Efficient Program Research Articles

A Systematic Review of the Costs of Drug-Associated Acute Kidney Injury and Potential Cost Savings with Nephrotoxin Stewardship Prevention Strategies.

There is a scarcity of information related to the financial impact of acute kidney injury (AKI), and even more so the economics of drug-associated AKI (D-AKI). Our goal was to provide a comprehensive summary of the economic burden of D-AKI by evaluating the costs of D-AKI compared to not developing AKI and cost savings associated with nephrotoxin stewardship approaches. Following the PRISMA guidelines, a literature search was conducted using PubMed to identify articles from database inception through November 2023. The main outcomes included AKI incidence, resource use, and cost of nephrotoxin stewardship programs/D-AKI event or no event. Key findings were summarized based on whether the study compared the cost of D-AKI vs. no AKI or identified potential cost savings associated with a nephrotoxin stewardship method to prevent D-AKI or worsening D-AKI. All costs were adjusted to USD2023. Twenty-five studies met the inclusion criteria. Eight studies compared the cost of D-AKI to no AKI. Total admission costs of patients who developed D-AKI ranged from $47,696 to $173,569. Nineteen studies implemented nephrotoxin stewardship with 12 substituting a less nephrotoxic drug; five using therapeutic drug monitoring and two altering drug dosing to limit exposure. Overall, these prevention strategies ranged from $5,171 to $364,973 in total medical cost savings and $17 to $942 in total cost savings per patient-day. The in-hospital economic impact of D-AKI is substantial. Implementing nephrotoxin stewardship strategies to reduce D-AKI is associated with cost savings. Institutions should adopt strategic and efficient nephrotoxin stewardship programs to optimize patient care and reduce costs.

Enhancing monitoring to promote early detection and eradication of invasive species

Ecological niche models are often used to predict the distribution of invasive species before or after they have been detected in new regions. Such models should also be used to guide surveys to promote the early detection and eradication of invasive species. Here we propose a practical framework that seamlessly uses ecological niche models to develop sampling routes that promote detection of invasive species. Our framework uses habitat suitability predictions and occurrence data on incursion populations to generate potential survey sites, which are then prioritized for sampling based on their size and suitability. The generated survey route is then displayed on an open street map platform. Our framework was developed into the ‘enmRoute' R package and a user‐friendly website to facilitate its application, and we validated our framework with a case study. We show that integrating ecological niche models with human transport routes promotes identification of survey sites that are predicted to collect more individuals and have a greater potential for species detection than traditional sampling approaches. Our framework may help industries, invasion biologists, and regulators develop economical and efficient survey programs for invasive pest monitoring that make eradication programs more attainable.

Energy policy implications of Ecuador’s NDC

In the third national communication submitted by Ecuador, the total greenhouse gases (GHG) emission was calculated at 80,627 GgCO2-eq, considering the country’s commitment to the Framework on Climate Change. In 2018, Ecuador ratified its nationally determined contribution (NDC) to reduce its GHG emissions by 11.87% from the business-as-usual (BAU) scenario by 2025. The macroeconomic impacts of NDC implementation in the energy sector are discussed. A Computable Equilibrium Model applied to Ecuador (CGE_EC) is used by developing scenarios to analyze partial and entry implementation, as well as an alternative scenario. Shocks in exogenous variables are linked to NDC energy initiatives. So, the NDC’s feasibility depends on guaranteeing the consumption of hydropower supply, either through local exports or domestic demand. In the last case, the government’s Energy Efficiency Program (PEC) and electricity transport have important roles, but the high levels of investment required and poor social conditions would impair its implementation. NDC implementation implies a GDP increase and price index decrease due to electricity cost reductions in the productive sector. These conditions depend on demand-supply guarantees, and the opposite case entails negative impacts on the economy. The alternative scenario considers less dependence on the external market, achieving higher GDP, but with only partial fulfillment of the NDC goals.

Application status and prospect of machine learning in the field of enterprise development planning

Abstract. In today's digital information background, data is already an important means of production, various kinds of data continue to grow, and it is difficult to rely on the manpower to deal with such a huge amount of data. Machine learning can learn the law of data change from a large number of data and build a model for analysis, to give accurate and efficient program opinions. With the development of machine learning, deep machine learning in daily life is gradually widespread, and several successful commercial applications such as Google Translate have been born. Based on machine learning technology, this paper studies its application status and prospects in enterprise development planning. The research results show that more and more enterprises now apply machine learning to the planning and summary of the enterprise, at the same time, there are many functions of machine learning to be developed in the enterprise, through more development, enterprises can carry out more reasonable development and planning. The significance of this research is that it can provide specific application programs and solutions for enterprises, and also provide important references and guidance for the future development of enterprises, which is conducive to the healthy development of enterprises. In the future, with the continuous increase in the amount of data, the continuous progress of algorithms and the improvement of computing power, the application of machine learning in enterprise development planning will be more extensive and in-depth.

Assessment of heating degree-days factor (HDDF) for building-oriented climate zoning in Türkiye

ABSTRACT In this study, a novel heating degree-days factor (HDDF) is proposed to determine the optimum facade design for buildings in Türkiye as a function of both cooling degree-days (CDD) and heating degree-days (HDD) data. For this purpose, the HDDF (HDD/(HDD+CDD)) is defined as the ratio of the heating degree-days of a region to the total heating and cooling degree-days. Firstly, the HDDF values of all provinces in Türkiye are calculated by using meteorological data from 81 meteorological stations covering the period 1991–2022, and HDDF performance is evaluated. Then, the building-oriented climate zoning is realized based on the HDDF values. Moreover, historical trends of monthly and yearly average HDD, CDD, and HDDF values are revealed and yearly HDD, CDD, and HDDF maps were created using the ArcGIS program. According to the results, HDDF values in Türkiye varied between 0.4519 and 0.9998, and HDDF values between 0.9 ≤ HDDF ≤ 1 cover 46 out of 81 cities in the country and span a broad area across the country. The findings suggest that if global warming continues at the current rate, cooling energy demand in the residential sector will dramatically increase in the future, while heating energy demand will fall in Türkiye. This indicates that domestic energy consumption regulations would require modification. The building-oriented climate zoning resulting from this study is unique to Türkiye and can aid engineers, architects, and policymakers in building energy efficiency programs, studies, regulations, and standards.

Evaluating the effectiveness of climate change policies and initiatives in the United States: A comprehensive review

Climate change presents profound challenges, demanding effective policies to mitigate its impacts. This paper reviews the effectiveness of climate policies in the United States, analyzing scholarly articles, government reports, and relevant literature. The review covers diverse policies such as greenhouse gas regulations, renewable energy initiatives, energy efficiency programs, and adaptation measures. It assesses their success in reducing carbon emissions, advancing a low-carbon economy, and enhancing resilience to climate impacts. Key findings highlight significant successes, such as the rise in renewable energy due to renewable portfolio standards and reduced emissions from vehicle fuel efficiency standards. State-level initiatives, like carbon pricing and renewable energy targets, also show promising outcomes. However, the review identifies substantial challenges, including political and economic obstacles, inconsistent policies across states, and the need for better federal-state-local collaboration. Additionally, disparities in policy benefits, with marginalized communities facing greater burdens and limited access to renewable resources, are significant concerns. To enhance the effectiveness of climate policies, the study recommends fostering coherent policies across all government levels, promoting equitable and inclusive climate solutions, raising public awareness, and supporting innovation in clean technologies. This comprehensive review provides valuable insights for policymakers and stakeholders to develop strategies that effectively address climate change, promote sustainability, and ensure a fair transition to a low-carbon future.

Enhancing Mineral Exploration Programs Through Quantitative XRD: A Case Study from the Gumsberg Polymetallic Sulphide Deposits, Sweden

As challenges in precious and base metal exploration intensify due to the diminishing availability of high-grade ore deposits, rising demand, energy costs, and stricter regulations towards net-zero carbon activities, advanced techniques to enhance exploration efficiency are becoming increasingly critical. This study demonstrates the effectiveness of quantitative X-ray diffraction (QXRD) with Rietveld refinement, coupled with multivariate statistical analysis (including agglomerative hierarchical clustering, principal component analysis, and fuzzy analysis), in characterizing the complex mineralogy of strata-bound volcanic-associated limestone-skarn Zn-Pb-Ag-(Cu-Au)-type sulphide deposits (SVALS). Focusing on 113 coarse rejects from the Gumsberg project located in the Bergslagen mining district in central Sweden, the research identified five distinct mineralogical clusters corresponding to polymetallic base metal sulphide mineralization, its proximal alteration zones, and variably metamorphosed host rocks. The results reveal significant sulphide mineralization, ranging from disseminated to massive occurrences of sphalerite, pyrrhotite, pyrite, and galena, with trace amounts of secondary minerals like anglesite in certain samples indicating weathering processes. The study also identifies rare minerals such as armenite, often overlooked in traditional geological logging. These findings underscore the potential of QXRD to enhance resource estimation, optimize exploration strategies, and contribute to more efficient and sustainable mineral exploration programs. The accuracy of QXRD was cross-validated with geological logs and geochemical data, confirming its reliability as a mineralogical discrimination tool.

Isokinetic Force Modeling in Sports Based on Embedded System of Motion Recognition Model

With the gradual deepening of sports research, sports training methods integrating more scientific and technological means can greatly improve athletes’ professional ability. Combined with the isokinetic strength training method, it can effectively alleviate muscle recovery and body flexibility, which requires an analysis of the level of sports body promotion, and a more efficient training program for various sports parameters. In this study, taking strength enhancement as an example, the convolutional neural network is innovatively integrated into isokinetic strength training. Using the motion data obtained by wearable sensors in the training, we trained the neural network model by real-time analysis of data samples through an embedded system. Further, we optimized the network parameters to realize the extraction and recognition of EMG depth features. Compared with the traditional isokinetic strength classification model, this model lays a foundation for further constructing personalized strength training guidance methods. After the simulation test, the difference of the model’s accuracy requirement before and after the pre-training is 4.6%, and the difference of the test set before and after the pre-intensive training is [Formula: see text]1%. It is 16.2% higher than the TCAM algorithm, and the gap with STA-LSTM remains between [Formula: see text]. It is verified that the attention concentration control scheme can not only reduce the error rate in visual recognition tasks, but also improve the complexity of the neural network model. The optimized lightweight data structure is especially suitable for embedded systems with limited computing power. All kinds of test projects verify its rationality and make full use of the control ability of the embedded system and the characteristics of parallel computing and programmability, which has great practical application value for the development of the Internet of Things system network. The research results have made a thorough study of the physiological guidance of existing strength training, the identification and evaluation methods of exercise-induced muscle fatigue, and the current situation of the existing isokinetic equipment.

Equilibrio y carga plantar en diferentes técnicas de sentadilla

This research aims to analyze dynamic balance and plantar load in different squatting techniques, providing insights into the influence of these exercises in practitioners who incorporate weightlifting into their training routines. A sample of 21 subjects engaged in activities involving three squatting techniques: Back Squat (BS), Front Squat (FS), and Overhead Squat (OHS) was analyzed. Participants assumed a baseline posture and performed exercises with approximately 50% RM for each squatting technique. A plantar pressure measurement system was used to assess center of pressure (COP) displacement, peak pressure (PP), and plantar load in different regions of the foot. One-way Welch's ANOVA considering COP displacement, PP, and percentage of force for each foot region was measured. The results showed no significant effect of the squatting technique on COP trajectory during the exercises. However, a significant interaction was observed between the squatting technique and PP compared to the static balance situation. All three squats resulted in higher PP than static balance, with OHS exhibiting the highest values, followed by FS and BS. No significant differences were found between the squat techniques. These findings suggest that different squat techniques can influence plantar load, but do not affect the trajectory of the COP during the execution of the exercise. The heel region generated the greatest plantar load among the three squat techniques and should receive greater attention during the squat and its variations. This information can assist in prescribing exercises and developing more efficient training programs for weightlifters.

Optimizing Colorectal Screening in Portugal with Process Mining

Abstract Background Efficient healthcare screening programs management influences patient outcomes. Process mining has emerged as a powerful tool to enhance these programs by refining complex workflows and pinpointing operational deviations. Objectives This study utilizes process mining techniques to analyze the colorectal cancer screening program in Northern Portugal, focusing on detecting deviations and performance bottlenecks in process models. Methods This screening program started in March 2018, but process mining was only used on data posterior to 2020. Over 1 million case observations from administrative data were included in the process model analysis, involving more than six key process activities and numerous resources. We used new software and statistical tools for process mining to rapidly compute observations and filter for different operational needs and monitor interventions. Results The discovery analysis identified deviations, such as unexpected performance issues and irregular activity sequences. Although over 100 traces were identified, fewer than five traces accounted for more than 90% of all case paths, indicating the most common traces that have a true impact in participation rates, referral rates, and treatment rates. Notable differences in resource performance during specific periods, such as COVID-19 or seasonal events, were also observed. These findings provided insights for adjusting resource allocation and improving coordination, with impact in minimizing seasonal phenomena or peak production periods. Lessons Process mining offers actionable insights for effective health program coordination, enabling the monitoring and timely response to process deviations. With key areas for action identified in a more timely and precise manner, we can enhance efficiency, and expand population coverage and treatment referral. Future initiatives will focus on further process optimization and national coverage expansion using these management tools. Key messages • Process mining revolutionizes healthcare by enabling the strategic management of complex oncological screening program workflows. • Comprehensive data analysis across the patient pathway enhances program efficiency and expands treatment access.

Genetic variability, heritability, and genetic advance estimates in selected groundnut genotypes (<i>Arachis hypogaea</i> L.) for agro-morphological and yield traits.

Groundnuts (Arachis hypogaea L.), is a crucial food and oil crop in the tropics, require genetic variability assessment for efficient breeding programs using appropriate parameters like genetic coefficient of variation, heritability estimates, association among yield related traits and genetic advance. This study aims to identify genotypes with desirable traits to improve yield and adaptability, contributing to better crop improvement strategies. The study used a randomized complete block design with three replications, consisting of fourteen selected genotypes. Quantitative traits were used to estimate genetic variability parameters, heritability, and genetic advance. The analysis of variance revealed significant differences (p < 0.005) for all studied traits, indicating greater variability in genotypes. Characters exhibited different levels of variability, heritability, and genetic advance among the genotypes. The estimate of the broad-sense heritability ranged from 13.33% to 33.33% for canopy spread and days to maturity respectively. Low to high phenotypic coefficient of variation (PCV) and genotypic coefficient of variation (GCV) were recorded. The study found that traits such as number of branches, pods per plant, fresh and dry plant weight, kernel yield, pod yield, and shelling percentage showed high genotypic coefficient of variation, moderate broad sense heritability, and high genetic advance. These traits can be beneficial for groundnut improvement through effective phenotypic selection, resulting in high expected genetic gain. The study found that pod yield is significantly correlated with kernel yield, grain length, grain width, 100-seed weight, fresh and dry 100-pod weight, and number of pods per plant. Kernel yield is positively correlated with the number of branches per plant, pod per plant, fresh and dry 100-pod weight, 100-seed weight, grain length, and grain width. In addition, the first four principal components accounted for 90.37% of total variation in all morphological traits.

Development of Leadership Skills in Medical Education: Protocol for a Scoping Review.

Leadership is recognized as an essential competency in health care and science, being central for professionals to face health challenges. Few physicians feel prepared to serve as leaders in the health care environment, and few receive training in the leadership skills needed to be successful. Teaching leadership skills together with extensive, longitudinal, clinical education in an authentic and nurturing environment can effectively develop students for leadership in medicine. Studies on the subject still do not show the best way to implement it in medical education, and an updated review is necessary. The aim of this study is to identify the types of available evidence on the teaching of leadership skills in undergraduate courses in the health area, analyze them, determine knowledge gaps, and disseminate the research results. This is a scoping review that will consider studies on leadership skills in medical and health undergraduate courses. Primary studies published in English, Spanish, and Portuguese since 2019 will be considered. The search will be performed in 8 databases, and reference lists will be searched for additional studies. Duplicates will be removed, and 2 independent reviewers will examine the titles, abstracts, and full texts of the selected studies. Data extraction will be performed using a tool developed by the researchers. The scoping review is currently in progress. The preliminary database search has been completed, yielding a total of 1213 articles across multiple databases. The next stages, including deduplication, title and abstract screening, and full-text review, are scheduled to be completed by December 2024. Data extraction and analysis are expected to be finalized by March 2025, with the final report anticipated to be ready for submission by June 2025. This scoping review on leadership in the medical curriculum can significantly contribute to the literature by organizing and synthesizing the available evidence on teaching leadership skills in undergraduate courses in the health area. Furthermore, by analyzing evidence and identifying knowledge gaps, the study can provide valuable insights to develop more efficient and comprehensive medical education programs, thus preparing students to take on leadership roles in the complex environment of health care. Open Science Framework YEXKB; https://osf.io/yexkb. PRR1-10.2196/62810.

The costs and the potential allocation of costs of bouillon fortification: The cases of Nigeria, Senegal, and Burkina Faso.

Food and condiment fortification programs are needed to address micronutrient deficiencies and their health, developmental, and mortality consequences; but these programs are never free. Knowing program costs and their allocation across stakeholders is essential to design and manage effective, efficient, and sustainable programs. We developed 10-year hypothetical bouillon fortification program cost models for Nigeria, Senegal, and Burkina Faso that included start-up and operational costs for government and industry, as well as premix costs generated by an embedded premix cost calculator to allow for alternative premix formulas in cost calculations. The main drivers of total costs were total bouillon consumption and the types and amounts of fortificants in the micronutrient premix. For a premix that meets 30% of Codex Nutrient Reference Values in 2.5g of bouillon for vitamin A, folate, vitamin B12, zinc, and iron, the cost per metric ton of fortified bouillon was ∼$325 for all countries (∼$0.01 per 2.5g serving). Annual start-up costs ranged from ∼$324k (Burkina Faso) to ∼$455k (Nigeria); nonpremix annual operating costs ranged from ∼$108k (Burkina Faso) to ∼$3.9m (Nigeria); and annual premix costs varied from ∼$2.4m (Burkina Faso) to ∼$76m (Nigeria). In policy discussions, program costs should be set alongside nutritional benefits.

The Construct of Emotional Support in Quranic Memorization Students: A Study on the Dynamic Influence of Reliable Relationships, Trusted Guidance, Psychological Well-being, and Quranic Memorization Achievement

Using a mixed-method approach, This study examines emotional support for Quranic memorization students (santri tahfidz) in Islamic boarding schools (pesantren). Quantitative analysis of 515 santri tahfidz revealed high levels of emotional support (M = 3.74, SD = 0.68) with two primary dimensions: reliable relationships (74.96%) and trusted guidance (75.17%). Multiple regression analysis uncovered a significant relationship between emotional support and Quranic memorization achievement (β = 0.42, p < .001, R² = 0.18). A meta-analysis of 28 studies (N = 3,627) confirmed a positive correlation between emotional support and students' psychological well-being (r = 0.39, 95% CI [0.34, 0.44]). MANOVA results indicated significant differences in santri tahfidz's perceptions of emotional support based on gender (Wilks' λ = .97, F(2, 512) = 7.85, p < .001, partial η² = .03) and age (Wilks' λ = .95, F(4, 1022) = 6.73, p < .001, partial η² = .03). These findings extend previous research by Hasfi (2018) and Vista (2018) by identifying the specific roles of teacher support (β = 0.31, p < .01) and peer support (β = 0.28, p < .01) in enhancing student resilience. Furthermore, this study contributes to a more comprehensive understanding of emotional support dynamics in pesantren tahfidz education, integrating Bronfenbrenner's (1979) ecological perspective and Deci and Ryan's (2000) self-determination theory. Finally in a practical context, the researcher evaluates this study has implications for the development of more structured and efficient emotional support intervention programs in tahfidz pesantren.

Anthropometric Profile and Position-Specific Changes in Segmental Body Composition of Professional Football Players Throughout a Training Period.

Body and anthropometric profiles of football players vary depending on the physiological and technical demands imposed by different positions. The aim of this study was to evaluate the body composition of professional soccer players in relation to their position on the field during a training macrocycle. The Direct Segmental Multi-Frequency Bioelectrical Impedance Analysis method was used to analyze 58 players at six key moments of the macrocycle. The results show that body profiles are adjusted to the specific demands of each position. Midfielders showed the lowest muscle mass, while defenders showed many notable changes. In general, as the season progressed, all field players experienced an increase in trunk body fat. Fat and lean mass values of goalkeepers differed greatly from the rest. The greatest variations in body composition were observed during pre-season and transition in relation to variations in training load and competitive intensity. The results suggest that the phase of the macrocycle has a greater influence on these variations, although the physical characteristics of each position are relevant. Understanding these dynamics allows for the design of more personalized and efficient training programs to optimize the performance of footballers according to their roles and each stage of the season.

Extracting efficient exact real number computation from proofs in constructive type theory

Abstract Exact real computation is an alternative to floating-point arithmetic where operations on real numbers are performed exactly, without the introduction of rounding errors. When proving the correctness of an implementation, one can focus solely on the mathematical properties of the problem without thinking about the subtleties of representing real numbers. We propose a new axiomatization of the real numbers in a dependent type theory with the goal of extracting certified exact real computation programs from constructive proofs. Our formalization differs from similar approaches, in that we formalize the reals in a conceptually similar way as some mature implementations of exact real computation. Primitive operations on reals can be extracted directly to the corresponding operations in such an implementation, producing more efficient programs. We particularly focus on the formalization of partial and nondeterministic computation, which is essential in exact real computation. We prove the soundness of our formalization with regards to the standard realizability interpretation from computable analysis and show how to relate our theory to a classical formalization of the reals. We demonstrate the feasibility of our theory by implementing it in the Coq proof assistant and present several natural examples. From the examples we have automatically extracted Haskell programs that use the exact real computation framework AERN for efficiently performing exact operations on real numbers. In experiments, the extracted programs behave similarly to native implementations in AERN in terms of running time and memory usage.

Viability Study of SYCL as a Unified Programming Model for Heterogeneous Systems Based on GPUs in Bioinformatics

Bioinformatics and Computational Biology are disciplines that have used GPUs for over two decades to accelerate data processing in computational applications, where CUDA has been the most utilized programming language. However, the exclusive use of CUDA presents a portability issue, as it is only compatible with NVIDIA GPUs and not with other heterogeneous architectures, such as AMD or Intel GPUs, or any other type of accelerator. To address this portability challenge, the Khronos Group recently introduced the SYCL standard, a multi-platform programming model that offers a high-level programming interface. This standard facilitates the development of portable applications that can efficiently leverage the capabilities of different hardware devices, such as NVIDIA, Intel, and AMD GPUs, without needing significant modifications to the source code. Therefore, this thesis set out as its general objective to evaluate the feasibility of SYCL as a unified, portable, and efficient heterogeneous programming model for the design and development of computationally demanding applications on heterogeneous GPU-based systems, specifically in the field of bioinformatics. Initially, a detailed investigation was conducted about heterogeneous programming models, performance metrics, and bioinformatics concepts, in order to establish the theoretical foundations of this thesis. Then, the SW# suite was chosen as the case study, as it represents a clear example of a CUDA-based bioinformatics application for biological sequence alignment. Using the SYCLomatic tool, a complete migration of the CUDA code to SYCL was carried out, which involved modifying the generated code and solving runtime errors. Additionally, the functionality was verified, optimizations were applied, and the resulting SYCL code was standardized to be compatible with other SYCL implementations. Subsequently, multiple experiments were conducted to evaluate the functionality and performance portability of the software migrated to SYCL. These experiments involved running the application on a wide variety of HPC platforms, including different CPUs and GPUs from various manufacturers. The results obtained demonstrated performance comparable to CUDA in most configurations, confirming the effectiveness of SYCL. Moreover, good performance portability across platforms was observed due to SYCL's ability to run on various hardware combinations. Furthermore, performance remained consistent when switching SYCL implementations. In conclusion, this study demonstrates that SYCL is a viable alternative as a unified, portable, and efficient programming model in the context of heterogeneous computing with GPUs for bioinformatics applications. The findings of this study lay the groundwork for the transition of legacy applications and the development of new solutions leveraging the capabilities of SYCL.

Research on cargo volume prediction and personnel scheduling optimization of sorting center based on BP neural network and improved genetic algorithm

With the development of Internet technology, e-commerce has become the primary choice for consumers to shop, and the huge order transaction volume has generated logistics problems. As an important part of the logistics network, the sorting platform is essential to improve its management efficiency. To enhance the efficiency of the sorting center, the topological relationship diagram is used to show the logistics network, and the cargo volume of the sorting center in the next 30 days and every hour is predicted based on the BP neural network model. Based on the mixed integer linear programming model and the improved genetic algorithm SCAGA model, the total man-days are minimized to ensure that the cargo volume is processed. At the same time, to ensure that the actual hourly efficiency is as balanced as possible, the optimization problem of personnel scheduling is studied. To maintain the efficient operation of the logistics industry, the accurate prediction of the number of goods and the reasonable scheduling of personnel, to provide a set of efficient and economical personnel scheduling programs for the logistics company, thus improving the overall operational efficiency and service quality.

Groundwater Recharge Response to Reduced Irrigation Pumping: Checkbook Irrigation and the Water Savings Payment Plan

Ongoing investments in irrigation technologies highlight the need to accurately estimate the longevity and magnitude of water savings at the watershed level to avoid the paradox of irrigation efficiency. This paradox arises when irrigation pumping exceeds crop water demand, leading to excess water that is not recovered by the watershed. Comprehensive water accounting from farm to watershed scales is challenging due to spatial variability and inadequate socio-hydrological data. We hypothesize that water savings are short term, as prior studies show rapid recharge responses to surface changes. Precise estimation of these time scales and water savings can aid water managers making decisions. In this study, we examined water savings at three 65-hectare sites in Nebraska with diverse soil textures, management practices, and groundwater depths. Surface geophysics effectively identified in-field variability in soil water content and water flux. A one-dimensional model showed an average 80% agreement with chloride mass balance estimates of deep drainage. Our findings indicate that groundwater response times are short and water savings are modest (1–3 years; 50–900 mm over 10 years) following a 120 mm/year reduction in pumping. However, sandy soils with shallow groundwater show minimal potential for water savings, suggesting limited effectiveness of irrigation efficiency programs in such regions.

CoolerSpace: A Language for Physically Correct and Computationally Efficient Color Programming

Color programmers manipulate lights, materials, and the resulting colors from light-material interactions. Existing libraries for color programming provide only a thin layer of abstraction around matrix operations. Color programs are, thus, vulnerable to bugs arising from mathematically permissible but physically meaningless matrix computations. Correct implementations are difficult to write and optimize. We introduce CoolerSpace to facilitate physically correct and computationally efficient color programming. CoolerSpace raises the level of abstraction of color programming by allowing programmers to focus on describing the logic of color physics. Correctness and efficiency are handled by CoolerSpace. The type system in CoolerSpace assigns physical meaning and dimensions to user-defined objects. The typing rules permit only legal computations informed by color physics and perception. Along with type checking, CoolerSpace also generates performance-optimized programs using equality saturation. CoolerSpace is implemented as a Python library and compiles to ONNX, a common intermediate representation for tensor computations. CoolerSpace not only prevents common errors in color programming, but also does so without run-time overhead: even unoptimized CoolerSpace programs out-perform existing Python-based color programming systems by up to 5.7 times; our optimizations provide up to an additional 1.4 times speed-up.