Initial Benchmark Research Articles

Design of a high-temperature cell for cobalt-60 irradiations of aqueous solutions with in situ UV-visible spectroscopy.

To understand the speciation of solutes in aqueous solutions in high temperature radiation environments, we report the design and fabrication of a custom-built, high temperature (≤300 °C) titanium irradiation cell with in situ optical spectroscopy capabilities, as afforded by coupled fiber optic cables. The wetted surfaces of the 8-inch tall cylindrical cell with 3.5 in. diameter are entirely made of titanium, sapphire, and gold, which are chemically and radiolytically inert. The initial benchmarking results are reported, including the baseline spectrum of deionized water as a function of temperature, the stability of a spectrum over 4h at 100 °C, and an irradiated Fricke dosimetry solution under ambient irradiator temperature conditions (27.0 ± 0.5 °C). The average gamma radiation dose rate in the cell in its current configuration is 26.1 ± 1.3 Gy min-1. This cell has application in studying several processes throughout the nuclear fuel cycle, including the reactor coolant behavior.

Customer Profitability Analysis in decision-making-The roles of customer characteristics, cost structures, and strategizing.

This study investigates the interplay between strategic goals and calculative practices, specifically Customer Profitability Analysis (CPA). Drawing on practice-based theories, the research aims to understand how managers strategize with CPA, including the balancing of financial and strategic objectives and the interplay of institutionalized practices with individual practitioners' actions. The study uses a qualitative, revelatory, and exploratory case study approach at the sub-organizational level in a manufacturing company. The researchers compare CPA practices across six departments, guided by a phenomenological research design. Data collection methods include informal conversations, qualitative observations, written documentation, numerical evidence from the accounting system, and interviews. The study offers four novel findings to the field. First, it highlights how managers employ procedural and interactive strategizing to reframe CPA practices. The sophistication of CPA practices increases with unevenly distributed customer volume, high customer-specific, controllable overhead, customer-to-customer interaction, and service complexity. Conversely, the sophistication of cost-focused CPA practices tends to decrease with diverse strategic goals. Additionally, CPA become more effective through the utilization of non-financial information, employee empowerment, localization, and strategic alignment. Second, CPA can be adapted through integrative strategizing where managers avoid using it as a financial benchmark for strategic initiatives. Third, accountants actively seek intermediary roles to incorporate arguments from strategy and marketing to balance strategic objectives-contrary to their portrayal as myopic guardians of profitability. Fourth, the localization of CPA practices to front-line employees compensates for a lack of sophisticated CPA practices. Future research should, investigate the adaptation of calculative practices in different cultures, and industries. Exploring additional contextual factors such as uncertainty, management characteristics, and linguistic framing of practices would be beneficial. Examining the interactions in utilizing CPA practices between front-line staff and customers would shed light on their effectiveness. Lastly, investigating the role of consultants in diffusing such practices would offer valuable perspectives.

Optimising Resource Utilisation: A Case Study on Oleofinos’ Advanced Line 4

This study presents significant advancements in the refinement of vegetable oils, demonstrating a 40% increase in processing capacity and a 30% reduction in production time. Additionally, energy consumption was reduced by 25%, leading to a 20% decrease in greenhouse gas (GHG) emissions. These improvements are attributed to the implementation of renewable energy technologies and heat recovery systems. With these advancements, Oleofinos has enhanced its safety and quality standards, maintaining the FSSC 22000 Certification for Food Safety Risk Management and the Rainforest Alliance Certification, which is recognised as the world’s leading certifier under the Forest Stewardship Council® (FSC®) with over 25 years of experience in environmental certification. Currently, Oleofinos is the largest plant of its kind in Mexico, boasting an unprecedented processing capacity of 500,000 tonnes per year. The results of this study underscore the significance and transformative impact of research, establishing a benchmark for future initiatives aimed at process improvement in the sector and reinforcing the commitment to operational excellence and environmental responsibility.

Outbreak prediction of COVID-19 using Recurrent neural network using Wireless Sensor Network

A multitude of outbreak prediction models are employed on a global scale to facilitate informed decision-making and the implementation of appropriate control measures in light of the COVID-19 pandemic. The authorities are now focusing on a straightforward epidemiological and statistical methodology for forecasting the global COVID-19 epidemic. These models are widely recognized within the media landscape. Long-term prediction has been found to have poor precision in typical models due to the presence of considerable uncertainty and insufficient vital data. Despite several efforts that have been undertaken to tackle this issue, there is a necessity to enhance the fundamental generalization and robustness of current models. There is a need for a comparative analysis of recurrent neural networks (RNN) and long short-term memory models (LSTM) in order to forecast the breakout of COVID-19, in comparison to the currently existing models. The architectural design incorporates the concept of the attention mechanism, specifically, the use of attention inside hidden data. This methodology enables the identification of patterns and the prioritization of time-sensitive information, leading to the creation of a network that is easily comprehensible. This research presents a proposal for utilizing RNN with LSTM to anticipate outbreaks of COVID-19. Additionally, we have incorporated a learning vector time embedding into our methodology, as the inclusion of time in a vector representation may be seamlessly integrated into many architectures. The model provides a forecast of the progression of the COVID-19 pandemic in Europe’s most severely affected nations, including Italy, France, Spain, as well as a country in North America, specifically Canada. This study proposes that variations in behavior across different nations indicate the potential efficacy of employing machine learning as a modelling tool for understanding and predicting the spread of epidemics. This paper provides an initial benchmarking analysis to demonstrate the potential of recurrent neural networks (RNNs) for future academic investigations. The study presents a unique method that combines RNNs with LSTM to effectively forecast flare-ups. The experimental results show that our proposed LSTM model performs better than existing models with lesser RMSE and MAPE values.

Evaluation of AlphaFold antibody-antigen modeling with implications for improving predictive accuracy.

High resolution antibody-antigen structures provide critical insights into immune recognition and can inform therapeutic design. The challenges of experimental structural determination and the diversity of the immune repertoire underscore the necessity of accurate computational tools for modeling antibody-antigen complexes. Initial benchmarking showed that despite overall success in modeling protein-protein complexes, AlphaFold and AlphaFold-Multimer have limited success in modeling antibody-antigen interactions. In this study, we performed a thorough analysis of AlphaFold's antibody-antigen modeling performance on 427 nonredundant antibody-antigen complex structures, identifying useful confidence metrics for predicting model quality, and features of complexes associated with improved modeling success. Notably, we found that the latest version of AlphaFold improves near-native modeling success to over 30%, versus approximately 20% for a previous version, while increased AlphaFold sampling gives approximately 50% success. With this improved success, AlphaFold can generate accurate antibody-antigen models in many cases, while additional training or other optimization may further improve performance.

Preliminary feasibility and acceptability of an interdisciplinary, single-session, telehealth class for patients with cancer-related pain.

251 Background: Cognitive behavioral therapy for pain (pain-CBT) is the standard of care for pain management. However, few patients with cancer have access to this treatment. We developed a brief (90-minute), single-session, telehealth intervention that combines medical education (e.g., pain, opioids) with pain-CBT modified for patients with cancer. We sought to quantitatively and qualitatively evaluate the preliminary feasibility and acceptability of the class for patients with cancer-related pain. Methods: Adults with chronic, cancer-related pain (≥4/10) who were receiving or recently completed treatment (<3 months) self-enrolled or were recruited from Stephenson Cancer Center (SCC) in Oklahoma between January and June 2023. We excluded patients who did not speak English or who were experiencing cognitive impairment. After consent, patients completed a baseline survey and attended the 90-minute class offered once monthly on Zoom. Following attendance, the E-acceptability survey, with additional free-text feedback, and were invited to participate in a semi-structured qualitative interview. An initial benchmark for feasibility was set at ≥70% attendance. Results: While recruitment is still ongoing, 66 participants have consented; 22 (33%) withdrew due to being too ill, no longer having pain, or being a fraudulent sign-up ( n = 4, 6%). Of the 44 (67%) remaining participants, 27 (61%) have attended the class; remaining participants are scheduled for future attendance. Participants were 68% female, 89% white, 55 ( SD = 13.1) years of age, and diagnosed with various cancer types (gynecologic 32%, gastrointestinal 20%, head/neck 11%, etc.). Eleven participants (25%) lived in rural Oklahoma. Thirty (68%) participants were taking opioids (short-acting=57%, 48% long-acting=48%, both=36%). Average pain intensity (Brief Pain Inventory) was rated as 6.0/10 ( SD = 1.9). Attendees rated their overall satisfaction with the class as 4.50/5. Participants enjoyed the class (4.15/5), reported learning about pain (4.23/5), and found the material useful (4.31/5) and helpful (4.12/5). Attendees found the class enjoyable and informational, with one stating, “this class [gave] me some tools to better manage my pain...and tools to be able to find help if I need it” (65-year-old, F). Conclusions: To date, participants’ attendance in this single-session, interdisciplinary, telehealth cancer pain class was below our feasibility benchmark, with many requesting to attend future sessions or withdrawing due to being too ill or being lost to follow up. However, among those who have attended the class, they reported very high acceptability and satisfaction. Using qualitative interview feedback, we will tailor the intervention (e.g. more cancer-specific examples, provide a recording) to improve accessibility to this scalable, comprehensive cancer pain education class.

Collaborative Governance in an Effort to Strengthen the Corporate Social Responsibility Program in the Health Field in the City of Bandung

This research aims to determine the implementation of the company's Corporate Social Responsibility (CSR) or Environmental Social Responsibility (TJSL) Program in the health sector in Bandung City, which to date has not gone according to expectations. The research method that the author uses in this research is descriptive analysis with a qualitative approach. Data collection was carried out by means of observation, in-depth interviews and documentation studies. Meanwhile, the data analysis technique used was Analytical Hierachy Process (AHP), with the aim of measuring the strength, priority and consistency of various criteria and alternatives in each dimension, namely focusing on collaborative research. Governance is determined by the Context, Driver and Collaboration Dynamics system. The results of the research state that the Corporate Social Responsibility Program in the health sector in Bandung City has been running well. To improve CSR programs more effectively, this can be done by prioritizing Collaborative Process factors which are supported by Drivers, which are also influenced by System Contex. Collaborative Dynamics factor, an important aspect to prioritize is increasing Capacity for Joint Action which is supported by Shared Motivation based on Principled Engagement, then Collaboration Dynamics itself is supported by the existence of Drivers who influence it.System Contextsin this case it influences all factors (collaboration dynamics and drivers). Referring to the findings, aspects that need to be emphasized are attention to Resource Conditions and Socio-economic/Cultural Health & Diversity. This can all be realized well with Network Connectedness, Levels of Conflict/Trust, and Political Dynamics/Power Relations that are in harmony. The appropriate model related to Collaborative Governance as an effort to strengthen the Corporate Social Responsibility program in the health sector in Bandung City is to optimize and modify the model adopted from Emerson & Nabatchi (2015b) and Ansell & Gash (2008). The results of this adoption can in turn become an initial benchmark in implementing other CSR programs or other similar programs, both in the city of Bandung and in other regions.

The Case for Administrative Benchmarks (and Some Challenges)

Summary Most U.S. Medicare beneficiaries are in population-based programs where spending targets, known as benchmarks, are used to determine payments. These include both the Medicare Advantage (MA) program, which relies on private health plans to manage care, and Accountable Care Organization (ACO) models, where providers are rewarded if they efficiently deliver care. When well designed, these models can incentivize reductions in spending and improve patient outcomes. A crucial aspect of these models, however, is how the benchmarks are determined. Policy makers have multiple approaches at their disposal, and each comes with unique strengths and weaknesses. The most common benchmark setting approach is empirical in that benchmarks are tied to actual spending or near-term forecasts based on lagged spending in fee-for-service traditional Medicare (TM). Empirical benchmarks reduce the risk that plans and providers face from fluctuations in costs that are beyond their control. That risk is absorbed by Medicare, so program spending will rise or fall with TM spending. Empirical benchmark systems have several significant weaknesses — including a ratchet effect and a collective success problem — that make it difficult for plans and providers to share in savings over the long run. This limits the long-term success of these population-based models. In contrast, administrative benchmarks are set by taking a base rate (such as historical spending) and growing it by an administrative factor that can reflect policy goals, anticipated volume, and intensity growth, or a socially agreed-upon rate, such as gross domestic product growth. The key feature is that, without policy action, the benchmark trajectory is not influenced by realized spending trends. An example of the administrative benchmark approach is the Accountable Care Prospective Trend component of the benchmarks for ACOs in the Medicare Shared Savings Program, which uses an initial benchmark, based on baseline spending, that grows based on spending growth that was forecast when the program was launched rather than realized spending growth. This administrative benchmark allows benchmark growth to diverge from, rather than track with, realized spending growth, which avoids shortcomings of empirical benchmarks and allows for greater incentives to deliver care efficiently, providing predictability over time. The key to success for administrative benchmarks is that the administrative growth rate needs to be set low enough so that Medicare saves money relative to what would happen without ACOs or MA, but high enough to still encourage participation.

Development of a Laboratory-Scale Test Methodology for Performance Evaluation of Lubricants for Hot Stamping of an Aluminium Alloy

In hot stamping of aluminium, the need for efficient methods to evaluate, compare, and rank lubricants based on their tribological performance is critical in the early stages of selection. Pilot and simulative testing can be costly, time-consuming, and complex, making it inefficient for initial benchmarking. This work aims to develop a test methodology to assess lubricant performance for hot stamping under key operating conditions without fully simulating the forming process. The proposed method distinguishes the impact of temperature on lubricant degradation, friction, wear response, and cleanability. The tests utilised a conventional hot work tool steel and a 6010S aluminium alloy with two commercially available lubricants: a polymeric lubricant and a lubricant containing graphite. The tribological tests involved a reciprocating, sliding flat-on-flat configuration at two temperatures (100 °C and 300 °C). The methodology showed that the graphite-containing lubricant exhibited over a four times lower friction coefficient than the polymer-based lubricant at 10 wt.% concentration and 300 °C. At 100 °C, both lubricants provide lubrication and can be cleaned, but increasing temperature led to a significant decline of both aspects. The observed temperature range where the lubricants degrade was between 120 °C and 170 °C.

Leveraging the OECD Framework to Create Policies That Enable Eco-Innovation: A Case study of Neom

Amid escalating urbanization worldwide, cities confront the imperative to reconcile economic growth and environmental sustainability. This study employs a comparative analysis to evaluate the applicability of the Organization for Economic Cooperation and Development’s (OECD) seminal eco-innovation policy framework within Saudi Arabia’s emergent Neom City. The OECD framework delineates a robust policy ecosystem encompassing economic incentives, regulatory standards, market facilitation mechanisms, and knowledge exchange networks to catalyze eco-innovation. Initial benchmarking reveals lacunae within Neom’s extant sustainability planning, including limited financial incentives for green technology adoption and nascent sustainability education programs. Targeted integration of OECD prescriptions could potentiate Neom’s eco-innovation across multifaceted domains spanning renewable energy integration, energy efficiency, green infrastructure, and sustainable mobility. Urban policymakers can operationalize elements of the OECD’s model to align Neom’s development objectives with ecological sustainability, propelling its ambitions to manifest as an pioneering sustainable city. This research proffers strategic insights that can enable urban governance actors worldwide to leverage policy architectures to synergize economic growth and sustainability.

Secure integration of extremely resource-constrained nodes on distributed ROS2 applications

Background: modern robots employ artificial intelligence algorithms in a broad ange of applications. These robots acquire information about their surroundings and use these highly-specialized algorithms to reason about their next actions. Despite their effectiveness, artificial intelligence algorithms are highly susceptible to adversarial attacks. This work focuses on mitigating attacks aimed at tampering with the communication channel between nodes running micro-ROS, which is an adaptation of the Robot Operating System (ROS) for extremely resource-constrained devices (usually assigned to collect information), and more robust nodes running ROS2, typically in charge of executing computationally costly tasks, like processing artificial intelligence algorithms. Methods: we followed the instructions described in the Data Distribution Service for Extremely Resource Constrained Environments (DDS-XRCE) specification on how to secure the communication between micro-ROS and ROS2 nodes and developed a custom communication transport that combines the application programming interface (API) provided by eProsima and the implementation of the Transport Security Layer version 1.3 (TLS 1.3) protocol developed by wolfSSL. Results: first, we present the first open-source transport layer based on TLS 1.3 to secure the communication between micro-ROS and ROS2 nodes, providing initial benchmarks that measure its temporal overhead. Second, we demystify how the DDS-XRCE and DDS Security specifications interact from a cybersecurity point of view. Conclusions: by providing a custom encrypted transport for micro-ROS and ROS2 applications to communicate, extremely resource-constrained devices can now participate in DDS environments without compromising the security, privacy, and authenticity of their message exchanges with ROS2 nodes. Initial benchmarks show that encrypted single-value messages present around 20% time overhead compared to the default non-encrypted micro-ROS transport. Finally, we presented an analysis of how the DDS-XRCE and DDS Security specifications relate to each other, providing insights not present in the literature that are crucial for further investigating the security characteristics of combining these specifications.

Cleaner production auditing for plastic recycling industry in Pakistan: A baseline study

Recycling plastics is a good alternative to manage the plastic waste generated in Pakistan. Unfortunately, the country lacks efficient system to manage or recycle the plastic waste it generates. Lack of government support, absence of standard operating procedures, negligence towards health and safety of workers, increasing costs of raw materials and poor quality of the recyclates are some of the issues currently faced by plastic recyclers in Pakistan. Considering the need of cleaner production audits in plastic recycling industries, this study was carried out to establish an initial reference benchmark. Production processes in 10 recycling industries were evaluated from cleaner production perspective. The study showed the average water consumption of a recycling industry as high as 3315 L/ton. All the consumed water is wasted in the nearby community sewer while, only 3 recyclers recycled between 70 and 75% of the treated wastewater. In addition, a recycling facility, on an average, consumed 172.5 kWh of power for processing 1 ton of plastic waste. The average temperature was observed to be 36.5 °C and noise levels exceeded the permissible limits. Moreover, the industry is male-dominated, workers are mostly underpaid and have no access to good healthcare facilities. Recyclers lack standardization and have no national guidelines to follow. Guidelines and standardization of recycling process, wastewater treatment, use of renewable energy, water reuse etc, are direly needed for uplifting this sector and reducing its impacts on the environment.

Pressure ulcer prevention in practice.

Pressure ulcer avoidance in the context of care has the potential to improve the quality and longevity of lives for those living in residential and non-residential care home settings. This paper reports on an educational intervention in the North East of England, which focused on the initial benchmarking of self-reported knowledge levels of healthcare workers, who regularly work with people living within this context. Using a longitudinal study design, a series of three questionnaires were used to collate data from research participants. The study revealed a disparity between what healthcare workers perceived their knowledge to be versus what it was, in terms of application to practice at the front line of patient care. The study reveals that confidence levels in dealing with pressure ulceration had been significantly altered by the training session that was being implemented. It also raised concerns on the methodological approaches being used in the education and training of care staff, which should ideally lead them to be proactive with patients in their care. The study provides an insight into the need for a strategic and targeted approach to pressure ulceration avoidance education, which is tailored to individual learning needs through supervision and mentorship as part of clinical education.

Implementing Stress-Reducing Veterinary Care: Perceptions of Australian Veterinary Professionals Working with Dogs

ABSTRACT Veterinary care that incorporates a proactive, practical approach to reducing fear and stress (often referred to as stress-reducing or low-stress veterinary care) in companion animals in the veterinary context is becoming increasingly prevalent. However, the attitude of veterinary professionals toward these techniques is not well understood. This mixed-methods study utilized an online survey to provide an initial benchmark of the certifications for stress-reducing veterinary care within the Australian veterinary industry and to gauge the attitude and experience of veterinary professionals (n = 291, 91% female) toward reducing stress during veterinary care for domestic dogs. One in five (n = 56) participants reported having a stress-reducing veterinary care certification (e.g., Fear-FreeTM Veterinary Professional, or similar). Respondents generally held a positive attitude toward stress-reducing veterinary care. Factors that predicted a positive attitude toward stress-reducing veterinary care included attitudes to animals (F = 7.83, p = 0.006) and whether the participant held a certification in stress-reducing veterinary care (F = 4.73, p = 0.031). Professionals with a certification reported higher frequency of use of stress-reducing techniques in general (p = 0.005) and of specific techniques (p = 0.001) in comparison with those without a certification. Commonly reported strategies included handling and interactions, training and distractions, environment, patient management, pharmaceutical interventions, and preventative strategies. Reported barriers to implementation centered on themes of workplace and practice management, colleagues, clients, clinic environment, work-specific conflict, personal abilities, and patients. While stress-reducing veterinary care is important to veterinary professionals, implementing strategies to improve patient welfare as part of routine practice likely still requires significant changes in workplace and industry culture.

Design and Feasibility Study of Novel Flying Wing Carrier for Launching Small Satellites in Low Earth Orbit

In recent years, there has been a considerable growth in the demand for low-orbit satellites, leading to a need for more flexible and cost-effective launch systems. This study presents a low-cost “carrier-launcher” configuration designed for space missions in low earth orbit. The carrier is a remote-controlled unmanned flying wing that can fulfil the role of the first stage of a multi-stage earth-to-orbit launcher rocket. Making the carrier a flying wing increases its effectiveness and efficiency compared to other state-of-the-art options. The flying wing architecture allows for a significantly lighter carrier compared to the traditional aircraft. The launcher is carried on the wing’s upper surface and is released during a high-altitude almost “zero g” parabolic manoeuvre. A state-of-the-art analysis has been conducted to initialize and develop the carrier’s conceptual configuration. The aerodynamics and flight mechanics of the flying wing carrier were studied using the potential aerodynamic code Athena Vortex Lattice. The high-altitude launcher’s release manoeuvre has been investigated to properly assess the required installed thrust. Finite element analyses were also performed using NASTRAN to preliminarily evaluate the aeroelastic behaviour of the proposed “carrier-launcher” configuration. The overall results show the conceptual feasibility of the flying wing carrier for launching small satellites in low earth orbit. This study provides valuable insights into the development of cost-effective launch systems for the growing demand in the low-orbit satellite sector. Our proposed design has a maximum take-off mass of 122,000 kg, uses 4 Rolls-Royce UltraFan model engines, has a wingspan of 54 m, and can carry a 10,000 kg launcher to put a 460 kg payload in LEO. As it is an initial conceptual study, this investigation establishes an initial benchmark for forthcoming inquiries, hence providing a starting point of a breakthrough concept to foster its future development.

A compendium of stable hotspots in the CHO genome.

The use of targeted integration for industrial CHO cell line development currently requires significant upfront effort to identify genomic loci capable of supporting multigram per liter therapeutic protein production from a limited number of transgene copies. To address this barrier to widespread adoption, we characterized transgene expression from thousands of stable hotspots in the CHO genome using the Thousands of Reporters Integrated in Parallel high-throughput screening method. This genome-scale data set was used to define a limited set of epigenetic properties of hotspot regions with sizes on the order of 10 kb. Cell lines with landing pad integrations at eight retargeted hotspot candidates consistently exhibited higher transgene mRNA expression than a commercially viable hotspot in equivalent culture conditions. Initial benchmarking of NISTmAb and trastuzumab productivity from one of these hotspots yielded mAb productivities of approximately 0.7-2 g/L (qP range: 2.9-8.2 pg/cell/day) in small-scale fed-batches. These findings indicate the list of hotspot candidates identified here will be a valuable resource for targeted integration platform development within the CHO community.

Uncovering and Filtering Industrial Symbiosis Networks for Carbon Dioxide Utilization in Trinidad and Tobago

The emission source profile of the small island developing State of Trinidad and Tobago (T&T) is unique amongst high national CO2 emitters per unit of GDP. To consider appropriate options for sustainably reusing CO2 in an industrial symbiosis network (ISN), a simple enterprise input-output model of CO2 exchanges between representative plants in a petrochemical cluster at the Point Lisas Industrial Estate in T&T was created. Then multi-objective optimization (MOO) was performed, so that both CO2 emissions and levelized transportation costs would be enhanced. Initial benchmarking uncovered the potential to reuse 17% of the process CO2 emissions in chemical manufacturing on the representative petrochemical network. Next, the effect of adding a CO2-reusing propylene carbonate plant to the ISN was examined. To determine how to add the propylene carbonate plant, MOO was reapplied to an updated network model, to filter all the possible ISNs. These MOO results suggest the addition of a CO2-reusing plant would increase the mass of CO2 reused in the ISN by 6.3%. Thus, eco-connectance, which is a measure of the level of IS, would be increased by 35%. The proposed changes could improve multiple Sustainable Development Goal (SDG) indicators concomitant with SDGs 8, 9, 12 and questionably 13.

Development of a COVID-19-Related Anti-Asian Tweet Data Set: Quantitative Study.

Since the advent of the COVID-19 pandemic, individuals of Asian descent (colloquial usage prevalent in North America, where "Asian" is used to refer to people from East Asia, particularly China) have been the subject of stigma and hate speech in both offline and online communities. One of the major venues for encountering such unfair attacks is social networks, such as Twitter. As the research community seeks to understand, analyze, and implement detection techniques, high-quality data sets are becoming immensely important. In this study, we introduce a manually labeled data set of tweets containing anti-Asian stigmatizing content. We sampled over 668 million tweets posted on Twitter from January to July 2020 and used an iterative data construction approach that included 3 different stages of algorithm-driven data selection. Finally, we found volunteers who manually annotated the tweets by hand to arrive at a high-quality data set of tweets and a second, more sampled data set with higher-quality labels from multiple annotators. We presented this final high-quality Twitter data set on stigma toward Chinese people during the COVID-19 pandemic. The data set and instructions for labeling can be viewed in the Github repository. Furthermore, we implemented some state-of-the-art models to detect stigmatizing tweets to set initial benchmarks for our data set. Our primary contributions are labeled data sets. Data Set v3.0 contained 11,263 tweets with primary labels (unknown/irrelevant, not-stigmatizing, stigmatizing-low, stigmatizing-medium, stigmatizing-high) and tweet subtopics (eg, wet market and eating habits, COVID-19 cases, bioweapon). Data Set v3.1 contained 4998 (44.4%) tweets randomly sampled from Data Set v3.0, where a second annotator labeled them only on the primary labels and then a third annotator resolved conflicts between the first and second annotators. To demonstrate the usefulness of our data set, preliminary experiments on the data set showed that the Bidirectional Encoder Representations from Transformers (BERT) model achieved the highest accuracy of 79% when detecting stigma on unseen data with traditional models, such as a support vector machine (SVM) performing at 73% accuracy. Our data set can be used as a benchmark for further qualitative and quantitative research and analysis around the issue. It first reaffirms the existence and significance of widespread discrimination and stigma toward the Asian population worldwide. Moreover, our data set and subsequent arguments should assist other researchers from various domains, including psychologists, public policy authorities, and sociologists, to analyze the complex economic, political, historical, and cultural underlying roots of anti-Asian stigmatization and hateful behaviors. A manually annotated data set is of paramount importance for developing algorithms that can be used to detect stigma or problematic text, particularly on social media. We believe this contribution will help predict and subsequently design interventions that will significantly help reduce stigma, hate, and discrimination against marginalized populations during future crises like COVID-19.

BUS-Set: A benchmark for quantitative evaluation of breast ultrasound segmentation networks with public datasets.

BUS-Set is a reproducible benchmark for breast ultrasound (BUS) lesion segmentation, comprising of publicly available images with the aim of improving future comparisons between machine learning models within the field ofBUS. Four publicly available datasets were compiled creating an overall set of 1154 BUS images, from five different scanner types. Full dataset details have been provided, which include clinical labels and detailed annotations. Furthermore, nine state-of-the-art deep learning architectures were selected to form the initial benchmark segmentation result, tested using five-fold cross-validation and MANOVA/ANOVA with Tukey statistical significance test with a threshold of 0.01. Additional evaluation of these architectures was conducted, exploring possible training bias, and lesion size and typeeffects. Of the nine state-of-the-art benchmarked architectures, Mask R-CNN obtained the highest overall results, with the following mean metric scores: Dice score of 0.851, intersection over union of 0.786 and pixel accuracy of 0.975. MANOVA/ANOVA and Tukey test results showed Mask R-CNN to be statistically significant better compared to all other benchmarked models with a p-value>0.01. Moreover, Mask R-CNN achieved the highest mean Dice score of 0.839 on an additional 16 image dataset, that contained multiple lesions per image. Further analysis on regions of interest was conducted, assessing Hamming distance, depth-to-width ratio (DWR), circularity, and elongation, which showed that the Mask R-CNN's segmentations maintained the most morphological features with correlation coefficients of 0.888, 0.532, 0.876 for DWR, circularity, and elongation, respectively. Based on the correlation coefficients, statistical test indicated that Mask R-CNN was only significantly different toSk-U-Net. BUS-Set is a fully reproducible benchmark for BUS lesion segmentation obtained through the use of public datasets and GitHub. Of the state-of-the-art convolution neural network (CNN)-based architectures, Mask R-CNN achieved the highest performance overall, further analysis indicated that a training bias may have occurred due to the lesion size variation in the dataset. All dataset and architecture details are available at GitHub: https://github.com/corcor27/BUS-Set, which allows for a fully reproduciblebenchmark.

The global biomass and number of terrestrial arthropods.

Insects and other arthropods are central to terrestrial ecosystems. However, data are lacking regarding their global population abundance. We synthesized thousands of evaluations from around 500 sites worldwide, estimating the absolute biomass and abundance of terrestrial arthropods across different taxa and habitats. We found that there are ≈1 × 1019 (twofold uncertainty range) soil arthropods on Earth, ≈95% of which are soil mites and springtails. The soil contains ≈200 (twofold uncertainty range) million metric tons (Mt) of dry biomass. Termites contribute ≈40% of the soil biomass, much more than ants at ≈10%. Our estimate for the global biomass of above-ground arthropods is more uncertain, highlighting a knowledge gap that future research should aim to close. We estimate the combined dry biomass of all terrestrial arthropods at ≈300 Mt (uncertainty range, 100 to 500), similar to the mass of humanity and its livestock. These estimates enhance the quantitative understanding of arthropods in terrestrial ecosystems and provide an initial holistic benchmark on their decline.