Wide Adoption Research Articles

Lost in translation: The need for updated messaging strategies in invasion biology communication

Societal Impact StatementAs with any complex environmental challenge, information about the “invasive species problem” must be communicated effectively to generate widespread public trust, understanding, and engagement in solutions. However, there is growing recognition that conventional messaging strategies can be ineffective or even detrimental at activating the broader public. Flawed communication practices can have harmful societal impacts by perpetuating misinformation, propagating feelings of panic and fatalism, and disaffecting audience members within historically marginalized communities. In this article, we draw attention to existing communication barriers in invasion biology and attempt to address the flawed messaging strategies which perpetuate negative societal outcomes.SummaryEffective communication about introduced, invasive species is necessary for the public to engage in productive dialogue and participate in solutions. Best practices in communication are more essential than ever before, as the harmful environmental and societal impacts of invasive species are exacerbated by globalization and anthropogenic disturbance. In this article, we review how public misunderstanding and mistrust present persistent barriers to invasion biology communication. We then describe three flawed messaging strategies that underlie these barriers. These strategies include fear appeals, simplified invasion metaphors, and militaristic and xenophobic language. Though intended to inspire a public sense of urgency and action, these strategies more often stoke sentiments of confusion and mistrust when employed in practice. Efforts to develop new best practices in communication are already underway. However, there is a great need for efficacy testing and wider adoption among science and education professionals alike. We draw from the interdisciplinary literature to recommend specific forms of value appeals, metaphors, and language that are a promising foundation for improvement.

Integrating Project-Based Collaborative Learning into English Classroom: A Systematic Review

This paper systematically reviews the integration of Project-Based Collaborative Learning (PBCL) in English classrooms, focusing on the aims, methodologies, results, novelty, and contributions of studies conducted over the past decade. The review identifies key patterns, gaps, and future research opportunities in PBCL, offering insights for educators and policymakers. The findings highlight the effectiveness of PBCL in improving student engagement, language acquisition, and collaborative skills, making a compelling case for its wider adoption in English language classroom education.

Advancing optical transparency in 3D‐printed PLA parts using chemical post‐processing

AbstractFused filament fabrication (FFF) is an increasingly popular three‐dimensional (3D) printing technology known for its ability to produce complex 3D models at a low‐cost using polymer materials. Despite its advantages, the poor surface finish and high layer resolution in FFF‐printed parts have hindered its wider adoption, particularly for applications requiring high transparency. Thus, the current study investigates the transparency evaluation of chemically post‐processed 3D‐printed transparent polylactic acid (PLA) parts, focusing on enhancing optical transparency. Fused Filament Fabrication was employed to produce transparent parts made from PLA filament, which were subsequently treated at various time settings using various solvents, including tetrahydrofuran (THF), chloroform or trichloromethane (TCM), and ethyl acetate (EA). This study examines the impact of chemical post‐processing on the surface roughness, transparency, mechanical properties, and dimensional accuracy of solvent treated parts. It has been found that the THF solvent with 40 s of immersion time resulted in a maximum transmittance of 74.66% and minimum surface roughness of 1.777 μm. Based on this finding a case study was also conducted to apply the findings of the current investigation to a practical application. The findings highlight the potential of chemical‐based post‐processing techniques to improve the optical properties of 3D printed PLA, offering a promising approach for applications in optics, medical devices, and consumer goods requiring transparent components.Highlights Effect of chemical post‐processing on 3D‐printed PLA parts was investigated. Tetrahydrofuran, chloroform, and ethyl acetate were used as solvents. Surface roughness, transparency, hardness, and dimensional accuracy were analyzed. Reported a maximum transmittance of 74.66% and minimum surface roughness of 1.777 μm.

Microbes in Agriculture: Prospects and Constraints to Their Wider Adoption and Utilization in Nutrient-Poor Environments

Microbes such as bacteria and fungi play important roles in nutrient cycling in soils, often leading to the bioavailability of metabolically important mineral elements such as nitrogen (N), phosphorus (P), iron (Fe), and zinc (Zn). Examples of microbes with beneficial traits for plant growth promotion include mycorrhizal fungi, associative diazotrophs, and the N2-fixing rhizobia belonging to the α, β and γ class of Proteobacteria. Mycorrhizal fungi generally contribute to increasing the surface area of soil-root interface for optimum nutrient uptake by plants. However, when transformed into bacteroids inside root nodules, rhizobia also convert N2 gas in air into ammonia for use by the bacteria and their host plant. Thus, nodulated legumes can meet a high proportion of their N requirements from N2 fixation. The percentage of legume N derived from atmospheric N2 fixation varies with crop species and genotype, with reported values ranging from 50–97%, 24–67%, 66–86% 27–92%, 50–92%, and 40–75% for soybean (Gycine max), groundnut (Arachis hypogea), mung bean (Vigna radiata), pigeon pea (Cajanus cajan), cowpea (Vigna unguiculata), and Kersting’s groundnut (Macrotyloma geocarpum), respectively. This suggests that N2-fixing legumes require little or no N fertilizer for growth and grain yield when grown under field conditions. Even cereals and other species obtain a substantial proportion of their N nutrition from associative and endophytic N2-fixing bacteria. For example, about 12–33% of maize N requirement can be obtained from their association with Pseudomonas, Hebaspirillum, Azospirillum, and Brevundioronas, while cucumber can obtain 12.9–20.9% from its interaction with Paenebacillus beijingensis BJ-18. Exploiting the plant growth-promoting traits of soil microbes for increased crop productivity without any negative impact on the environment is the basis of green agriculture which is done through the use of biofertilizers. Either alone or in combination with other synergistic rhizobacteria, rhizobia and arbuscular mycorrhizal (AM) fungi have been widely used in agriculture, often increasing crop yields but with occasional failures due to the use of poor-quality inoculants, and wrong application techniques. This review explores the literature regarding the plant growth-promoting traits of soil microbes, and also highlights the bottle-necks in tapping this potential for sustainable agriculture.

Issues in the Adoption of Online Medical Care: Cross-Sectional Questionnaire Survey.

Telemedicine, or online medical care, has gained considerable attention worldwide. However, it has not been widely adopted in Japan, and the detailed status of received and provided online medical care and the reasons for its lack of popularity remain unknown. This study aims to investigate the current status of online medical care in Japan and the factors limiting its adoption from the perspective of both patients receiving and medical professionals providing online medical care. In total, 2 nationwide questionnaire surveys were conducted. The first survey, targeting both patients and healthy individuals, screened approximately 40,000 participants among 13 million people. The participants were selected to match the age distribution of the Japanese population based on government data, and their online medical care experience and medical visit status were recorded. To further investigate online medical care use and satisfaction, a web-based survey was conducted with 15% (6000/40,000) of the screened participants. The second survey, targeting medical professionals, was administered to a physician, a nurse, and a member of the administrative staff in each of 4900 randomly selected medical facilities to inquire about their online medical care practices and impressions. In addition, both surveys investigated the factors limiting online medical care expansion in Japan. The response rates among patients and healthy individuals targeted for the screening and main surveys were 92.5% (36,998/40,000) and ˃80% (1312/1478, 88.77%; 1281/1522, 84.17%; 404/478, 84.5%; and 2226/2522, 88.26% in 4 survey groups), respectively. The survey of medical professionals yielded 1552 responses (n=618, 39.82% physicians; n=428, 27.58% nurses; n=506, 32.6% administrative staff). Although the facility-level response rate was low (794/4900, 16.2%), some facility categories had relatively high response rates. Only 5.29% (1956/36,998) of the patients and healthy individuals had online medical care experience. When there were more hospitals nearby and they felt it was more work to see a physician in person, they were more likely to use online medical care (more nearby hospitals: adjusted odds ratio [aOR] 1.33, 95% CI 1.18-1.50; more work: aOR 1.48, 95% CI 1.35-1.63 per survey response point in the patient group). Similarly, these factors were substantially associated with satisfaction (more nearby hospitals: aOR 1.40, 95% CI 1.14-1.73; more work: aOR 1.50, 95% CI 1.27-1.76 per survey response point in the patient group). In both surveys, the most frequently selected factor preventing the widespread use of online medical care was patients' need to switch to face-to-face medical care for mandatory tests and procedures. Inadequate awareness of and education on online medical care were also frequently selected. Our nationwide surveys provided insights into the current status of online medical care in Japan and simultaneously identified several problems and issues related to it, which will be useful in promoting its wider adoption.

Examining the challenges impeding the utilization of construction materials with lower embodied carbon in the construction sector

ABSTRACT This paper aimed at determining the barriers preventing the extensive utilization of innovative materials in the construction sector. As a way of providing clear understanding of the complex interrelationship between the barriers, the literature review was supplemented by an expert-based survey to determine the barriers, whereas the hierarchical structure between the barriers was analyzed using the interpretive structural modelling (ISM) approach. Based on the outcome of the level partitioning of the ISM approach, it was found out that “reluctance to take responsibility for embodied carbon reduction (B14) was partitioned at the 8th level indicating that it is the most highly prioritized barrier considered to be the most crucial in preventing the use of low-carbon building materials in the construction industry. The paper produces vital knowledge and evidence that could be applied to encourage more sustainable construction practices by the wide adoption of alternative construction materials with lower embodied carbon in the construction sector. The implications inferred based on the findings of this paper seek to suggest remedies, strategies, and actions that could help to overcome the undesirable impact of the barriers that affect the utilization of low-carbon building materials, particularly amongst prominent stakeholders of construction projects.

Challenges and Opportunities in the Adoption of Flexible Manufacturing Systems in Indian Manufacturing Industries

The manufacturing sector is increasingly adopting automation technologies to enhance productivity and flexibility. Among these technologies, Flexible Manufacturing Systems (FMS) offer substantial benefits, such as reduced lead times, improved production flexibility, and lowered operational costs. Despite its advantages, FMS adoption in Indian manufacturing industries has been slow due to several barriers, including high capital costs, technological complexity, and inadequate government support. This study investigates the current status of FMS adoption in India, identifies the key barriers hindering its implementation, and proposes a decision-making model to facilitate its wider adoption. The research gap highlights the lack of a comprehensive framework to quantify the barriers and assess the performance of FMS adoption. To address this, a two-phase Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methodology was used. This approach allowed for the classification and prioritization of barriers, as well as the development of a model for FMS selection based on statistical analysis. A case study was conducted using this methodology to evaluate various FMS models and provide a systematic decision-making framework for manufacturers. The results revealed that the most significant barriers to FMS adoption in India include high upfront costs, insufficient infrastructure, and the challenges posed by rapidly advancing FMS technology. By applying the AHP-TOPSIS approach, this study offers insights into overcoming these barriers and presents actionable recommendations for manufacturers and policymakers. The findings emphasize the need for increased government support and industry collaboration to accelerate FMS adoption in India. This research contributes to the development of a structured decision-making framework, which could help manufacturers implement FMS more effectively, thereby enhancing competitiveness in an increasingly dynamic global market. Keywords— Flexible Manufacturing Systems, Barriers to FMS, Indian Manufacturing, AHP-TOPSIS, Decision-Making Model

Exploring the potential of biohydrogen as a sustainable energy solution for Pakistan: A review of production processes and policy challenges

Due to growing carbon dioxide emissions and the depletion of fossil-based energy sources, international studies have been focusing more intensely on developing hydrogen as a source of renewable energy. Biohydrogen is an exciting and green alternative energy source with various different benefits. Unlike traditional energy sources, biohydrogen production does not emit pollutants, making it a more environmentally friendly option. Biohydrogen generation, particularly from microalgae and other low-cost biomass feedstocks, so offers the dual benefit of delivering clean electricity while absorbing Carbon Dioxide. This research investigates the possibility of biohydrogen as a sustainable energy solution for Pakistan's climate issue. We examine the current state of biohydrogen generation technologies, such as dark fermentation, photo fermentation, bio photolysis, with a focus on their potential application in Pakistan. The study investigates global advances in biohydrogen research and their significance in Pakistan, demonstrating biohydrogen's potential contribution to Pakistan's energy independence and environmental targets, as well as the practical and policy-related challenges that must be addressed to advance biohydrogen energy generation and wider adoption.

Changes in Customer Behavior Towards Video Advertising Post-Pandemic

Background: Video Advertising (VI) is a powerful media tool used by several companies as a marketing strategy. During COVID-19 pandemic, there was a wide adoption of digital media, particularly VI, to promote company products. However, some changes occurred post-pandemic, which influenced customer behavior. Objective: This research aimed to explore changes in customer behavior towards VI post-pandemic. The exploration focused on understanding changes in four major factors which included Sensory Appeal (SEN), Informativeness (INF), Entertainment (ENT), and Telepresence (PRE). Methods: Data were collected using snowball sampling method, resulting in 744 responses. After deleting outliers and non-shopping customer, there were 584 analyzable data. Covariance-Based Structural Equation Model (CB SEM) method facilitated by Lisrel Application was used for data analysis. Results: The result showed that significant changes have occurred in customer behavior to VI post-pandemic. Among the 13 tested hypotheses, 11 showed significant influences, while 2 did not, indicating shifts in customer behavior. Conclusion: COVID-19 pandemic led to significant changes and imparted customer with a new understanding of VI, which became a major marketing tool. These changes were due to experiences during the pandemic, which affected SEN (72%), INF (77%), ENT (76%), and PRE (70%). Further analysis showed that ENT affected Customer Trust (CT) and Actual Purchase (APU) by 20% and 27%, while PRE caused 34% and 20% respectively, indicating a decrease in customer response from VI to CT and APU. Based on these results, further exploration should build on the identified factors and investigate additional variables that had not been considered. Keywords: Post-Pandemic, COVID-19, Customer Behavior, Video Advertising

Service similarity measurement integrating Bi-LSTM contextual representation and attention mechanism for web service discovery

With the rapid growth and wide adoption of web services, service discovery is becoming commonly used to locate the optimal services that can meet the requirements of users. This paper proposes a bidirectional long short-term memory (Bi-LSTM) service discovery method that integrates contextual representation and attention mechanisms for web service discovery. To conduct the study, FastText word embedding with the N-gram feature is primarily used as the input of the Bi-LSTM model to obtain the context feature vector of the service function description. Moreover, the attention mechanism is employed to calculate the similarity of the service function description to gain the semantic feature vector of the correlation degree between services. Then, the multilayer perceptron network is used to mine the mapping relationship between semantic feature vectors and matching levels among services. Finally, for a given query, target services are retrieved by ranking candidate services according to the prediction cores through the matching method. The proposed method is examined against multiple evaluation metrics, including accuracy and error, and compared with the state-of-the-art searching approaches. The experimental results show that the proposed method is more effective and attains better relevant web services, which helps the request service find more accurate results. Furthermore, this study provides new insights for researchers into web service discovery.

Assessing the utility of computer vision for age determination of Gulf Menhaden

AbstractObjectiveIn this work, we assess the potential of computer vision techniques for age estimation of Gulf Menhaden Brevoortia patronus scales. Scales are the primary structure used for the age determination of Gulf Menhaden, and the ageing process can be labor intensive. Gulf Menhaden is the second‐largest fishery by weight in the United States, with average annual landings from 2018 to 2022 of 449,540 metric tons, and is assessed with age‐structured models that require information about the age structure of the catch and the stock.MethodsWe used convolutional neural networks and deep neural networks to classify the age from images of Gulf Menhaden scales from three different sets of images of scales. The first set of data consists of images of scales from fish at ages 0 and 1 year. The second set of data consists of images of scales from fish at ages 0–4 years. The last set of data consists of images of scales from fish of ages 0, 1, and 2 years and includes only images of scales for which there is agreement by readers of age estimates derived from analyzing sagittal otoliths and scales from the same individual.ResultThe classification of ages was best when using a convolutional neural network model on the first data set. The poorest classification was for the model using deep neural networks with the second data set.ConclusionAlthough we show that computer vision has promise for age determination from fish scale samples, our results indicate that considerable work must be done for wide adoption of the approach. With the continuous enhancements of computer vision models, improvements in the quality of scale images, and the accumulation of larger sets of scale images that can be used to train machine learning models, we believe that using computer vision can serve to reduce processing time and increase the accuracy of age estimates.

Microstructure regulation of WxV1-xO2(B) nanorods with improved electrochemical properties

VO2(B) is regarded as a highly promising cathode material for secondary batteries due to its outstanding theoretical specific capacity and unique expansion tunnel structure, which facilitates reversible insertion and extraction of metal ions. Nonetheless, inherent structural instability, suboptimal ion/electron transport, and constrained capacity resulting from low redox potentials pose challenges to its wider adoption. Hence, in this paper, we proposed a strategy for synthesizing gradient concentration-doped WxV1-xO2(B) nanorods via a one-step hydrothermal method to solve the above problems. The experimental results demonstrated that as the W ion content increased, all fabricated WxV1-xO2(B) nanorods exhibited a monoclinic crystal phase along with noticeable lattice distortion. The positron annihilation lifetime spectra indicated that vacancy clusters were the predominant defect types in WxV1-xO2(B) nanorods. The synergistic effects of grain sizes and vacancy defects contributed to the superior electrochemical performance observed in the W0.01V0.99O2(B) sample. The capacitor retention reached 88.76 % at 0.10 A/g after 1000 charge/discharge cycles. These results had provided experimental foundation for improving electrochemical properties of WxV1-xO2(B) in metal ion secondary batteries.

Reviewing Open Data Semantic 3D City Models to Develop Novel 3D Reconstruction Methods

Abstract. Structured semantic 3D city models are pivotal in creating urban 3D digital twins. The wide adoption of such models has been primarily enabled by robust, model-based, and automatic 3D reconstruction methods. However, these methods impose requirements on the reconstruction, mainly restricting the solution space to several model types and relying on accurate 2D footprints. Recent research shows that deep-learning-based methods promise highly generic solution space and are footprint-free. Yet, the current training and test datasets are limited, hindering the methods’ development. In this work, we analyze the ubiquity of already existing, open 3D city model datasets and their potential to serve as a large-scale training and test set for 3D reconstruction, where 27 potential dataset collections have been identified. Our review shows that more than 215 million building models are readily available. We firmly believe that this review will facilitate further research on robust automatic 3D city model reconstruction and serve as a reference for benchmarking 3D city models.

Sintering of Copper Nanoparticles using Intense Pulsed Light for screen-printed films on flexible substrate

Abstract Rapid growth in applications involving large area flexible electronics has led to wide adoption of the technique of sintering printed films using intense pulsed light (IPL) due to its pulse mode operation and ability to reach high sintering temperatures without affecting the underlying substrate significantly. We study sintering mechanisms of screen-printed thin films of copper nanoparticles on polyethylene terephthalate (PET) plastic substrate by varying irradiance energy over a wide range to monitor conductivity and associated microstructure changes. We obtain optimized parameters which indicate existence of a sharp threshold irradiance energy to kickstart sintering, and two distinct regimes beyond that. The low temperature regime has a high energy barrier while the high temperature regime has a substantially reduced energy barrier with change of phase due to local melting.

Standards for Data Space Building Blocks

Data spaces are conceptualised as a trusted and secure distributed data ecosystem through which to exchange resources in the Web. Several efforts define guidance toward data space implementation, such as reference architectures and frameworks. As yet, the proposed data space solutions do not provide common and mature implementation options yet, and this gap between concept and implementation risks confusing users and developers. However, well-recognised organisations have been developing solutions and standards that address interoperability and good data exchange practices for decades, especially in the domain of geospatial information and remote sensing. Therefore, this paper compares the available solutions, providing the mapping and integration of the proposed blueprints to available interoperable standards. This concrete mapping, followed by a discussion with experts, results in a proposal of integrated reference data space building blocks, and an overview of the related standards and solutions. It is designed to support the effective practical implementation of data spaces and to guide future solution developments. This work can form the base for effective collaboration among different organisations, clearly identifying their scopes. A key role is apparent for standards and use cases from the remote sensing and geospatial domains, which have achieved wide adoption and maturity over the past years.

Towards Ensemble Feature Selection for Lightweight Intrusion Detection in Resource-Constrained IoT Devices

The emergence of smart technologies and the wide adoption of the Internet of Things (IoT) have revolutionized various sectors, yet they have also introduced significant security challenges due to the extensive attack surface they present. In recent years, many efforts have been made to minimize the attack surface. However, most IoT devices are resource-constrained with limited processing power, memory storage, and energy sources. Such devices lack the sufficient means for running existing resource-hungry security solutions, which in turn makes it challenging to secure IoT networks from sophisticated attacks. Feature Selection (FS) approaches in Machine Learning enabled Intrusion Detection Systems (IDS) have gained considerable attention in recent years for having the potential to detect sophisticated cyber-attacks while adhering to the resource limitations issues in IoT networks. Apropos of that, several researchers proposed FS-enabled IDS for IoT networks with a focus on lightweight security solutions. This work presents a comprehensive study discussing FS-enabled lightweight IDS tailored for resource-constrained IoT devices, with a special focus on the emerging Ensemble Feature Selection (EFS) techniques, portraying a new direction for the research community to inspect. The research aims to pave the way for the effective design of futuristic FS/EFS-enabled lightweight IDS for IoT networks, addressing the critical need for robust security measures in the face of resource limitations.

A complex ePrescribing antimicrobial stewardship-based (ePAMS+) intervention for hospitals: mixed-methods feasibility trial results

BackgroundAntibiotic resistant infections cause over 700,000 deaths worldwide annually. As antimicrobial stewardship (AMS) helps minimise the emergence of antibiotic resistance resulting from inappropriate use of antibiotics in healthcare, we developed ePAMS+ (ePrescribing-based Anti-Microbial Stewardship), an ePrescribing and Medicines Administration (EPMA) system decision-support tool complemented by educational, behavioural and organisational elements.MethodsWe conducted a non-randomised before-and-after feasibility trial, implementing ePAMS+ in two English hospitals using the Cerner Millennium EPMA system. Wards of several specialties were included. Patient participants were blinded to whether ePAMS+ was in use; prescribers were not. A mixed-methods evaluation aimed to establish: acceptability and usability of ePAMS+ and trial processes; feasibility of ePAMS+ implementation and quantitative outcome recording; and a Fidelity Index measuring the extent to which ePAMS+ was delivered as intended. Longitudinal semi-structured interviews of doctors, nurses and pharmacists, alongside non-participant observations, gathered qualitative data; we extracted quantitative prescribing data from the EPMA system. Normal linear modelling of the defined daily dose (DDD) of antibiotic per admission quantified its variability, to inform sample size calculations for a future trial of ePAMS+ effectiveness.ResultsThe research took place during the SARS-CoV-2 pandemic, from April 2021 to November 2022. 60 qualitative interviews were conducted (33 before ePAMS+ implementation, 27 after). 1,958 admissions (1,358 before ePAMS+ implementation; 600 after) included 24,884 antibiotic orders.Qualitative interviews confirmed that some aspects of ePAMS+ , its implementation and training were acceptable, while other features (e.g. enabling combinations of antibiotics to be prescribed) required further development. ePAMS+ uptake was low (28 antibiotic review records from 600 admissions; 0.047 records per admission), preventing full development of a Fidelity Index. Normal linear modelling of antibiotic DDD per admission showed a residual variance of 1.086 (log-transformed scale). Unavailability of indication data prevented measurement of some outcomes (e.g. number of antibiotic courses per indication).ConclusionsThis feasibility trial encountered unforeseen circumstances due to contextual factors and a global pandemic, highlighting the need for careful adaptation of complex intervention implementations to the local setting. We identified key refinements to ePAMS+ to support its wider adoption in clinical practice, requiring further piloting before a confirmatory effectiveness trial.Trial registrationISRCTN Registry ISRCTN13429325, 24 March 2022.

Enhancing production efficiency through optimizing plant density in maize-soybean strip intercropping.

Due to limited arable land resources, intercropping has emerged as an efficient and sustainable production method for increasing total grain yield per unit land area. Maize-soybean strip intercropping (MSSI) technology is being widely promoted and applied across China. However, the combination of optimal density for achieving higher production efficiency of both soybean and maize remains unclear. The objective of this study was to evaluate the differences in yield, economic benefits, land, and nitrogen (N) efficiency in MSSI systems under different densities. Five maize/soybean density combinations (67,500/97,500 plants ha-1, D1; 67,500/120,000 plants ha-1, D2; 67,500/142,500 plants ha-1, D3; 60,000/142,500 plants ha-1, D4; 52,500/142,500 plants ha-1, D5) were set under the same N input in the field experiment. The results demonstrated that optimizing the density in the intercropping system could enhance production efficiency. Increasing the density of soybean and maize significantly increased the total grain yield (D3 > D2 > D1 > D4 > D5). The D3 treatment, exhibiting the best comprehensive performance, also promoted increases in leaf area index, dry matter accumulation, and N absorption and utilization. Path analysis indicated that density had the most substantial impact on maize yield, while grain number had the greatest influence on soybean yield, with contribution rates of 49.7% and 61.0%, respectively. These results provide valuable insights into optimal field density for summer planting in MSSI, facilitating its wider adoption.

Concurrent Data Structures Made Easy

Design of an efficient thread-safe concurrent data structure is a balancing act between its implementation complexity and performance. Lock-based concurrent data structures, which are relatively easy to derive from their sequential counterparts and to prove thread-safe, suffer from poor throughput under even light multi-threaded workload. At the same time, lock-free concurrent structures allow for high throughput, but are notoriously difficult to get right and require careful reasoning to formally establish their correctness. In this work, we explore a solution to this conundrum based on a relatively old idea of batch parallelism---an approach for designing high-throughput concurrent data structures via a simple insight: efficiently processing a batch of a priori known operations in parallel is easier than optimising performance for a stream of arbitrary asynchronous requests. Alas, batch-parallel structures have not seen wide practical adoption due to (i) the inconvenience of having to structure multi-threaded programs to explicitly group operations and (ii) the lack of a systematic methodology to implement batch-parallel structures as simply as lock-based ones. We present OBatcher---a Multicore OCaml library that streamlines the design, implementation, and usage of batch-parallel structures. OBatcher solves the first challenge (how to use) by suggesting a new lightweight implicit batching design pattern that is built on top of generic asynchronous programming mechanisms. The second challenge (how to implement) is addressed by identifying a family of strategies for converting common sequential structures into the corresponding efficient batch-parallel versions, and by providing a library of functors that embody those strategies. We showcase OBatcher with a diverse set of benchmarks ranging from Red-Black and AVL trees to van Emde Boas trees, skip lists, and a thread-safe implementation of a Datalog solver. Our evaluation of all the implementations on large asynchronous workloads shows that (a) they consistently outperform the corresponding coarse-grained lock-based implementations---the only ones available in OCaml to date, and that (b) their throughput scales reasonably with the number of processors.

Percutaneous Insertion of Peritoneal Dialysis Catheters.

Patients with end-stage renal disease undergoing hemodialysis encounter significant challenges in care coordination and experience higher complication rates. Peritoneal dialysis (PD) is an evidence-based alternative that significantly improves patients' quality of life.Peritoneal dialysis catheter insertion methods include open surgical, laparoscopic, peritoneoscopic, and percutaneous image-guided approaches. Despite comparable success rates and cost-effectiveness, the US healthcare system underutilizes the percutaneous method.This article aims to provide an overview of the essential components of the technique of percutaneous peritoneal dialysis catheter insertion, as well as address patient selection nuances and considerations for urgent-start dialysis. Additionally, it reviews the outcomes and complications associated with image-guided percutaneous PD catheter placement, advocating for its wider adoption.