Efficient Allocation Research Articles

Holistic personas to increase the novice developer productivity

A deeper understanding and integration with system users' thoughts and emotional experiences are required for user-engaged development. User experience (UX) journey integrates user requirements and problem-solving approaches. The integration of data-driven techniques and user-centric approaches in software development is investigated in this study. It focuses on using the Markov chain model to predict developer productivity based on data gathered while creating personas across three projects. Organizations can gain valuable insights into user needs and requirements by conducting purposeful activities such as strength, weaknesses, opportunities, and threats (SWOT) analysis, competitor analysis, hypothesis formulation, identification of behavioral variables, mapping interviews, and defining characteristics and objectives. The model has predictive capabilities that allow for more informed decision-making, more efficient resource allocation, and better project planning. The goal of the activity and the model ensure the development of software products that effectively meet the needs of users, resulting in a higher success rate for software development initiatives. This study emphasizes the importance of integrating quantitative and qualitative analysis to drive successful software development projects and increase productivity while meeting user needs. According to the findings of the research conducted from the three projects completed, the proposed methods have similarities, and predictions using the Markov chain can determine the success of novice developers.

Community participation and effective water management: A study on water user organizations (WUOs) in Thailand

This study examines the factors enhancing the effectiveness of Water User Organizations (WUOs) in Thailand, spotlighting the integration of geo-informatics and community involvement in water management. Employing a community-based participatory research approach, we assessed the impact of advanced technological tools and increased community engagement on WUOs' operations and sustainability. Our methodology included surveys, interviews, and an analysis of water management practices before and after geo-informatics implementation. Findings reveal a significant increase in community participation and an improvement in water management knowledge. The use of geo-informatics led to notable advancements in water allocation efficiency and conflict resolution, directly contributing to the financial sustainability of WUOs. Specifically, the application of geo-informatics facilitated more accurate and timely water resource allocation, evidenced by enhanced community engagement metrics and the resolution of longstanding conflicts. These results underscore the value of combining geo-informatics with community-driven approaches to water management, offering valuable insights for policymakers and practitioners. The study advocates for policies that support the adoption of technological innovations and promote community-centric governance, providing a scalable model for sustainable water resource management. This approach not only improves the functionality and sustainability of WUOs but also sets a precedent for holistic water governance practices globally.

Road network model in Thailand for highly efficient transportation

This research presents a comprehensive model for enhancing the road network in Thailand to achieve high efficiency in transportation. The objective is to develop a systematic approach for categorizing roads that aligns with usage demands and responsible agencies. This alignment facilitates the creation of interconnected routes, which ensure clear responsibility demarcation and foster efficient budget allocation for road maintenance. The findings suggest that a well-structured road network, combined with advanced information and communication technology, can significantly enhance the economic competitiveness of Thailand. This model not only proposes a framework for effective road classification but also outlines strategic initiatives for leveraging technology to achieve transportation efficiency and safety.

Monthly Runoff Prediction for Xijiang River via Gated Recurrent Unit, Discrete Wavelet Transform, and Variational Modal Decomposition

Neural networks have become widely employed in streamflow forecasting due to their ability to capture complex hydrological processes and provide accurate predictions. In this study, we propose a framework for monthly runoff prediction using antecedent monthly runoff, water level, and precipitation. This framework integrates the discrete wavelet transform (DWT) for denoising, variational modal decomposition (VMD) for sub-sequence extraction, and gated recurrent unit (GRU) networks for modeling individual sub-sequences. Our findings demonstrate that the DWT–VMD–GRU model, utilizing runoff and rainfall time series as inputs, outperforms other models such as GRU, long short-term memory (LSTM), DWT–GRU, and DWT–LSTM, consistently exhibiting superior performance across various evaluation metrics. During the testing phase, the DWT–VMD–GRU model yielded RMSE, MAE, MAPE, NSE, and KGE values of 245.5 m3/s, 200.5 m3/s, 0.033, 0.997, and 0.978, respectively. Additionally, optimal sliding window durations for different input combinations typically range from 1 to 3 months, with the DWT–VMD–GRU model (using runoff and rainfall) achieving optimal performance with a one-month sliding window. The model’s superior accuracy enhances water resource management, flood control, and reservoir operation, supporting better-informed decisions and efficient resource allocation.

Welfare loss of pricing schemes in commuting train and interregional train operations

As metropolitan areas expand, commuter rail increasingly extends outward, creating unintended competition with interregional rail. Competition between commuter train service and interregional train service engenders an inefficient allocation of rail services, thereby inducing welfare losses. Hence, the imperative arises to stratify train classes clearly via rail fare adjustments. However, the public nature of rail services acts as a price ceiling, making it challenging to implement rail service fare increases, thus necessitating a solution capable of distinguishing between train classes with minimal fare adjustments. This study employs a Stated Preference (SP) survey targeting rail users traversing the Seoul-Suwon and Seoul-Cheonan segments of the Gyeongbu Line in Korea to elucidate remedies for enhancing the inefficiency prevalent in the rail pricing schemes. Distinct preference clusters for each rail service and the value of time and seat reservation service for rail users were preliminarily discerned by leveraging the SP survey. The proposed solution advocates for an increase in interregional rail service fares of $0.52 for the Seoul-Suwon section and $3.14 for the Seoul-Cheonan section, along with a restriction in seat reservation services, to achieve differentiation between train classes. Implementation of this solution is anticipated to recalibrate the mode share within the rail services, fostering efficiency in service allocation and bolstering the profitability of rail operators, thereby mitigating welfare losses. This study contributes to rail pricing and efficiency of railway operations through SP analysis of rail transportation.

Investigating the Application of Regression Discontinuity in the Field of International Health Economics

As one of the most significant quasi-experimental research methods, with over eight decades of development, Regression Discontinuity Design (RDD) has garnered extensive attention within the field of international health economics. It has provided robust methodological support for causal inference in research conducted within this domain. This paper provides a comprehensive review and synthesis of the foundational concepts, origins, and evolutionary trajectory of the RDD. It systematically examines the relevant studies in the domain of international health economics, discusses the strengths and limitations of the RDD, and anticipates its future applications in the same field. The aim is to support the utilization of RDD in international health economic research. On a theoretical level, it has effectively enhanced the flexibility and applicability of research designs within the domain of international health economics, augmenting the economic efficiency and external validity of studies, and offering a versatile tool for related research on a global scale. On a practical level, RD accommodates the diverse policy needs of programs across various countries and regions. It provides a more robust and reliable empirical foundation for the formulation of pertinent health policies, aiming to improve the efficiency of health resource allocation and promote equity in health outcomes.

Application of Data Mining Techniques in Healthcare: Identifying Inter-Disease Relationships through Association Rule Mining

This research focuses on the application of data mining techniques in a healthcare environment by utilizing patient visit data from Hospital X, coded with ICD-10 diagnoses. The purpose of this study is to explore the application of data mining techniques in a healthcare environment, specifically to identify the relationship between diseases using patient visit data from X Hospital. This research utilizes the FP-Growth algorithm method followed by Association Rule Mining to find frequent occurrences of diseases in the data set. The research process involved data pre-processing, transformation into binary format, and careful parameter setting (minimum support 0.95 and confidence 0.9). The results showed a strong association between chronic conditions such as hypertension and diabetes, which are prevalent in the patient population. This association provides insight into potential comorbidities and may assist healthcare providers in improving diagnosis accuracy and treatment effectiveness. This research has implications for the application of data mining techniques, demonstrating its potential in improving predictive analytics in healthcare and strategic planning. This approach not only aids in the efficient allocation of healthcare resources, but also aligns with the broader goal of improving personalized patient care.

Jasmonic acid and heat stress induce high volatile organic compound emissions in Picea abies from needles, but not from roots.

Plants emit diverse volatile organic compounds (VOCs) from their leaves and roots for protection against biotic and abiotic stress. An important signaling cascade activated by aboveground herbivory is the jasmonic acid (JA) pathway that stimulates the production of VOCs. So far it remains unclear if the activation of this pathway also leads to enhanced VOC emissions from conifer roots, and how the interplay of above- and belowground defenses in plants are affected by multiple stressors. Therefore, we simultaneously analyzed needle and root VOC emissions of Picea abies saplings, as well as CO2 and H2O fluxes in response to aboveground JA treatment, heat stress and their interaction in a controlled climate chamber experiment. Continuous online VOC measurements by PTR-TOF-MS showed an inverse pattern of total needle and root VOC emissions, when plants were treated with JA and heat. While needle sesquiterpene emissions increased nine-fold one day after JA application, total root VOC emissions decreased. This was mainly due to reduced emissions of acetone and monoterpenes by roots. In response to aboveground JA treatment, root total carbon emitted as VOCs decreased from 31% to only 4%. While VOC emissions aboveground increased, net CO2 assimilation strongly declined due to JA treatment, resulting in net respiration during the day. Interestingly, root respiration was not affected by aboveground JA application. Under heat the effect of JA on VOC emissions of needles and roots was less pronounced. The buffering effect of heat on VOC emissions following JA treatment points towards an impaired defense reaction of the plants under multiple stress. Our results indicate efficient resource allocation within the plant to protect threatened tissues by a rather local VOC release. Roots may only be affected indirectly by reduced belowground carbon allocation, but are not involved directly in the JA-induced stress response.

Research on the impact of regional integration policy on carbon emissions--Taking the Yangtze River Economic Belt as an example.

The Yangtze River Economic Belt serves as a paradigm of ecological integration and high-quality development within China. Under the constraints of the "Dual Carbon" goal, how does the integrative policy of the Yangtze River Economic Belt, aimed at reinforcing inter-regional industrial cooperation, impact carbon emissions across various provinces within the region? Leveraging panel data from 30 Chinese provinces spanning 2009-2019 and treating the 2016 promulgation of the "Yangtze River Economic Belt Development Planning Outline" as a quasi-natural experiment, this study employs a Difference-in-Differences (DID) model for discerning the effects of regional integration policies on carbon emissions, intensity, and efficiency in the 11 provinces of the Yangtze River Economic Belt. The research further delves into the underlying mechanisms through which policy interventions modulate provincial carbon emissions. Key findings include: (1) The policy's enactment has significantly attenuated provincial carbon emissions and intensity, albeit somewhat hampering the enhancement of carbon efficiency-conclusions robust to parallel trend and placebo tests; (2) Heterogeneity analyses reveal disparities in the policy's carbon emission effects, contingent on varying economic development levels and geographical loci; (3) Mechanistically, while the integration bolsters regional environmental governance and strengthens economic ties-thereby curtailing provincial emissions-it is evident that local governments have been somewhat inept in effectively channeling the influx of substantial short-term capital, hence stifling provincial carbon efficiency. In light of these insights, it is posited that local governments within the Yangtze River Economic Belt should ardently endeavor to refine and elevate regional industrial structures, champion the construction of an integrated regional market, intensify capital oversight and coordinated utilization, and enhance the efficiency of regional capital allocation, all in the quest to augment regional carbon emission reduction performance.

Adaptive Mobility-Based IoT LoRa Clustering Communication Scheme

Long Range (LoRa) as a low-power wide-area technology is distinguished by its robust long-distance communications tailored for Internet of Things (IoT) networks. Because LoRa was primarily designed for stationary devices, when applied to mobile devices, they become susceptible to frequent channel attenuation. Such a condition can result in packet loss, higher energy consumption, and extended transmission times. To address these inherent challenges posed by mobility, we propose an adaptive mobility-based IoT LoRa clustering communication (AMILCC) scheme, which employs the 2D random waypoint mobility model, strategically partitions the network into optimal spreading factor (SF) regions, and incorporates an adaptive clustering approach. The AMILCC scheme is bolstered by a hybrid adaptive data rate (HADR) mechanism categorized into two approaches, namely intra-SF and inter-SF region HADRs, derived from the standard network-based ADR mechanism for stationary devices, to ensure efficient resource allocation for mobile IoT LoRa devices. Evaluation results show that, based on simulations at low mobility speeds of up to 5 m/s, AMILCC successfully maximizes the packet success ratio to the gateway (GW) by over 70%, reduces energy consumption by an average of 55.5%, and minimizes the end-to-end delay by 47.62%, outperforming stationary schemes. Consequently, AMILCC stands as a prime solution for mobile IoT LoRa networks by balancing the high packet success ratio (PSR) with reliability with energy efficiency.

The Causal Linkages between Market Power and Cost Efficiency: Testing Quiet Life Hypothesis for the Banking Industry

This study aims to test the quiet life hypothesis considering the impact of market power on cost efficiency in Pakistani banks where managers of the banking industry slacken their efforts to enjoy a ‘quiet life’ due to market power and are not focused on the cost efficiency of their banks. Thus, banks with high market power are relatively less focused on the efficient allocation of resources, resulting in a loss of consumer surplus and efficiency. We take panel data for a sample of 25 commercial banks throughout 2006-2019. Our findings suggest Uni-directional causality running from cost efficiency towards market power in the case of Pakistan. In addition, to control for endogeneity, we adopt a dynamic two-step system GMM approach to model the relationship between market power and cost efficiency. Our findings suggest significantly negative effects of cost efficiency over market power, disproving the presence of quiet life in the case of Pakistan. This study provides substantial theoretical contributions in the form of the Quiet life hypothesis and welfare performance of banks. However, to the best of our knowledge, the causal linkages and the effects of market power on cost efficiency or vice versa are not documented for the banking industry of Pakistan.

From Time-Series to Hybrid Models: Advancements in Short-Term Load Forecasting Embracing Smart Grid Paradigm

This review paper is a foundational resource for power distribution and management decisions, thoroughly examining short-term load forecasting (STLF) models within power systems. The study categorizes these models into three groups: statistical approaches, intelligent-computing-based methods, and hybrid models. Performance indicators are compared, revealing the superiority of heuristic search and population-based optimization learning algorithms integrated with artificial neural networks (ANNs) for STLF. However, challenges persist in ANN models, particularly in weight initialization and susceptibility to local minima. The investigation underscores the necessity for sophisticated predictive models to enhance forecasting accuracy, advocating for the efficacy of hybrid models incorporating multiple predictive approaches. Acknowledging the changing landscape, the focus shifts to STLF in smart grids, exploring the transformative potential of advanced power networks. Smart measurement devices and storage systems are pivotal in boosting STLF accuracy, enabling more efficient energy management and resource allocation in evolving smart grid technologies. In summary, this review provides a comprehensive analysis of contemporary predictive models and suggests that ANNs and hybrid models could be the most suitable methods to attain reliable and accurate STLF. However, further research is required, including considerations of network complexity, improved training techniques, convergence rates, and highly correlated inputs to enhance STLF model performance in modern power systems.

Towards optimized scheduling and allocation of heterogeneous resource via graph-enhanced EPSO algorithm

Efficient allocation of tasks and resources is crucial for the performance of heterogeneous cloud computing platforms. To achieve harmony between task completion time, device power consumption, and load balance, we propose a Graph neural network-enhanced Elite Particle Swarm Optimization (EPSO) model for collaborative scheduling, namely GraphEPSO. Specifically, we first construct a Directed Acyclic Graph (DAG) to model the complicated tasks, thereby using Graph Neural Network (GNN) to encode the information of task sets and heterogeneous resources. Then, we treat subtasks and independent tasks as basic task units while considering virtual or physical devices as resource units. Based on this, we exploit the performance adaptation principle and conditional probability to derive the solution space for resource allocation. Besides, we employ EPSO to consider multiple optimization objectives, providing fine-grained perception and utilization of task and resource information. It also increases the diversity of particle swarms, allowing GraphEPSO to adaptively search for the global optimal solution with the highest probability. Experimental results demonstrate the superiority of our proposed GraphEPSO compared to several state-of-the-art baseline methods on all evaluation metrics.

#989 Fund and incentivize multidisciplinary CKD care: what can we learn from the French CKD bundled payment program?

Abstract Background and Aims Multidisciplinary care (MDC) is crucial to slow down the progression of Chronic Kidney Disease (CKD) and prevent or post-pone kidney replacement therapy. To incentivize the development of that care, France launched a nationwide bundled payment program (CKD-BP) targeted to patients with CKD G4 and G5. In this study, we review the implementation of the program, 3 years into its launch. Method First, using the CKD-BP program data collected by participating facilities, we described the characteristics and trajectories of patients enrolled from 2020 through 2022. Second, from exploratory interviews with medical and management staff, we performed a thematic analysis to identify the obstacles encountered during implementation and the changes introduced by the program. Results Since 2020, 157,863 patients were reported by 408 facilities (median age 75 y-o, 40% women, 77% G4). Missing data frequencies were high for comorbidities. Each year, around 4200 dialysis start (5% of patients) and 2600 deaths (3% of patients) were reported. On average, 47% and 40% of patients visited nurses and dietitians at least once a year respectively. From 22 interviews across 7 facilities, we identified challenges of implementation: adapting information systems, gathering the support of facility management and organizing the care in a context of workforce shortage, specifically nurses. Despite those challenges, facilities welcomed positively the new model, notably with increased time between nurses and patients and affirmation of dietitian's role. Conclusion We estimate the exhaustiveness of the CKD-BP program at 40% of all French patients with G5 CKD (around 4,600 dialysis start reported in 2021, compared to 11,000 from the national exhaustive REIN registry data). The results of this study suggest that interventions to accompany facilities in adapting their information systems are a priority to ensure efficient allocation of medical time and quality of the data. The results also suggest the need for increased flexibility regarding what professional is considered in the CKD-BP program, beyond nurses and dietitians. These challenges remain to be addressed in order to complete a successful shift from fee-for-service to assessable value-based care for CKD in France.

Task offloading with enhanced Deep Q-Networks for efficient industrial intelligent video analysis in edge–cloud collaboration

With the rapid development of 5G and IoT technologies, industrial video surveillance systems face increasingly significant challenges in processing large-scale data. Traditional cloud-centric video analysis methods often lead to processing delays and inefficiencies in resource allocation due to centralized data processing. In this context, the emergence of edge computing, especially in edge–cloud collaborative intelligent video analysis, offers an innovative solution. Within this architecture, task offloading strategies focus on intelligent computation task distribution to minimize latency and optimize resource utilization. However, existing methods frequently overlook key application-specific metrics in video analysis, such as accuracy and timeliness. This study introduces a novel Edge–Cloud Collaborative Industrial Video Intelligent Analysis Architecture (ECIVA) based on the GRU-Enhanced Deep Q-Network (GEDQN). This architecture integrates the strengths of Gated Recurrent Units (GRU) for processing sequential data with the efficiency of Deep Q-Networks in optimizing task-offloading decisions. By incorporating a token bucket mechanism, the architecture optimally manages the task offloading rate within the constraints of limited network bandwidth, thus ensuring high accuracy and real-time responsiveness in video analysis. Empirical evaluation with offshore drilling platform video data shows that the architecture achieves 87.5% mean Average Precision (mAP) at a 0.5 IoU threshold and increases system throughput by 39.2% over traditional cloud-centric methods.

Credit Market Conditions and Mental Health

Research offers conflicting predictions about the impact of credit conditions on mental health. We first assess how bank regulatory reforms that improved credit conditions, for example, by enhancing the efficiency of credit allocation and lowering lending rates, impacted mental health. We discover that among low-income individuals, these regulatory reforms reduced mental depression, boosted labor market outcomes, eased access to mortgage debt, and reduced the ranks of the “unbanked.” We also find that mergers of large regional banks that led to branch closures and tighter credit constraints in affected counties harmed the mental health of lower-income individuals in treated counties. This paper was accepted by Kay Giesecke, finance. Funding: C. Lin and M. Tai acknowledge financial support from the National Natural Science Foundation of China [Grant 72192841] and the Research Grants Council of the Hong Kong Special Administration Region, China [Project T35/710/20R]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00194 .

Co-designing talent transfer pathways in para-sport: a case study

ABSTRACT Research question: Talent transfer pathways offer great potential in para-sport yet are underexplored from a sport management perspective. Therefore, this study addressed the questions: What are the facilitators and barriers impacting sport systems ability to support talent transfer athletes and pathways, and how can the sport system improve talent transfer pathways in para-sport? Research methods: This exploratory study draws on expert practitioners’ perspectives of para-sport talent transfer through a co-design workshop conducted in 2023 with 12 participants (6 men and 6 women) experienced in areas including coaching, pathways and high-performance co-ordination and management, and classification. Findings: Processes for recognising, identifying, confirming, and progressing talent transfer athletes, the provision of para-sport and talent transfer-specific resources, and system collaboration were identified as factors influencing the sport system’s ability to support current, predominately informal, talent transfer. Eight distinct strategic areas for potential solutions were identified to improve talent transfer pathways in para-sport. Implications: The findings provide insights to enhance understanding of critical sport system factors in the underexplored area of para-sport talent transfer. Additionally, it offers direction for para-sport managers developing talent transfer initiatives, frameworks, and collaborations, ensuring the efficient allocation of resources, enhanced support and opportunities for athletes, and increased effectiveness of such programs.

Accident black spot clustering oriented maritime search and rescue resource allocation and optimization

Efficient and rapid deployment of maritime search and rescue(MSAR) resources is a prerequisite for maritime emergency search and rescue, in order to improve the efficiency and accuracy of MSAR. This paper proposes an integrated approach for emergency resource allocation. The approach encompasses three main steps: identifying accident black spots, assessing high-risk areas, and optimizing the outcomes through a synergistic combination of an optimization algorithm and reinforcement learning. In the initial step, the paper introduces the iterative self-organizing data analysis technology (ISODATA) for identifying accident spots at sea. A comparative analysis is conducted with other clustering algorithms, highlighting the superiority of ISODATA in effectively conducting dense clustering. This can effectively carry out dense clustering, instead of the situation where the data spots are too dispersed or obvious anomalies that affect the clustering. Furthermore, this approach incorporates entropy weighting to reassess the significance of accident spots by considering both the distance and the frequency of accidents. This integrated approach enhances the allocation of search and rescue forces, ensuring more efficient resource utilization. To address the MSAR vessel scheduling problem at sea, the paper employs the non-dominated sorting genetic algorithm II combined with reinforcement learning (NSGAII-RL). Comparative evaluations against other optimization algorithms reveal that the proposed approach can save a minimum of 7% in search and rescue time, leading to enhanced stability and improved efficiency in large-scale MSAR operations. Overall, the integrated approach presented in this paper offers a robust solution to the ship scheduling problem in maritime search and rescue operations. Its effectiveness is demonstrated through improved resource allocation, enhanced timeliness, and higher efficiency in responding to maritime accidents.

Enhancing the Efficiency of a Cybersecurity Operations Center Using Biomimetic Algorithms Empowered by Deep Q-Learning

In the complex and dynamic landscape of cyber threats, organizations require sophisticated strategies for managing Cybersecurity Operations Centers and deploying Security Information and Event Management systems. Our study enhances these strategies by integrating the precision of well-known biomimetic optimization algorithms—namely Particle Swarm Optimization, the Bat Algorithm, the Gray Wolf Optimizer, and the Orca Predator Algorithm—with the adaptability of Deep Q-Learning, a reinforcement learning technique that leverages deep neural networks to teach algorithms optimal actions through trial and error in complex environments. This hybrid methodology targets the efficient allocation and deployment of network intrusion detection sensors while balancing cost-effectiveness with essential network security imperatives. Comprehensive computational tests show that versions enhanced with Deep Q-Learning significantly outperform their native counterparts, especially in complex infrastructures. These results highlight the efficacy of integrating metaheuristics with reinforcement learning to tackle complex optimization challenges, underscoring Deep Q-Learning’s potential to boost cybersecurity measures in rapidly evolving threat environments.

Gene-deficient mouse model established by CRISPR/Cas9 system reveals 15 reproductive organ-enriched genes dispensable for male fertility

Since the advent of gene-targeting technology in embryonic stem cells, mice have become a primary model organism for investigating human gene function due to the striking genomic similarities between the two species. With the introduction of the CRISPR/Cas9 system for genome editing in mice, the pace of loss-of-function analysis has accelerated significantly. This has led to the identification of numerous genes that play crucial roles in male reproductive processes, including meiosis, chromatin condensation, flagellum formation in the testis, sperm maturation in the epididymis, and fertilization in the oviduct. Despite the advancements, the functions of many genes, particularly those enriched in male reproductive tissues, remain largely unknown. In our study, we focused on 15 genes and generated 13 gene-deficient mice [4933411K16Rik, Adam triple (Adam20, Adam25, and Adam39), BC048671, Cfap68, Gm4846, Gm4984, Gm13570, Nt5c1b, Ppp1r42, Saxo4, Sh3d21, Spz1, and Tektl1] to elucidate their roles in male fertility. Surprisingly, all 13 gene-deficient mice exhibited normal fertility in natural breeding experiments, indicating that these genes are not essential for male fertility. These findings have important implications as they may help prevent other research laboratories from duplicating efforts to generate knockout mice for genes that do not demonstrate an apparent phenotype related to male fertility. By shedding light on the dispensability of these genes, our study contributes to a more efficient allocation of research resources in the exploration of male reproductive biology.