Resource Allocation Research Articles

Water, sanitation, and hygiene service inequalities and their associated factors among urban slums and rural communities in Eastern Ethiopia

BackgroundUnderstanding the level of inadequate water, sanitation, and hygiene (WaSH) services in urban and rural settlements is crucial for prioritizing community interventions and resource allocation. However, there is a lack of evidence regarding discrepancies in WaSH services across rural and urban slum communities in Ethiopia.ObjectiveThis study aims to assess inequalities in households’ WaSH services and their associated factors among urban slums and rural communities in Eastern Ethiopia from February to April 2024.MethodsA comparative community-based cross-sectional study design was conducted, with study participants selected through stratified random sampling using proportional-to-size allocation. Data were entered into Epi Data version 3.1 and then exported to STATA version 17 for analysis. Bi-variable and multivariable logistic regression was conducted, and associations were reported as odds ratios (ORs) with 95% confidence intervals (CI), using a p-value less than 0.05 as the significance threshold.ResultA total of 278 urban and 301 rural households participated in the study, with a response rate of 94%. Key indicators showed that WaSH services were significantly better in urban areas compared to rural households. For example, 98% (95% CI: 96, 99) of urban households had access to an improved water source compared to 76% (95% CI: 71, 80) of rural households. The proportion of households with improved latrines was 44% (95% CI: 38, 50) in urban areas and only 14% (95% CI: 11, 19) in rural areas. Handwashing practices at all five critical times were reported by 52% (95% CI: 46, 58) of urban household heads, compared to 22% (95% CI: 18, 27) of rural households. Additionally, occupation (such as being a farmer, p = 0.000) and water service satisfaction (p = 0.000) were significantly associated with these key WaSH outcomes.ConclusionThe study revealed that the WaSH services in urban slums are considerably better than in rural households. Socioeconomic factors significantly influence the existing disparities in WaSH services. Stakeholders should focus on providing targeted, strategic support to communities to address the challenges in WaSH service provision.

Extending the G1 phase improves the production of lipophilic compounds in yeast by boosting enzyme expression and increasing cell size

Cell phase engineering can significantly impact protein synthesis and cell size, potentially enhancing the production of lipophilic products. This study investigated the impact of G1 phase extension on resource allocation, metabolic functions, and the unfolded protein response (UPR) in yeast, along with the potential for enhancing the production of lipophilic compounds. In brief, the regulation of the G1 phase was achieved by deleting CLN3 (G1 cyclin) in various yeast strains. This modification resulted in a 83% increase in cell volume, a 76.9% increase in dry cell weight, a 82% increase in total protein content, a 41% increase in carotenoid production, and a 159% increase in fatty alcohol production. Transcriptomic analysis revealed significant upregulation of multiple metabolic pathways involved in acetyl-CoA (acetyl coenzyme A) synthesis, ensuring an ample supply of precursors for the synthesis of lipophilic products. Furthermore, we observed improved protein synthesis, attributed to UPR activation during the prolonged G1 phase. These findings not only enhanced our understanding and application of yeast's capacity to synthesize lipophilic compounds in applied biotechnology but also offered unique insights into cellular behavior during the modified G1 phase, particularly regarding the UPR response, for basic research. This study demonstrates the potential of G1 phase intervention to increase the yield of hydrophobic compounds in yeast, providing a promising direction for further research.

Enhancing brewery logistics with smart kegs: A simulation study

Background: Supply chain optimisation is critical for companies in today’s globalised world. Efficient supply chains ensure product quality and freshness in the food industry. For products like beer, efficiency extends beyond transportation and includes the management of logistics containers such as returnable kegs.Objectives: This study investigated the impact of tracking logistics data on the efficiency of container logistics in a small regional brewery. The focus was reducing fleet size, processing times and pick-up distances.Method: A simulation of a small brewery was used to analyse the impact of tracking logistics data. The study examined how kegs equipped with real-time tracking technology could improve logistics operations.Results: The simulation showed that tracking logistics data significantly improved logistics processes. It led to a reduction in the fleet size, turnaround times and pick-up distances for empty kegs. These improvements were attributed to better resource allocation and more efficient management of reusable containers.Conclusion: The study showed that smart kegs could reduce logistical inefficiencies in the brewing industry. The use of smart technologies in container logistics improves operational efficiency and sustainability.Contribution: This research provides insights into how smart technologies can transform traditional logistics models. The results suggest that smart containers can revolutionise supply chains with efficient resource allocation, real-time tracking and automation. This shift towards smart logistics aligns with the broader adoption of the Industrial Internet of Things, offering a blueprint for future innovations in supply chain management. The implications extend beyond brewing, highlighting the potential for greater sustainability and a positive reduction in environmental impact in global supply chains.

BN SO34 - Outcomes in giant versus non-giant hiatus hernia repair: Same payment different operative burden

Abstract Background Hospital trust payment for operative procedures often rely on coding systems like the NHS's Office of Population Censuses and Surveys (OPCS) codes, which determine reimbursement rates. Both giant hiatus hernia (GHH) and non-giant hiatus hernia (NGHH) repairs often share the same code, as in our trust. This results in equal reimbursement to trusts for procedures with higher operative burden and resource needs. This study aims to assess differences in the patient population, their hiatal type, operative challenges and post-operative outcomes between GHH and NGHH repairs. Method All patients undergoing primary hiatus hernia surgery between March 2019 to March 2024 at a single institution were included. Retrospective review of electronic records were utilised to record: patient demographics, hernia type, operative details, length of stay (LOS), post-operative course and need for a return to theatre (RTT). Outcomes were comparison of surgical approach, use of mesh and gastropexy (as proxy measures to indicate difficulty of operation), 30-day morbidity measured using Clavien-Dindo (CD) grading and 30-day mortality and RTT. Analysis was undertaken using un-paired t-test and Chi2. A p-value of <0.05 was considered statistically significant. Results Table 1. Patients GHH NGHH p n 91 135 Age (average) 69 (±10) 52 (±16) <0.001 Female (%) 66.7 55.2 0.1100 BMI 30.8 (±5.4) 30.7 (±7.8) 0.2029 ASA grade (average) 2.2 1.9 <0.001 Hernia Types (median) 3 1 <0.001 Table 2. Operative details GHH NGHH p Open (%) 6.6 0 0.0020 Laparoscopic (%) 60.4 62.24 0.9840 Robotic (%) 33 33.3 0.9239 Mesh used (%) 8.8 0.7 <0.041 Gastropexy (%) 12 0.7 <0.001 Table 3. Outcomes GHH NGHH p LOS (median) 2 (0-161) 1 (0-8) 0.0179 RTT (%) 10.7 8 0.4747 30-day morbidity (CD≥3, %) 4.8 4.4 0.6519 30-day mortality 0 0 NA Conclusion This study highlights significant differences between giant hiatus hernia (GHH) and non-giant hiatus hernia (NGHH) repairs. GHH repairs have an increased likelihood of undergoing open procedures, use of mesh, and gastropexy, reflecting their greater complexity. GHH patients are older, have higher ASA grades, and present with more advanced hernia types compared to NGHH patients. Additionally, GHH repairs result in longer hospital stays. The findings underscore the need for improved coding accuracy to reflect these disparities accurately in patient demographics, operative strategies, and recovery outcomes within hospital trusts, ensuring appropriate resource allocation and reimbursement based on procedural complexity.

Enhancing survival in cardiac arrest: The urgent need for optimized extracorporeal cardiopulmonary resuscitation implementation and protocols: Editorial

Extracorporeal CPR (ECPR) involves venoarterial extracorporeal membrane oxygenation (VA-ECMO) in patients with sudden and unexpected pulse loss due to heart-stopping mechanical activity. ECPR in patients with cardiac arrest has been shown to significantly improve the prognosis. In emergency rooms, interdisciplinary coordination exists among emergency medicine, cardiology, critical care, and perfusion technology. However, some problems must be solved, such as excessive costs, resource allocation, and the need for specialized equipment. Resuscitation success and patient outcomes can be enhanced by combining ECPR and emergency care.

Machine learning for early dynamic prediction of functional outcome after stroke.

Prediction of outcome after stroke is critical for treatment planning and resource allocation but is complicated by fluctuations during the first days after onset. We propose a machine learning model that can provide hourly predictions based on the integration of continuous variables acquired within 72 h of hospital admission. We analyzed 2492 admissions for ischemic stroke in the Geneva University Hospital from 01.01.2018 to 31.12.2021, amounting to 2'131'752 unique data points. We developed a transformer model that continuously included clinical, physiological, imaging, and biological data recorded within 72 h of admission. This model was trained to generate hourly predictions of mortality and morbidity. Shapley additive explanations were used to identify the most relevant predictors to explain outcomes for each patient. The MIMIC-III database was used for external validation. Our transformer model predicts mortality, with an area under the receiver operating characteristic curve of 0.830 (95% CI 0.763-0.885) on admission, reaching 0.893 (95% CI 0.839-0.933) 72 h later for a 3-month outcome. Validated in an independent cohort, it outperforms all static models. Based on their mean explanatory weights, the top predictors included continuous clinical evaluation, baseline patient characteristics, timing from admission to acute treatment, and markers of inflammation and organ dysfunction. The performance of our transformer model demonstrates the potential of machine learning models integrating clinical, physiological, imaging, and biological variables over time after stroke. The clinical applicability of our model is further strengthened by access to hourly updated predictions along with accompanying explanations.

Conditional Flux Balance Analysis (cFBA) Toolbox for python: application to research metabolism in cyclic environments

Abstract Summary We present py_cFBA, a Python-based toolbox for conditional flux balance analysis (cFBA). Our toolbox allows for an easy implementation of cFBA models using a well-documented and modular approach and supports the generation of Systems Biology Markup Language (SBML) models. The toolbox is designed to be user-friendly, versatile, and freely available to non-commercial users, serving as a valuable resource for researchers predicting metabolic behaviour with resource allocation in dynamic-cyclic environments. Availability Extensive documentation, installation steps, tutorials and examples are available at [https://tp-watson-python-cfba.readthedocs.io/en/]. The py_cFBA python package is available at [https://pypi.org/project/py-cfba/].

Effects of sequential and non-sequential presentation conditions of multiple-stem facts on memory integration and cognitive resource allocation.

What limits the self-generation of new knowledge in the memory integration process? One striking contender is the amount of necessary pieces of information that are dispersed. Specifically, when essential information is scattered across multiple sources/places, it becomes challenging to effectively integrate and generate new knowledge. Most of the studies on memory integration have focused on the study of paired stem facts, but have neglected the exploration of multiple-stem facts. The present study examined college students' performance on memory integration under different conditions of three stem facts. In Experiment 1, participants were exposed to a series of novel, authentic stem facts in which every three relevant ones could be integrated to generate new knowledge. The results of Experiment 1 found that college students could spontaneously generate a new piece of information by integrating two or three separate but related facts. The integration can occur in at least two distinct types due to the different presentation orders of the learning materials: sequential recursive integration and non-recursive integration. College students performed better in sequential recursive integration than in non-sequential recursive integration, and this difference in integration performance is not caused by differences in memory for the stem facts. Based on Experiment 1, Experiment 2 used eye-tracking technology to explore the allocation of internal cognitive resources across different conditions of three stem facts. We found that in non-sequential recursive integration, college students had the longest visual duration and the highest number of fixations on the second stem fact. In sequential recursive integration, there were no other significant differences in the number and duration of visual fixations for the three stem facts. College students paid longer fixations to the second stem fact and the third stem fact in the non-sequential recursive condition than in the sequential recursive condition. Our study suggests that when information is related but cannot be integrated, longer fixation indicates stumbling when dealing with an unresolvable difficulty. When knowledge is presented in a stepwise manner (such as in the sequential recursive integration condition), it results in better semantic memory extension.

Estimates of resource use in the public-sector health-care system and the effect of strengthening health-care services in Malawi during 2015-19: a modelling study (Thanzi La Onse).

In all health-care systems, decisions need to be made regarding allocation of available resources. Evidence is needed for these decisions, especially in low-income countries. We aimed to estimate how health-care resources provided by the public sector were used in Malawi during 2015-19 and to estimate the effects of strengthening health-care services. For this modelling study, we used the Thanzi La Onse model, an individual-based simulation model. The scope of the model was health care provided by the public sector in Malawi during 2015-19. Health-care services were delivered during health-care system interaction (HSI) events, which we characterised as occurring at a particular facility level and requiring a particular number of appointments. We developed mechanistic models for the causes of death and disability that were estimated to account for approximately 81% of deaths and approximately 72% of disability-adjusted life-years (DALYs) in Malawi during 2015-19, according to the Global Burden of Disease (GBD) estimates; we computed DALYs incurred in the population as the sum of years of life lost and years lived with disability. The disease models could interact with one another and with the underlying properties of each person. Each person in the Thanzi La Onse model had specific properties (eg, sex, district of residence, wealth percentile, smoking status, and BMI, among others), for which we measured distribution and evolution over time using demographic and health survey data. We also estimated the effect of different types of health-care system improvement. We estimated that the public-sector health-care system in Malawi averted 41·2 million DALYs (95% UI 38·6-43·8) during 2015-19, approximately half of the 84·3 million DALYs (81·5-86·9) that the population would otherwise have incurred. DALYs averted were heavily skewed to children aged 0-4 years due to services averting DALYs that would be caused by acute lower respiratory tract infection, HIV or AIDS, malaria, or neonatal disorders. DALYs averted among adults were mostly attributed to HIV or AIDS and tuberculosis. Under a scenario whereby each appointment took the time expected and health-care workers did not work for longer than contracted, the health-care system in Malawi during 2015-19 would have averted only 19·1 million DALYs (95% UI 17·1-22·4), suggesting that approximately 21·3 million DALYS (20·0-23·6) of total effect were derived through overwork of health-care workers. If people becoming ill immediately accessed care, all referrals were successfully completed, diagnostic accuracy of health-care workers was as good as possible, and consumables (ie, medicines) were always available, 28·2% (95% UI 25·7-30·9) more DALYS (ie, 12·2 million DALYs [95% UI 10·9-13·8]) could be averted. The health-care system in Malawi provides substantial health gains with scarce resources. Strengthening interventions could potentially increase these gains, so should be a priority for investigation and investment. An individual-based simulation model of health-care service delivery is valuable for health-care system planning and strengthening. The Wellcome Trust, UK Research and Innovation, the UK Medical Research Council, and Community Jameel.

Medium and long-term regional water demand prediction using Harris hawks optimisation-backpropagation neural network model.

Precise water demand prediction is essential for the efficient allocation and rational utilisation of regional water resources. This study addressed the challenge associated with medium and long-term water demand prediction by introducing a novel coupled model, HHO-BPNN (Harris Hawks Optimisation-Backpropagation Neural Network). Principal component analysis was employed to reduce the dimensionality of potential water demand factors. The performance of the forecasting models was compared through mean square error (MSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and coefficient of determination (R2). The findings indicated that the HHO-BPNN outperformed traditional methods, such as BPNN, support vector machines, and grey prediction model. The study utilised the sliding window method to predict water demand for the next 1, 3, and 5 years for five prefecture-level cities in northern Jiangsu Province, China. High prediction accuracy was achieved across various categories of water demand (agricultural, industrial, domestic, and ecological), with the overall accuracy being impressive at 97%. Additionally, the forecasts aligned well with local developmental plans, suggesting practical applicability for urban planning. This study elucidates the key drivers impacting water demand, providing an effective tool for regional water demand forecasting, facilitating efficient and precise water management and decision-making in the future.

Gonadal development of adultoid reproductive in subterranean termites indicates strong reproductive potential.

The fair allocation of reproductive resources between population growth and outward dispersal plays a crucial role in determining competitive advantage and ecological success of organisms within their environment. Termite colonies demonstrate flexibility in allocating their reproductive resources through wing polymorphism: with alates possessing fully developed wings for dispersal to propagate the colony's genetic material, while adultoids have floppy wings, serving a secondary reproductive to ensure stability and development within the natal colony. However, the extent of reproductive potential exhibited by alates (post-shed-wings called dealate) and adultoids within the colony remains uncertain. In this study, through experimentation, we compared the reproductive capacity of dealate and adultoid. We found that the adultoids can develop to maturity and lay eggs faster after emergence. The reproductive potential including the number of ovariole, the number of oocytes in growth and vitellogenesis stage, and the expression levels of vitellogenin genes were all higher than dealates. More importantly, paired adultoids has a longer oviposition cycle and is able to obtain more eggs and larvae. These results suggest that adultoids possess a greater reproductive potential allowing them to produce a large number of offspring within a short period of time. Our results help to an explanation of the biological significance that the differentiation of adultoids reproductive persist in lower termite colonies.

The burden and trends of heart failure caused by ischemic heart disease at the global, regional, and national levels from 1990 to 2021.

Ischemic heart disease (IHD) is a major cause of heart failure (HF), a condition expected to increasingly affect global health and economics. This study evaluates the global burden, trends, and disparities of HF linked to IHD, aiming to inform health policy development. Data from the Global Burden of Disease Study 2021 (GBD2021) is analyzed using Joinpoint regression, decomposition analysis, and Bayesian age-period-cohort analysis (BAPC). Health disparities are assessed through the Socio-demographic Index (SDI) via the Slope Index of Inequality (SII) and the Concentration Index (CI), with future trends projected from 2022 to 2045. In 2021, global HF cases due to IHD were over 19.16 million, with an age-standardized prevalence rate (ASPR) of 228.31 per 100 000 [95% UI, 188.18 to 279.55] and age-standardized years lived with disability (ASYLDs) rate of 20.43 per 100 000 [95% UI, 13.55 to 28.7].In 2021, there was a 2.87% increase in ASPR and ASYLDs compared to 1990, primarily driven by population growth and aging.Significant reductions in global ASPR and ASYLDs disparities are observed, though the disease burden has intensified in countries with lower SDI levels. Projections indicate that by 2045, while the prevalence and YLDs for HF caused by IHD will increase, while the ASPR and ASYLDs are expected to decrease. The global burden of HF from IHD remains a significant concern. Urgent improvements in the allocation of medical resources and the implementation of effective prevention and management strategies are necessary to address this issue.

Cross sectional survey of maternal and newborn quality of care in Niger: Selected findings, lessons learned and recommendations.

Comprehensive assessments of quality of care (QoC) are essential for monitoring progress towards meeting global and national maternal and newborn health (MNH) targets. Liverpool School of Tropical Medicine (LSTM) and the Niger Ministry of Public Health adapted a 2014 WHO QoC tool to cover antenatal care (ANC) and postnatal care (PNC) and include client voices through exit interviews. The objectives of the study were to assess quality of MNH care in Niger and to document lessons learnt from implementing the LSTM QoC tool. Quality of Care (QoC) was assessed using five tools: health facility survey (using register and official record data), partograph reviews, healthcare provider knowledge assessment, exit interviews with clients, and observation of antenatal and postnatal care. A nationally representative sample of 110 public healthcare facilities at three levels (integrated health centres, district hospitals and mother-child hospitals) and 2153 women were included. Descriptive analysis with 95% confidence intervals was presented. The health facility survey showed variable access to electricity (63% [53-72]), water (72% [62-81]), and transportation (40% [31-50]). Tests and treatment for HIV, TB, and malaria were found in more than 90% of relevant facilities. During observation of first ANC visit, 62% [48-74] women were tested for HIV; 13% [5-30] for pre-eclampsia and 22% [12-36] for anaemia. Observation of PNC showed much lower rates of screening (15% [7-27] tested for HIV and 4% [0;11] for malaria). Partographs were used in 86% of deliveries with inconsistent completion. At client interviews, respectful care indicators were variable, with providers not always explaining results to clients (59% [50-67]). Targeted allocation of resources and training could impact on QoC and reduce missed opportunities for prevention, screening and management of diseases among pregnant women and babies. The QoC assessment tool proved capable of offering a comprehensive overview of priorities in MNH, while maintaining feasibility in the Nigerien context.

BS SO07 - NHS Tertiary Centre Experience with Bariatric Tourism; A Call for National Registry

Abstract Background Bariatric surgery has demonstrated notable efficacy and is becoming an increasingly popular practice. Nonetheless, patients often opt for bariatric tourism due to the unaffordability of bariatric procedures or the lengthy waiting lists. The postoperative care for such patients not only imposes financial burdens on a different health system, necessitating costly interventions for complex cases but also affects the allocation of resources for patients receiving care in the public health services. A national guidance platform and registry are required to measure the impact of bariatric tourism on the modern NHS and plan future resource allocation. Method This is a retrospective review of patients referred to one of the bariatric units in the United Kingdom for complications following bariatric procedures abroad. Patient demographics, bariatric intervention, presenting complications, length of hospital stay and management received were recorded. Results Between 2022 and 2024, our unit admitted nine patients. Eight had sleeve gastrectomy in Turkey, and one had a gastric balloon in Thailand. In the sleeve group, five patients had intractable vomiting, one had small bowel ischemia, one leaked, and one had reactive hypoglycemia. The patient with the gastric balloon had vomiting and abdominal pain, leading to balloon removal. Three sleeve patients had a conversion to Roux-en-Y bypass, one is under investigation, and one is awaiting bypass surgery, one required three surgeries for small bowel ischemia, and one with a leak was treated with a pigtail and gastric stent. Conclusion Medical tourism for bariatric procedures is an appealing option for obese patients due to its affordability and shorter wait times. Patients typically receive brief pre-operative preparation and education in addition to a limited post-operative care. However, managing complex bariatric cases is expensive, necessitates extended hospital stays and nutritional and emotional support, and can only be done in specialised centres. It's important to create a national bariatric surgery guidance platform providing recognised centre details internationally and a national registry of bariatric tourism patients to assess the impact on the healthcare system and to plan for future resource allocation.

Assessing Project Resilience Through Reference Class Forecasting and Radial Basis Function Neural Network

A project needs to be able to anticipate potential threats, respond effectively to adverse events, and adapt to environmental changes. This overall capability is known as project resilience. In order to make efficient project decisions when the project is subjected to disruption, such as adjusting the project budget, reformulating the work plan, and rationalizing the allocation of resources, it is necessary to quantitatively understand the level of project resilience. Therefore, this paper develops a novel approach for forecasting project performance, illustrating the changes in performance levels during the disruption and recovery phases of a project and thus quantitatively assessing project resilience. While there are several methods for assessing project resilience in existing research, the majority of assessment approaches originate from within projects and are highly subjective, which makes it difficult to objectively reflect the level of project resilience. Moreover, the availability of project samples is limited, which makes it difficult to forecast the level of project performance. In view of the fact that the Reference Class Forecasting (RCF) technique avoids subjectivity and the Radial Basis Function (RBF) neural network is known to be better at forecasting small sample datasets, this paper therefore combines the RCF technique and the RBF neural network to construct a model that forecasts the project performance of the current project after experiencing a disruption, further assessing the level of the project resilience. Specifically, this paper first presents a conceptual model of project resilience assessment; subsequently, an RBF neural network model that takes into account project budget, duration, risk level of disruption, and performance before disruption based on the RCF technique is developed to forecast project performance after experiencing disruption; and finally, the level of project resilience is assessed through calculating the ratio of recovery to loss of project performance. The model is trained and validated using 64 completed construction projects with disruptions as the datasets. The results show that the average relative errors between the forecast results of schedule performance index (SPI) and the real values are less than 5%, and the R2 of the training set and the testing set is 0.991 and 0.964, respectively, and the discrepancy between the forecasted and real values of project resilience is less than 10%. These illustrate that the model performs well and is feasible for quantifying the level of project resilience, clarifying its impact on project disruption and recovery situations, and facilitating the decision-makers of the project to make reasonable decisions.

Evaluation of Capital Utilization Efficiency in Agricultural Listed Firms Based on a Three-Stage Data Envelopment Analysis

Rural revitalization is intricately tied to national prosperity, with listed companies in agriculture, forestry, animal husbandry, and fisheries playing pivotal roles in driving this transformative agenda. However, these sectors face critical challenges, notably resource constraints and environmental pressures, which underscore the necessity of evaluating capital utilization efficiency. This study employs a combined three-stage DEA model and Malmquist index to analyze capital utilization efficiency across 85 listed companies within China’s agriculture, forestry, animal husbandry, and fishery industries over the period 2018–2022. The primary objectives are to quantify capital utilization efficiency, identify pathways for industry optimization, advance ecological agricultural sustainability, and offer valuable insights to investors and policymakers. Key findings include: (1) When grouped by sub-sector and adjusted to account for external environmental factors and random disturbances, the initial comprehensive capital utilization efficiency in agricultural companies was found to be significantly overestimated. Subsequent third-stage adjustments revealed decreases in average comprehensive efficiency, technical efficiency, and scale efficiency by 29.79%, 3.03%, and 27.37%, respectively, largely driven by a marked decline in scale efficiency, ultimately diminishing overall efficiency. (2) The capital utilization efficiency in agricultural trading firms remains suboptimal, with scale efficiency posing a critical limitation. High dependency on government subsidies and excess specialized personnel further constrain efficiency improvements. (3) Dynamic analysis using the DEA-Malmquist model indicates low total factor productivity in the agricultural sub-sector, primarily due to inefficiencies in capital management and suboptimal scale allocation. These findings underscore the need for targeted strategies to enhance resource allocation and management, bolster talent development and financial management frameworks, and drive technological research and development, innovation, and efficient capital allocation across the agriculture, forestry, animal husbandry, and fishery sectors.

Societal Impact of Test Automation: Reducing Human Error in Critical Systems

This article explores the profound societal impact of test automation across critical sectors such as healthcare, finance, transportation, and energy. It examines how automated testing processes significantly reduce human error, enhance system reliability, and improve service quality. The article presents compelling evidence from various studies and reports, demonstrating substantial improvements in medical diagnostics, financial fraud detection, transportation safety, and energy distribution efficiency. Beyond error reduction, the article discusses broader societal benefits, including enhanced accuracy in data processing, faster emergency response times, improved service quality, and strategic resource allocation. The article underscores the crucial role of test automation in addressing the challenges posed by increasingly complex technological systems and its far-reaching implications for public safety, economic stability, and overall quality of life.

Climate legislation and green agricultural production: impacts, transmission mechanisms and heterogeneities

PurposeThis study examines the impact of climate legislation on green agricultural production and tests the heterogeneous impact of different types of climate legislation on agricultural green production.Design/methodology/approachIn this study, the super-slacks-based measure (super-SBM) model is used to calculate agricultural green total factor productivity (AGTFP). The impact of climate legislation (including legislative acts and executive orders) on AGTFP is examined through regression analysis. The transmission mechanism of climate legislation affecting agricultural green production is further investigated.FindingsThis study shows that climate legislation has a positive long-term effect on AGTFP. It stimulates innovation in agricultural green technology but has a negative impact on resource allocation efficiency. Executive orders have a more significant effect on AGTFP than climate legislative acts. The effectiveness of climate legislation is more significant in countries with stronger legislation. Moreover, climate legislation reduces AGTFP in low-income countries while enhancing AGTFP in high-income countries. This effect is most prominent in upper-middle-income countries.Originality/valueThis study examines the different effects of various types of climate legislation, considering the level of economic development and the strength of the legal system on AGTFP. The findings can offer a global perspective and insights for China’s policymaking.

The Strategy of the Yogyakarta City Government in Implementing a Sustainable Zero Inorganic Waste Policy

This research aims to analyze the implementation of the Zero Inorganic Waste policy enacted by the Yogyakarta City Government, focusing on waste sorting strategies, infrastructure support, and challenges encountered in managing inorganic waste. The qualitative methodology involves field observations, in-depth interviews, and focus group discussions with various stakeholders. The research findings indicate that while this policy has successfully raised public awareness about waste separation, significant challenges remain, such as low public participation, limited infrastructure, and insufficient education efforts. Therefore, the government must increase resource allocation for waste management infrastructure and develop more effective educational programs to encourage behavioral change. This study’s contribution to urban waste management literature lies in its emphasis on the importance of a participatory and systematic approach to waste management and the need for public behavioral change to achieve sustainability goals. The conclusions of this study provide valuable insights for policymakers and practitioners in formulating more effective waste management strategies in Yogyakarta and other urban areas. This research also offers substantial theoretical contributions, particularly regarding the importance of participatory and systematic approaches in urban waste management and the necessity for societal behavioral changes to achieve sustainability objectives.

The economic burden of nosocomial infections for hospitals: evidence from Germany.

Nosocomial infections (NI) significantly worsen patient outcomes, resulting in higher mortality rates and reduced health-related quality of life. Furthermore, they pose substantial economic strain on healthcare systems and hospitals. For instance, patients with nosocomial infections (NIs) experience prolonged hospital stays compared to those without NIs. These extended stays result in occupied bed-days, leading to opportunity costs for hospitals. This study aimed to estimate the opportunity costs for a German hospital based on hospital stays, daily revenue, and occupancy rates (OCR). We analysed cost data obtained from routine records maintained by the accounting department of a German hospital's surgical and orthopedic units from 2018 to 2019 for the "HygArzt" research project. To ensure balance, we employed genetic matching. We estimated the differences in length of stay (LOS) and daily revenue between patients with and without nosocomial infections (NI) using linear regression. Finally, we calculated the opportunity cost borne by the hospital by treating NI patients instead of non-NI patients. All costs are reported in 2018 Euros. The final sample included 81 patients with NI matched with 207 patients without NI. The majority of the NI patients (77.0%) had surgical site infection (SSI). Compared to non-NI patients, we observed that NI patients had a longer LOS (10 days, p < 0.001) and lower daily revenue (€400, p < 0.001). We also found that comorbidities and the frequency of operations had significant impact on the LOS. Using a baseline 30 to 50% preventable NIs, successful prevention of a single NI could potentially reduce the length of hospital stay by 3 to 5 days and increase hospital revenue by approximately €120 to €200 per day per prevented NI. Consequently, the hospital saves 3 to 5 more bed-days to backfill and generate more revenue, and/or make more efficient resource allocation by changing bed-capacity and staffing. The resulting opportunity costs can potentially exceed €1,000 per preventable case. NIs pose a substantial economic burden for hospitals. From a health economics' perspective, there are strong economic incentives for hospitals to implement infection control interventions, such as the involvement of a prevention link physician/nurse.