongest evidence of an alignment of interests between shareholders and workers in the manufacturing sector, despite its declining labor share of output. Our findings suggest that at quarterly frequencies, product demand shocks are more important drivers of industry- and city-level stock returns than technology shocks. [ABSTRACT FROM AUTHOR]

Cybersecurity presents non-negligible challenges for firm collaboration and supply chain viability, as information exchange among nodes introduces potential interdependent risks. How to make appropriate decisions on security investments and information exchange modes is a significant issue for supply chain members. Considering two information exchange modes: system interconnection and system independence, this study develops two game models to investigate the cybersecurity investments in a vertical supply chain composed of a retailer and n suppliers. Initial analysis shows that although firms learn investment decisions mutually in the face of a changing cybersecurity environment, suppliers always take a free ride on the efforts of retailer in two cases and the increased interdependent risks will damp nodes enthusiasm for security investments. Next, to compare the two cases, we introduce information exchange efficiency as the mediate parameter to link degree of system interconnection and proportion of information shared. We found that under the system independence mode, firms with high information-exchanging demand in the large-scale supply chain are more motivated to invest in cybersecurity. Furthermore, we extend our models to a centralised decision-making scenario. We find that security investment efficiency is greatly improved, and the free-riding behaviour of supplier is significantly reduced when systems are interconnected. [ABSTRACT FROM AUTHOR]

Matching the supply of medical equipment with soaring demand caused by an epidemic event is a daunting challenge. Focusing on medical equipment manufacturers, this study empirically examines how supply chain management impacts manufacturers' responses to epidemic-driven demand. This study employs novel supply chain data from South Korea collected before and after the MERS (Middle East Respiratory Syndrome) epidemic event. In the results, we show that such an epidemic positively influences the sales performance of medical equipment manufacturers (i.e. a 10.2 percentage-point increase). To identify the mechanism behind this result, we provide evidence that increasing the supply base (i.e. the number of supply channels) enables medical equipment manufacturers to better respond to epidemic-driven demand and ultimately improves their sales performance. More interestingly, we find that medical equipment manufacturers with lower market status (i.e. lower market shares and lower supplier shares) expand their supply bases more extensively than other medical equipment manufacturers with higher market status do. Interpreting these results, we offer managerial insights into how medical equipment manufacturers can effectively manage their supply chains and improve their performance when responding to epidemic-driven demand. [ABSTRACT FROM AUTHOR]

I show that cash distributions through cash mergers, dividend payments, and stock buybacks are, in principle, similar to investor fund flows in generating demand for investable assets. Abnormal returns on certain assets can be forecasted because delegated investors predictably reinvest cash returns toward certain holdings. Novel measures of stock-level demand constructed using proportional reinvestments by mutual funds predict abnormal returns and issuances in noncash-paying stocks. These results highlight an alternative and substantial source of price fluctuations in the cross section of equities. [ABSTRACT FROM AUTHOR]

Exploiting demand shocks from changes in federal government spending, we examine how the organizational structure of a firm affects its investment behavior. Government spending shocks affect the investment of government-dependent conglomerate segments less than matched stand-alone firms. Investment also increases in lower government-dependent segments when other segments within the same firm experience positive demand shocks, indicating cross-subsidization between segments. We further show that this cross-subsidization leads to worse operating performance and increases the diversification discount. Our findings are robust after addressing the endogeneity of government spending. [ABSTRACT FROM AUTHOR]

Supply chains are widely studied as carriers of value, but their potential to create value remains underappreciated. This study conceptualizes supply chain orchestration as a business model that allows a firm to create value in a supply chain with high market uncertainty by matching demand and supply more effectively than the present norm, and without relying on product innovation. Building on contingency theory, this study theorizes supply chain orchestration and articulates three defining qualities of its activity system. Next, it describes the evolution of this business model: its creation requires architectural market knowledge; however, sustained belief in the architectural market knowledge, ironically, can hinder its adaptation to changes in the firm's environment and lead to its abandonment. The study, I hope, will promote management practice and research at the largely untapped intersection of business models and strategic supply chain management to foster novel value creation by orchestrating supply chains. [ABSTRACT FROM AUTHOR]

Manufacturers experience random environmental fluctuations that influence their supply and demand processes directly. To cope with these environmental fluctuations, they typically utilise operational hedging strategies in terms of pricing, manufacturing, and procurement decisions. We focus on this challenging problem by proposing an analytical model. Specifically, we study an integrated problem of procurement, manufacturing, and pricing strategies for a continuous-review make-to-stock system operating in a randomly fluctuating environment with exponentially distributed processing times. The environmental changes are driven by a continuous-time discrete state-space Markov chain, and they directly affect the system's procurement price, raw material flow rate, and price-sensitive demand rate. We formulate the system as an infinite-horizon Markov decision process with a long-run average profit criterion and show that the optimal procurement and manufacturing strategies are of state-dependent threshold policies. Besides that, we provide several analytical results on the optimal pricing strategies. We introduce a linear programming formulation to numerically obtain the system's optimal decisions. We, particularly, investigate how production rate, holding cost, procurement price and demand variabilities, customers' price sensitivity, and interaction between supply and demand processes affect the system's performance measures through an extensive numerical study. Furthermore, our numerical results demonstrate the potential benefits of using dynamic pricing compared to that of static pricing. In particular, the profit enhancement being achieved with dynamic pricing can reach up to 15%, depending on the problem parameters. [ABSTRACT FROM AUTHOR]

This paper proposes an auction-based market trade mechanism for the electric vehicle battery recycling (EVBR) problem, which aims to realise the optimal resource allocation and pricing of EVBR. The main motivation of this paper is to attempt to explore an approach to achieving efficient battery recycling. We first consider an EVBR market with m buyers and n sellers, and develop the multi-unit trade reduction (MTR) mechanism in the EVBR market. According to the supply and demand relationship in the EVBR market, we consider three market scenarios of supply and demand balance, oversupply, and overdemand, and formulated corresponding auction allocation rules. Numerical study results show that the proposed MTR mechanism can achieve efficient resource allocation. We also observed that not all results increased with the number of sellers/buyers. Second, considering the distance between sellers and buyers, we developed a stochastic multiple MTR (SM-MTR) mechanism to enable sellers and buyers within the region to conduct transactions. Finally, we propose an integrated MTR, SM-MTR and one-sided Vickrey--Clarke--Groves auction mechanism that is feasible in both one-sided and bilateral environments. Furthermore, our work can provide novel managerial implications for EVBR market stakeholders in terms of practical application. [ABSTRACT FROM AUTHOR]

Motivated by the COVID-19 pandemic, this paper explores the supply chain viability of medical equipment, an industry whose supply chain was put under a crucial test during the pandemic. This paper includes an empirical network-level analysis of supplier reachability under Random Failure Experiments (RFE) and Intelligent Attack Experiments (IAE). Specifically, this study investigates the effect of RFE and IAE across multiple tiers and scales. The global supply chain data was mined and analysed from about 45,000 firms with about 115,000 intertwined relationships spanning across 10 tiers of the backward supply chain of medical equipment. This complex supply chain network was analysed at four scales, namely: firm, country-industry, industry, and country. A notable contribution of this study is the application of a supply chain tier optimisation tool to identify the lowest tier of the supply chain that can provide adequate resolution for the study of the supply chain pattern. We also developed data-driven-tools to identify the thresholds for breakdown and fragmentation of the medical equipment supply chain when faced with random failures or different intelligent attack scenarios. The novel network analysis tools utilised in the study can be applied to the study of supply chain reachability and viability in other industries. [ABSTRACT FROM AUTHOR]

After exogenous demand shocks caused by natural disasters, FinTech lenders are more responsive to increased demand for reconstruction mortgages than traditional banks and non-FinTech shadow banks. Both FinTech and traditional banks increase credit supply, but FinTech supply is more elastic without increases in risk-adjusted interest rates or delinquency rates. Comparing lending supply channels, banks respond to regulatory incentives to lend to damaged areas, whereas FinTech lenders supply more credit when traditional banks rely more on balance sheet financing and physical branch networks. Compared to traditional banks, FinTech lenders increase supply elasticity more aggressively in response to local competitive pressure. [ABSTRACT FROM AUTHOR]

The development of society has put forward higher requirements for the supply of maintenance spare parts. However, the current support index is not comprehensive enough to evaluate the system ability. Meanwhile, the influence of horizontal supply time on spare parts storage is not fully reflected. Therefore, the problem of spare parts storage configuration in two-echelon maintenance and supply support systems is analysed in the paper. First, the state transition rates of different parts are studied under the context. Then a system-level comprehensive support rate based on importance measure is proposed. To determine the number of different spare parts, an optimisation model is established to maximise system support efficiency. Furthermore, a site importance measure based on horizontal supply time is proposed to measure the site priority over spare parts storage. Next, a two-stage decision method is presented for determining the location of spare parts. Finally, the feasibility of the proposed method is verified with the help of an example for the aircraft maintenance and supply support system. [ABSTRACT FROM AUTHOR]

We construct text-based measures of the primary concerns listed firms associated with the spread of COVID-19 and other epidemic diseases. We identify which firms perceive to lose or gain from a given epidemic and textually decompose the epidemic's effect on the firm's demand and supply. We find that the effects of COVID-19 manifest as a simultaneous shock to demand and supply, with both shocks affecting firms' market valuations in equal measure on average. By contrast, demand-related impacts appear more important in accounting for the observed collapse in firm-level investment during the COVID-19 crisis. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online [ABSTRACT FROM AUTHOR]

Based on the carbon emission policy and low-carbon capacity sharing, this paper studies the optimal product pricing and capacity matching strategies of competition and cooperation between two companies in a duopoly market. And the optimal strategies with or without capacity sharing are discussed. In addition, a differential game model based on carbon emission constraints, low-carbon capacity sharing, and delayed decision-making is established. We also study the evolution of duopoly enterprises from the initial state to the equilibrium state. Besides, the influence of delayed decision variables, price adjustment speed and decision weights on the stability of the game system is analyzed. The results show that the government can effectively intervene and guide the enterprises' low-carbon production strategy through carbon emission restriction policy. Low-carbon capacity sharing is beneficial to both enterprises, and the demand side benefits more than the supply side. We discuss the impact of stable and unstable systems on enterprises' decisions. When the system is stable, enterprises can converge from different initial game states to equilibrium states. The adoption of a delay strategy will not affect the tendency of convergence to the equilibrium. Furthermore, the system using the variable feedback control method is of higher stability. [ABSTRACT FROM AUTHOR]

Carbon neutrality and resilience are two of important goals pursued by supply chains today. This study seeks to determine how carbon footprint reduction (CFR) can be achieved in a resilient supply chain from a process integration perspective. Based on contingency theory, we employed the decision-making trial and evaluation laboratory (DEMATEL) approach to examine the critical influencing factors of process integration using evaluations from academic and professional experts. Our findings reveal that, first, at the strategic level, supply chain collaboration (SCC) and supply chain agility and responsiveness (SCAR) have the highest contributions to process integration. Second, process and control risks have the highest contributions in supply chain risk management; herein, collaborative planning, forecasting, and replenishment (CPFR) holds the highest contribution in the SCC, visibility and velocity have the highest contributions in the SCAR, and supply chain efficiency holds the highest contribution in supply chain flexibility (SCF). Finally, CPFR has a significant influence on both supplier and customer engagement, with supply chain intelligence influencing customer engagement in the SCC. Both the standardisation and parallel processes influence supplier spending in the SCF. These findings provide a new conceptual framework for empirical research and an action plan for supply chains to pursue carbon neutrality. [ABSTRACT FROM AUTHOR]

Using a standardized methodology, we empirically evaluate 55 proposed determinants of capital structure in terms of statistical significance, economic significance, and identification. We find that robust and economically important determinants of debt ratios are relatively few in number. Nevertheless, because each determinant relates to one of five market imperfections—taxes, distress costs, information asymmetry, agency costs, or supply frictions—we draw conclusions from the evidence as a whole regarding the explanatory power of different capital structure theories. We find greater support for pecking order theory and supply-related theories, with less support for traditional tradeoff theory and agency theory. [ABSTRACT FROM AUTHOR]

We study the impact of the refinancing channel of monetary policy on very small and medium-sized businesses. Using data covering the near universe of these businesses, we find that increased household refinancing reduces the probability that a business exits or exhausts its debt capacity in the calendar year, as well as 6 years after the first exposure. It also helps younger businesses maintain credit relationships. Financial factors, like business liquidity, as well as local demand dependence, amplify these effects, especially for very small businesses. These results suggest that the refinancing channel of monetary policy can have large long-run effects on local economies. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

ABSTRACT Through a systematic literature review of 42 recent peer‐reviewed empirical studies of international arms markets, this paper examines the determinants of supply and demand for manned fixed‐wing combat aircraft in the post–Cold War era. This literature review distinguishes more than 80 different determinants related to either the international system interaction, an international state or the aircraft offered. The determinants can be related to the main characteristics of the market, namely, security, economics and normative values. Measurable effects are only visible for security and economic determinants. Normative factors appear to play a rudimentary role across the board, although they can be found to be significant in individual studies. One conclusion that can be drawn is that the most commonly studied determinants are not those with the greatest impact. In addition, it is important to note that all but a few of the publications analysed do not address the difference in market power between a producing and a supplying (reselling) state. [ABSTRACT FROM AUTHOR]

Grandparents are an important source of childcare worldwide, but international patterns vary. We examine how demographic characteristics of parents, and of grandparents, factor into grandparent care provision considering the cultural assumptions and policy settings Australian families live within. Using the Household Income and Labour Dynamics in Australia survey, we identify determinants of both the demand for and the supply of grandparent childcare in Australia (4266 grandparents and 9822 parents). Results suggest that grandmothers and mothers, as much or more than fathers and mothers, balance their reciprocal participation in employment and childcare. University‐educated grandmothers are more likely to provide regular childcare (at least once a week) and university‐educated mothers are more likely to draw upon it, inconsistent with research in other countries. It appears grandparents are stepping in as both “mother savers” and “system savers,” suggesting a need for more public policy support for Australian working mothers to capitalise on their increasingly high educational attainment. [ABSTRACT FROM AUTHOR]

ABSTRACT This article examines the puzzle of why China's two policy banks, China Development Bank (CDB) and the Export‐Import Bank of China (Eximbank), have lending portfolios for power‐generation projects in Africa that have drastically different levels of carbon dioxide emissions. From the supplier side, Eximbank balances two imperatives: Beijing's ideational ambition as a new development provider to African recipients with sustainability commitments, and China's industrial goal to offshore non‐renewable capacity. In contrast, the CDB prioritizes its commercial interests, which results in the bank lending solely for coal projects. On the demand side, Eximbank's concessional capital has emerged as a second‐best option among international financial sources for renewable and hydropower generation projects. Conversely, CDB's market‐rate lending makes it the fiscal last resort for host countries seeking financial support for thermal‐power projects which are shunned by other financiers. This divergence can be understood through the polycentric development finance model, which captures the parallel decision‐making institutions governing Chinese energy financing in Africa. Specifically, the lending decisions of Eximbank are linked with institutionalized policy processes, translating priorities of Chinese and African state actors. Meanwhile, the loan origination processes of CDB are more independent of state actors, allowing greater autonomy for the financier to pursue commercial interests. [ABSTRACT FROM AUTHOR]

In recent years, ride-hailing has played an important role in daily transportation. In the ride-hailing system, how to set the prices for orders is a crucial issue. Considering the variations in vehicle supply and order demand in different regions of the same city, it is necessary to divide regions according to the supply and demand in each region and develop differentiate pricing strategy. However, the traditional fixed region-division strategy is difficult to adapt to the dynamically changed supply and demand over the time. To address this issue, we propose a dynamic region-division based pricing strategy to set prices according to the demand and supply of different regions to maximize the long-term profit of the platform. Specifically, we first design a dynamic region-clustering algorithm based on Deep Q Network and K-Means algorithm to dynamically cluster small zones with similar demand and supply status and nearby locations into the same region. We then propose an adaptive multi-region dynamic pricing algorithm to set unit price for each clustered region to maximize the long-term profit of the ride-hailing platform. We further run extensive experiments based on a real-world dataset to demonstrate the effectiveness of our proposed algorithm. The experimental results show that the platform's profit is increased under different pricing algorithms combined with dynamic region-clustering algorithm. Furthermore, we find that the combination of adaptive multi-region dynamic pricing algorithm with dynamic region-clustering algorithm can bring higher profits, serve more orders and have a higher service rate than benchmark approaches. [ABSTRACT FROM AUTHOR]

Purpose: Effective knowledge management has played a crucial role in propelling the green transformation of organisations and industries. Nevertheless, its underutilisation in the real estate sector has impeded the progress of green transformation. Therefore, the purpose of this paper is to offer a theoretical and practical analysis of the green transformation of the real estate industry through the lens of knowledge management and to provide a valuable reference to facilitate the industry's green transformation. Design/methodology/approach: This study entailed applying induction and deduction method, using China's real estate industry as a typical case, and collecting and analysing the public data, corporate reports and literature of China's real estate industry. On this basis, the authors conducted an in-depth analysis of the mechanisms through which the green transformation has empowered the sustainable development of China's real estate industry, as well as the critical role of knowledge management. Findings: The study has revealed that the primary challenges encountered by China's real estate industry during the green transformation have stemmed from significant disparities in the knowledge base among different industry entities, the complexities related to knowledge integration and the "difficulty" of applying green knowledge across the entire life cycle. To address these issues, the authors recommend several strategic actions, including creating a dedicated green knowledge platform for the real estate industry, establishing a knowledge-sharing mechanism, enhancing knowledge acquisition on both the supply and demand sides and intensifying the focus on the application of green knowledge within the real estate industry. Research limitations/implications: This research holds considerable theoretical and practical significance concerning the comprehension and promotion of knowledge management's role in the green transformation of China's real estate industry. These insights can be applied to significantly enhance the theoretical framework of knowledge management, and the research outcomes provide substantial support for propelling the green transformation in China's real estate industry and contributing to the sustainable development of the overall economy in China. Originality/value: From a knowledge management perspective, this study introduces a series of solutions and recommendations, presenting new research ideas and pathways for advancing the green transformation of the real estate industry. In addition to guiding the industry's sustainable development, it also significantly contributes to enhancing the theoretical framework of knowledge management. [ABSTRACT FROM AUTHOR]

Background: Accurate leaf water potential (Ψw) determination is crucial in studying plant responses to water deficit. After excision, water potential decreases, even under low evaporative demand conditions, which has been recently attributed to the equilibration of pre-excision Ψw gradients across the leaf. We assessed the influence of potential re-equilibration on water potential determination by monitoring leaf Ψw and relative water content decline after excision using different storage methods. Results: Even though leaf Ψw declined during storage under low evaporative demand conditions, this was strongly reduced when covering the leaf with a hydrophobic layer (vaseline) and explained by changes in relative water content. However, residual water loss was variable between species, possibly related to morpho-physiological leaf traits. Provided water loss was minimized during storage, pre-excision leaf transpiration rate did not affect to the magnitude of leaf Ψw decline after excision, confirming that transpiration-driven Ψw gradients have no effect on leaf Ψw determination. Conclusions: Disequilibrium in water potentials across a transpiring leaf upon excision is dissipated very quickly, well within the elapsed time between excision and pressurization, therefore, not resulting in overestimation of leaf Ψw measured immediately after excision. When leaf storage is required, the effectiveness of a storage under low evaporative demand varied among species. Covering with a hydrophobic layer is an acceptable alternative. [ABSTRACT FROM AUTHOR]

Historically, meteorological drought in the western United States (WUS) has been driven primarily by precipitation deficits. However, our observational analysis shows that, since around 2000, rising surface temperature and the resulting high evaporative demand have contributed more to drought severity (62%) and coverage (66%) over the WUS than precipitation deficit. This increase in evaporative demand during droughts, mostly attributable to anthropogenic warming according to analyses of both observations and climate model simulations, is the main cause of the increased drought severity and coverage. The unprecedented 2020-2022 WUS drought exemplifies this shift in drought drivers, with high evaporative demand accounting for 61% of its severity, compared to 39% from precipitation deficit. Climate model simulations corroborate this shift and project that, under the fossil-fueled development scenario (SSP5-8.5), droughts like the 2020-2022 event will transition from a one-in-more-than-a-thousand-year event in the pre-2022 period to a 1-in-60-year event by the mid-21st century and to a 1-in-6-year event by the late-21st century. [ABSTRACT FROM AUTHOR]

Inter alia due to the high demand for lithium‐ion batteries, the required critical heavy metals have become a scarce resource. Consequently, effective recycling processes that enable the recovery of such materials are of high interest. A silica adsorbent functionalized with amino‐polycarboxylate derivate ligands (HSU331) was applied in order to selectively adsorb nickel(II) and cobalt(II). Subsequently, suitable parameters for a pH‐swing desorption were investigated. Maximum desorption efficiencies for Co(II) were achieved at pH = 2.7 (72 %) and for Ni(II) at pH = −0.5 (94 %). These results were used for the design of a pH‐shift desorption process in order to selectively recover Co(II) and regenerate the adsorbent after simultaneous adsorption of Co(II) and Ni(II). [ABSTRACT FROM AUTHOR]

In order to eliminate the adverse effects caused by power coupling of the isolated three‐port converter (ITPC), this paper proposes a three‐port resonant converter that uses a switch‐controlled‐capacitor (SCC) and an auxiliary inductor, which has resonant parameter compensation capability (RPC‐ITPC). The topology and operational principle of RPC‐ITPC are discussed in detail. To achieve resonant‐type power decoupling, it is usually necessary to ensure that the resonant frequency of the decoupling port resonant tank is consistent with the switching frequency, which puts high demands on the accuracy of the resonant components. To address the sensitivity of resonant‐type power decoupling to the parameters of resonant components, an SCC was adopted to solve the problem. The use of SCC enables the RPC‐ITPC to have resonant parameter compensation capability. In order to further optimize the performance of the RPC‐ITPC, a triple‐phase‐shift (TPS) modulation strategy is proposed to achieve the minimum RMS value of resonant current (MRMS‐TPSMS). Under the MRMS‐TPSMS, the switching devices of the power decoupling port have difficulty achieving ZVS turn‐on. To circumvent this problem, we realize ZVS of the decoupling port switching devices via auxiliary inductor current injection. Finally, a 1 kW experimental prototype was constructed to validate the theoretical propositions presented in this study. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Background: MR-guided stereotaxy has emerged as a viable alternative to CT-guided frame-based or frameless approaches. By offering direct MR-guided navigation, this technique addresses most limitations inherent to CT navigation, enabling submillimeter precision. Method: We detail the workflow of using the ClearPoint® MR navigation system for laser catheter placement in the treatment of a hypothalamic hamartoma. Conclusion: MR-guided navigation is a precise and effective stereotactic method for laser ablation of targets with high demands for accuracy. However, compared to standard stereotactic techniques, MR-guided navigation is more time-consuming and costly. [ABSTRACT FROM AUTHOR]

Direct oxidation of KA oil (the mixture of cyclohexanone and cyclohexanol) toward ε-caprolactone is in high demand yet hard to implement in need of juggling the activation of both methyne C-H bond of cyclohexanol and α-C-C bond of cyclohexanone. Here we demonstrate that in situ formed Cu1+δ-Oδ-• active site, which originates from relay reaction at Ni(II) and Cu(I) pairs in a metal-organic framework (known as NiCu-MOF-74) with O2 and benzaldehyde (PhCHO), efficiently oxidizes KA oil toward ɛ-caprolactone along with good stability. Mechanism investigation discloses that the auxiliary Ni(II) site first adsorbs O2 for abstracting formyl hydrogen in PhCHO followed by transfer of PhCO· to react with another O2 over the major Cu(I) site, leading to formation of Cu1+δ-Oδ-• and PhCOOH. This major-auxiliary cooperative strategy will be particularly suitable for multivariate MOFs as next generation catalysts towards complex reactions. ε-Caprolactone is a key starting point for the preparation of many polymers, such as polycaprolactone, polyurethane and nylon-6. Here the authors show that a metal-organic framework in conjunction with oxygen and benzaldehyde can facilitate the synthesis of ε-caprolactone from ketone-alcohol oil feedstock in a cascade oxidation. [ABSTRACT FROM AUTHOR]

Tungsten is an irreplaceable primary material and strategic resource for national economic development and modern national defense security. The endowment of tungsten ore resources in various countries is different; we should not only focus on the availability of tungsten ore itself but also the sustainability of its entire process from upstream supply to downstream industry demand. Analyzing the evolution of the supply and demand patterns of tungsten ore trade from the perspective of the industrial chain, identifying the key supply and demand subjects, and exploring the influencing factors of trade has become an important research topic. Therefore, we construct global tungsten trade networks based on the industrial chain, analyze its overall characteristics, explore critical trade supply and demand entities, and identify China's trade position. We got some meaningful results: (1) The global tungsten trade networks have prominent power-law distribution characteristics. The trend of trade globalization is evident, but the dominance is still in the hands of a few developed countries or developing countries with developed manufacturing industries. (2) In different parts of the tungsten industry chain, the impact of resource endowment, industrial technology level, and geographical location on the country's trade role is different. (3) China's position in the global tungsten resource industry chain and value chain is constantly improving, but there is still a risk of high-end technology being "stuck." This provides some suggestions for the systematic understanding of the evolution of the trade pattern of the tungsten industry chain and how countries occupy a dominant position in the game of tungsten resources. [ABSTRACT FROM AUTHOR]

Background: RNA secondary structural alignment serves as a foundational procedure in identifying conserved structural motifs among RNA sequences, crucially advancing our understanding of novel RNAs via comparative genomic analysis. While various computational strategies for RNA structural alignment exist, they often come with high computational complexity. Specifically, when addressing a set of RNAs with unknown structures, the task of simultaneously predicting their consensus secondary structure and determining the optimal sequence alignment requires an overwhelming computational effort of O (L 6) for each RNA pair. Such an extremely high computational complexity makes these methods impractical for large-scale analysis despite their accurate alignment capabilities. Results: In this paper, we introduce REDalign, an innovative approach based on deep learning for RNA secondary structural alignment. By utilizing a residual encoder-decoder network, REDalign can efficiently capture consensus structures and optimize structural alignments. In this learning model, the encoder network leverages a hierarchical pyramid to assimilate high-level structural features. Concurrently, the decoder network, enhanced with residual skip connections, integrates multi-level encoded features to learn detailed feature hierarchies with fewer parameter sets. REDalign significantly reduces computational complexity compared to Sankoff-style algorithms and effectively handles non-nested structures, including pseudoknots, which are challenging for traditional alignment methods. Extensive evaluations demonstrate that REDalign provides superior accuracy and substantial computational efficiency. Conclusion: REDalign presents a significant advancement in RNA secondary structural alignment, balancing high alignment accuracy with lower computational demands. Its ability to handle complex RNA structures, including pseudoknots, makes it an effective tool for large-scale RNA analysis, with potential implications for accelerating discoveries in RNA research and comparative genomics. [ABSTRACT FROM AUTHOR]

Limited insights have been obtained regarding the flows and stocks of thulium, the scarcest rare earth element. Thus, this study quantifies the thulium material flow from 2011 to 2020 in China, the largest thulium producer and reserve holder. Dynamic material flow analysis method is used and the demand and supply pattern of China's thulium is clarified. Results reveal that the final demand for thulium grows at an annual rate of 6.2%, reaching from 121 tonnes in 2011 to 208 tonnes in 2020, driven mainly by laser‐related end‐use sectors. Accumulative 1506 tonnes (80.7%) of domestically produced thulium in China originates from illegal mining during 2011–2020. This, combined with imported thulium compounds, bridges the significant gap between final demand and domestic legal supply. Moreover, 1422 tonnes of thulium are stocked in end‐use sectors by 2020, with 30.8% in fiber‐optic communication sector. A supply–demand gap has emerged since 2016 with thulium demand surpassing its domestic supply, which is projected to further widen in the future. Measures like promoting recycling and recovery, optimizing the trade patterns, and exploring material and technology substitution are proposed to mitigate such a gap. [ABSTRACT FROM AUTHOR]

Breeders successfully increased US soybean [Glycine max (L.) Merr.] yields over the past nearly 100 years and altered various plant characteristics underpinning the yield gains. However, the impact of breeding on plant‐level water‐use efficiency (WUEp) has not been examined yet. This study, conducted across eight environments using maturity group IV cultivars released between 1930 and 2005, aimed to (1) determine if soybean WUEp, assessed using C isotope composition (δ13C) measurements on shoot biomass sampled at early seed filling (R5), has changed with cultivar year of release (YoR), and (2) assess how canopy temperature (CT) and WUEp relate to each other and to seed yield. Across environments and cultivars, δ13C ranged from −27.52‰ to −28.24‰ and the correlation between cultivar YoR and WUEp was positive in four individual environments (p ≤ 0.07), as well as across the eight environments (p = 0.0083), with an average increase of shoot δ13C of 0.004‰ per year of soybean breeding. Lower average δ13C values for specific environments were associated with higher precipitation prior to biomass sampling, which is consistent with a lower WUEp when more water was available. Interestingly, across environments, midday CT at early seed filling was negatively correlated with YoR and WUEp, suggesting that intrinsic WUE of more recently released cultivars was lower during high demand periods. Further studies are needed to understand the mechanisms underlying these relationships between WUEp and CT and to identify physiological mechanisms that can be targeted for breeding high‐yielding cultivars while increasing or maintaining WUEp. [ABSTRACT FROM AUTHOR]

Long-duration energy storage (LDES) is a key resource in enabling zero-emissions electricity grids but its role within different types of grids is not well understood. Using the Switch capacity expansion model, we model a zero-emissions Western Interconnect with high geographical resolution to understand the value of LDES under 39 scenarios with different generation mixes, transmission expansion, storage costs, and storage mandates. We find that a) LDES is particularly valuable in majority wind-powered regions and regions with diminishing hydropower generation, b) seasonal operation of storage becomes cost-effective if storage capital costs fall below US$5 kWh−1, and c) mandating the installation of enough LDES to enable year-long storage cycles would reduce electricity prices during times of high demand by over 70%. Given the asset and resource diversity of the Western Interconnect, our results can provide grid planners in many regions with guidance on how LDES impacts and is impacted by energy storage mandates, investments in LDES research and development, and generation mix and transmission expansion decisions. This study models a zero-emissions Western North American grid to provide guidelines and understand the value of long-duration storage as a function of different generation mixes, transmission expansion decisions, storage costs, and storage mandates. [ABSTRACT FROM AUTHOR]

Energy storage (ES) units are vital for the reliable and economical operation of the power system with a high penetration of renewable distributed generators (DGs). Due to ES's high investment costs and long payback period, energy management with shared ESs becomes a suitable choice for the demand side. This work investigates the sharing mechanism of ES units for low-voltage (LV) energy prosumer (EP) communities, in which energy interactions of multiple styles among the EPs are enabled, and the aggregated ES dispatch center (AESDC) is established as a special energy service provider to facilitate the scheduling and marketing mechanism. A shared ES operation framework considering multiple EP communities is established, in which both the energy scheduling and cost allocation methods are studied. Then a shared ES model and energy marketing scheme for multiple communities based on the leader–follower game is proposed. The Karush–Kuhn–Tucker (KKT) condition is used to transform the double-layer model into a single-layer model, and then the large M method and PSO-HS algorithm are used to solve it, which improves convergence features in both speed and performance. On this basis, a cost allocation strategy based on the Owen value method is proposed to resolve the issues of benefit distribution fairness and user privacy under current situations. A case study simulation is carried out, and the results show that, with the ES scheduling strategy shared by multiple renewable communities in the leader–follower game, the energy cost is reduced significantly, and all communities acquire benefits from shared ES operators and aggregated ES dispatch centers, which verifies the advantageous and economical features of the proposed framework and strategy. With the cost allocation strategy based on the Owen value method, the distribution results are rational and equitable both for the groups and individuals among the multiple EP communities. Comparing it with other algorithms, the presented PSO-HS algorithm demonstrates better features in computing speed and convergence. Therefore, the proposed mechanism can be implemented in multiple scenarios on the demand side. [ABSTRACT FROM AUTHOR]

Charging infrastructure is the backbone of electromobility. Due to new charging behaviors and power distribution and charging space constraints, the energy demand and supply patterns of electromobility and the locations of current refueling stations are misaligned. Infrastructure developers (charging point operators, fleet operators, grid operators, vehicle manufacturers, and real-estate developers) need new methodologies and tools that help reduce the cost and risk of investments. To this extent we propose a transport-energy-demand-centric, dynamic adaptive planning approach and a data-driven Spatial Decision Support System (SDSS). In the SDSS, with the help of a realistic digital twin of an electrified road transport system, infrastructure developers can quickly and accurately estimate key performance measures (e.g., charging demand, Battery Electric Vehicle (BEV) enablement) of a candidate charging location or a network of locations under user-specified transport electrification scenarios and constraints and interactively and continuously calibrate and/or expand their network plans as facts about the deep uncertainties about the supply side of transport electrification (i.e., access to grid capacity and real-estate and presence of competition) are gradually discovered/observed. This paper describes the components and the planning support of the SDSS and how these can be used in competitive and collaborative settings. Qualitative user evaluations of the SDSS with 33 stakeholder organizations in commercial discussions and pilots have shown that both transport-energy-demand-centric and dynamic adaptive planning of charging infrastructure planning are useful. [ABSTRACT FROM AUTHOR]

Purpose: Arthroscopic Bankart repair combined with remplissage and autologous scapular spine bone grafting have been described as a treatment for off‐track Hill–Sachs lesions with subcritical glenoid bone defects in the anterior shoulder instability. However, whether these two techniques can achieve satisfactory postoperative outcomes is unclear, and there are few comparative studies between them. Therefore, this study compared the postoperative efficacy of the two techniques for off‐track Hill–Sachs lesions with subcritical glenoid bone loss. Method: Between June 2017 and December 2020, 62 patients with shoulder instability due to Off‐Track Hill–Sachs lesions with subcritical glenoid bone loss underwent surgical treatment and were included in this regression study. Thirty‐two patients underwent arthroscopic Bankart repair combined with remplissage (B + R group), and 30 patients underwent additional autologous scapular glenoid bone grafting (additional bone grafting group). The general information of the patients was recorded. The patient's activity before and after surgery was recorded. The DASH score and Constant–Murley (CM) score were used to assess the patient's functional status; the Rowe score was used to evaluate the patient's shoulder stability. The shoulder function and stability before and after surgery were analyzed and compared between the two groups. Results: The final DASH scores of the B + R group and the additional bone grafting group were significantly lower than those before surgery, with a statistically significant difference (9.76 ± 4.32 vs. 27.89 ± 6.63, 8.50 ± 3.32 vs. 28.0 ± 4.27, p = 0.000); the final CM scores of the two groups were significantly higher than those before surgery (88.71 ± 3.74 vs. 73.68 ± 3.74, 87.16 ± 2.29 vs. 71.37 ± 2.68, p = 0.000). There was no statistical difference in the final DASH score and final CM score between the two groups (p > 0.05). In terms of postoperative stability, the final Rowe scores of the two groups were significantly higher than those before surgery, with a statistically significant difference (89.06 ± 9.19 vs. 41.71 ± 4.13; 93.16 ± 4.99 vs. 42.33 ± 2.53, p = 0.000). Compared with the control group, the additional bone graft group achieved higher final Rowe scores (93.16 ± 4.99 vs. 89.06 ± 9.19, p = 0.032). Conclusion: For patients with anterior shoulder instability due to off‐track Hill–Sachs lesions with subcritical glenoid bone loss, although Bankart and remplissage can achieve satisfactory clinical results, additional autogenous scapular spine bone grafting can provide better stability of the shoulder, especially for patients with high sports demands. [ABSTRACT FROM AUTHOR]

Background There is a high demand in the food industry and in public health for rapid automated methods capable of high-volume sample processing. Objective In an unpaired study, Roka Atlas® System performance was compared with Health Canada reference method MFHPB-30 for Listeria spp. (LSP Roka assay) and Listeria monocytogenes (LmG2 Roka Assay) detection on plastic (PL), sealed concrete (SC), and stainless-steel (SS) surfaces (45 samples each per candidate or reference method). Methods Seeking shorter enrichment time for the candidate method, R2 medium pre-enrichment for 14, 16, and 24 h at 35°C was combined with the Roka assay. Listeria welshimeri , L. innocua , and L. monocytogenes were employed to individually inoculate each of the three surfaces, with two competing microorganisms within the 10–100-fold higher concentration range. Results False negative, false positive, sensitivity, and specificity were 0, 0, 100, and 100%, respectively, for the plastic, sealed concrete, and stainless-steel surfaces, regardless of inoculation level (high, low, and uninoculated) and enrichment time. Candidate method detected 10, 7, and 9 true positives, versus 10, 6, and 10 by the reference method in individually inoculated SS, PL, and SC, respectively. Conclusions Probability of detection for all the three surfaces for the Roka Atlas System was comparable to the reference method in this unpaired study. Highlights The Roka Atlas System detected targets after as little as 14 h enrichment. Surface type did not negatively affect assay sensitivity or specificity. The Roka Atlas System was comparable to the reference method. [ABSTRACT FROM AUTHOR]

Climate variability influences renewable electricity supply and demand and hence system reliability. Using the hidden states of the sea surface temperature of tropical Pacific Ocean that reflect El Niño-Southern Oscillation (ENSO) dynamics that is objectively identified by a nonhomogeneous hidden Markov model, we provide a first example of the potential predictability of monthly wind and solar energy and heating and cooling energy demand for 1 to 6 months ahead for Texas, United States, a region that has a high penetration of renewable electricity and is susceptible to disruption by climate-driven supply-demand imbalances. We find a statistically significant potential for oversupply or undersupply of energy and anomalous heating/cooling demand depending on the ENSO state and the calendar month. Implications for financial securitization and the potential application of forecasts are discussed. [ABSTRACT FROM AUTHOR]

Software process simulation (SPS) has become an effective tool for software process management and improvement. However, its adoption in industry is less than what the research community expected due to the burden of measurement cost and the high demand for domain knowledge. The difficulty of extracting appropriate metrics with real data from process enactment is one of the great challenges. We aim to provide evidence‐based support of the process metrics for software process (simulation) modeling. A systematic literature review was performed by extending our previous review series to draw a comprehensive understanding of the metrics for process modeling following our proposed ontology of metrics in SPS. We identify 131 process modeling studies that collectively involve 1975 raw metrics and classified them into 21 categories using the coding technique. We found product and process external metrics are not used frequently in SPS modeling while resource external metrics are widely used. We analyze the causal relationships between metrics. We find that the models exhibit significant diversity, as no pairwise relationship between metrics accounts for more than 10% SPS models. We identify 17 data issues may encounter in measurement and 10 coping strategies. The results of this study provide process modelers with an evidence‐based reference of the identification and the use of metrics in SPS modeling and further contribute to the development of the body of knowledge on software metrics in the context of process modeling. Furthermore, this study is not limited to process simulation but can be extended to software process modeling, in general. Taking simulation metrics as standards and references can further motivate and guide software developers to improve the collection, governance, and application of process data in practice. [ABSTRACT FROM AUTHOR]

The Disability Royal Commission (the Commission) investigated many issues, one of which was whether people with cognitive disabilities, including people with intellectual disabilities, faced systemic neglect in the Australian health system. Using a method of co-design with people with intellectual disabilities, we analysed the Commission's approach and recommendations about preventive health care. We identified how the Commission's recommendations can inform the achievement of the National Preventive Health Strategy (the Strategy) for people with intellectual disabilities. We found that a strength of the Commission's process was the multiple ways it inquired about the experience of accessing preventive health care. However, the inclusiveness of the invitations to participate in the Commission's work and the location of the public hearings were limitations for key stakeholders. Our analysis also noted that the Commission's public hearings focused on the supply side of healthcare access (health services) compared to the demand side of access (health users). This was a missed opportunity to learn from health users about their experiences. Two challenges to the implementation of the recommendations include the focus on cognitive disability instead of intellectual disability, and the focus on health care in general, rather than specifically on preventive health care. We also found that the Commission's recommendations mainly relate to the leadership, governance and funding elements in the Strategy, with a few actions relevant to prevention, partnerships, information, research, surveillance and preparedness. It is important that all the elements required to mobilise prevention systems are considered and actioned to achieve the Strategy. [ABSTRACT FROM AUTHOR]

Urban land use is central to urban planning. With the emergence of urban big data and advances in deep learning methods, several studies have leveraged graph convolutional networks (GCNs) with local functional characteristics from points of interest data and spatial features from flow data to infer urban land use. However, these studies cannot distinguish spatial interaction and spatial dependence in terms of conceptualization and modeling mechanisms and overlook the inadequacy of GCNs in modeling spatial interaction. This study proposes a novel framework—a heterogeneous graph convolutional network (HGCN)—to explicitly account for the spatial demand and supply components embedded in spatial interaction data. Several experiments, including 19 different models and datasets from Shenzhen and London, were conducted to validate the proposed framework and its generalizability within the same and different spatial contexts. The HGCN can distinguish heterogeneous mechanisms in supply- and demand-related modalities of spatial interactions, incorporating both spatial interaction and spatial dependence for urban land-use inference. Empowered by HGCN, we found that spatial interaction features play a distinctively crucial role in urban land-use inference compared to local attributes and spatial dependence features. In addition, our findings highlight the superiority of HGCN-based models in boosting performance and enhancing model transferability. [ABSTRACT FROM AUTHOR]

Summary: An analysis of a single‐ended high‐efficiency broadband parallel‐circuit (PC) Class‐E power amplifier (PA) with a single reactance compensation technique (RCT) and low‐pass (LP) Chebyshev impedance transformer is first presented under low supply voltage. Analysis of the gate and drain voltage allows to determine the required optimum voltage values for the expected PA design objectives. Derivation and design procedure of the broadband matching network (MN) is provided, which can effectively filter out higher order harmonics and reduce circuit complexity. In addition, the load adaptability for this PA is studied, achieving higher than 67.87% drain efficiency (DE) in the case of load mismatch. A PC Class‐E PA based on NMOS transistor is designed and implemented to validate the analysis over a frequency band of 7–9.4 MHz. Experimental results show that the PA delivers 36.48–38.83 dBm output power with a DE of 84.91% to 86.91%, as well as achieves 83.98% to 86.28% power‐added efficiency (PAE), 19.48–21.83 dB gain with a low supply voltage of 12 V. It is also demonstrated that the experimental results exhibit good agreement with the simulation results, which prototype the radio frequency identification transmitter with broadband high‐efficiency and system compactness. To the author's knowledge, this study represents the first approach to design broadband PC Class‐E PA under low supply voltage for Internet of Things (IoT) applications at HF frequency band, such as electronic article surveillance (EAS) system. [ABSTRACT FROM AUTHOR]

Cloud storage adoption has increased over the years given the high demand for fast processing, low access latency, and ever-increasing amount of data being generated by, e.g., Internet of Things applications. In order to meet the users' demands and provide a cost-effective solution, cloud service providers offer tiered storage; however, keeping the data in one tier is not cost-effective. In this respect, cloud storage tier optimization involves aligning data storage needs with the most suitable and cost-effective storage tier, thus reducing costs while ensuring data availability and meeting performance requirements. Ideally, this process considers the trade-off between performance and cost, as different storage tiers offer different levels of performance and durability. It also encompasses data lifecycle management, where data is automatically moved between tiers based on access patterns, which in turn impacts the storage cost. In this respect, this article explores two novel classification approaches, rule-based and game theory-based, to optimize cloud storage cost by reassigning data between different storage tiers. Four distinct storage tiers are considered: premium, hot, cold, and archive. The viability and potential of the proposed approaches are demonstrated by comparing cost savings and analyzing the computational cost using both fully-synthetic and semi-synthetic datasets with static and dynamic access patterns. The results indicate that the proposed approaches have the potential to significantly reduce cloud storage cost, while being computationally feasible for practical applications. Both approaches are lightweight and industry- and platform-independent. [ABSTRACT FROM AUTHOR]

Comprehensive Summary: The saline and buffered environment in actual wastewater imposes higher demands on Fenton and Fenton‐like catalytic systems. This study developed a MoS2 co‐catalytic Fe2O3 Fenton‐like system with controllable Lewis acid‐base sites, achieving efficient treatment of various model pollutants and actual industrial wastewater under neutral buffered environment. The acidic microenvironment structured by the edge S sites (Lewis basic sites) in the MoS2/Fe2O3 catalyst is susceptible to the influence of Lewis acidic sites constructed by Mo and Fe element, affecting catalytic performance. Optimizing the ratio of precursor amounts ensures the stable presence of the acidic microenvironment on the surface of catalyst, enabling the beneficial co‐catalytic effect of Mo sites to be realized. Furthermore, it transcends the rigid constraints imposed by the Fenton reaction on reaction environments, thereby expanding the applicability of commonplace oxides such as Fe2O3 in actual industrial water remediation. [ABSTRACT FROM AUTHOR]

This study focused on evaluating the perceptions of a group of call center operators in Quito regarding their work environment using Plithogenic statistics to address the complexity and ambiguity of the responses. The sample included 114 workers from three companies, assessed using the Battery of the Ministry of Social Protection of Colombia and a work stress questionnaire. The results revealed high job demands, perceived as stressful by employees, although satisfaction with the rewards received was shown. However, significant uncertainty was identified regarding control over work, leadership, and social relationships, suggesting critical areas for improvement. The use of Plithogenic logic allowed for an in-depth analysis of these phenomena, providing valuable information for future organizational interventions. It is recommended to implement stress management programs and improve communication and leadership to strengthen role clarity and increase job satisfaction and efficiency. [ABSTRACT FROM AUTHOR]