Worst In Terms Research Articles

Racial/Ethnic Differences in Housing Insecurity: Evidence from the Household Pulse Survey, 2021–2022

The growing body of research of the eviction process and forced moves in general is mixed regarding racial/ethnic differences, likely resulting from focusing on past experiences with eviction, potential sampling undercoverage, and no controls for housing arrears. Our paper builds on this research and examines racial/ethnic disparities in housing insecurity—time spent in rent arrears and perceived likelihood of expulsion—using data on renters from the Household Pulse Survey. Our multivariate analyses show that blacks are significantly more likely than whites to spend a longer time being behind on rent and to expect an expulsion from their housing units, controlling for other factors. Compared to other nonwhite groups, blacks fare the worst in terms of their housing insecurity, relative to whites. Our results reveal that for blacks, long-term housing insecurity likely stems from the persistence of discriminatory housing market processes.

Evaluating the Ranking of Performance Variables in Flexible Manufacturing System through the Best-Worst Method

Flexible Manufacturing Systems (FMSs) provide a competitive edge in the ever-evolving manufacturing landscape, offering the agility to swiftly adapt to changing customer demands and product lifecycles. Nevertheless, the complex and interconnected nature of FMSs presents a distinct challenge: the evaluation and prioritization of performance variables. This study clarifies a conspicuous research gap by introducing a pioneering approach to evaluating and ranking FMS performance variables. The Best-Worst Method (BWM), a multicriteria decision-making (MCDM) approach, is employed to tackle this challenge. Notably, the BWM excels at resolving intricate issues with limited pairwise comparisons, making it an innovative tool in this context. To implement the BWM, a comprehensive survey of FMS experts from the German manufacturing industry was conducted. The survey, which contained 34 key performance variables identified through an exhaustive literature review and bibliometric analysis, invited experts to assess the variables by comparing the best and worst in terms of their significance to overall FMS performance. The outcomes of the BWM analysis not only offer insights into the factors affecting FMS performance but, more importantly, convey a nuanced ranking of these factors. The findings reveal a distinct hierarchy: the “Quality (Q)” factor emerges as the most critical, followed by “Productivity (P)” and “Flexibility (F)”. In terms of contributions, this study pioneers a novel and comprehensive approach to evaluating and ranking FMS performance variables. It bridges an evident research gap and contributes to the existing literature by offering practical insights that can guide manufacturing companies in identifying and prioritizing the most crucial performance variables for enhancing their FMS competitiveness. Our research acknowledges the potential introduction of biases through expert opinion, delineating the need for further exploration and comparative analyses in diverse industrial contexts. The outcomes of this study bear the potential for cross-industry applicability, laying the groundwork for future investigations in the domain of performance evaluation in manufacturing systems.

Produktivitas kinerja Pegawai Negeri Sipil di Kota Bitung

This research was conducted at the Girian District Office, Bitung City with the aim of analyzing the productivity of civil servant performance from the aspects of Attitude, Ability and Work Spirit as well as analyzing what obstacles are faced in preparing or creating employee performance targets. The method used is a qualitative research method. The results of this study show that 1) in terms of attitude, the performance of district staff is good and they are able to communicate well with the community; 2) In terms of efficiency, the performance of district staff remains low; 3) The performance of district employees remains the worst in terms of work ethics. The factors hindering the performance of the staff of Gillian District Office in Bitung City are: 1) lack of motivation to work, which may affect community participation in community activities; 2) County employees do not receive training or development so that they do not know what type of work they will be required to do when appointed as county employees; 3) The average age of district employees is 50 years old; 4) Existing regulations change very quickly; 5) Technical bodies still provide minimal information on the latest regulations.

Thermal conductivity and electrical resistance of carbon-based cementitious composites

The aim in this study was to produce multifunctional cementitious composites with advanced thermal and electrical performance, considering that these composites have high conductivity and can be used in, for example, deicing, electromagnetic shielding, antistatic or anticorrosion applications. Carbon fibre (CF) and carbon powder (CP) were used singly or together to develop cementitious composites with high conductivity. Electrical resistance and thermal conductivity tests were performed to measure the conductivity of the cementitious composites, while a minispreading test was applied to assess the consistency of the fresh-state cementitious composites containing conductive materials, to determine the distribution of the incorporated conductive material, and scanning electron micrographs were analysed. In addition, compressive strength tests were conducted to determine the composites’ mechanical properties. According to the test results, the lowest electrical resistance (197 Ω on the first day) was obtained from binary mixtures, while the highest thermal conductivity (1250 mW/m.K on the seventh day) was obtained from mixtures containing only CP (0.6 vol%). The mixture with 0.5% CF by volume performed worst in terms of mechanical and workability, with 20.5 MPa less compressive strength and 16 cm less minispread diameter values, compared with the control mixture.

Short-term antithrombotic strategies after left atrial appendage occlusion: a systematic review and network meta-analysis.

Background: Percutaneous left atrial appendage occlusion (LAAO) has emerged as a stroke prevention strategy in patients with nonvalvular atrial fibrillation (NVAF), and these patients were required to receive antithrombotic therapy post-procedure. However, the optimal antithrombotic strategy after LAAO remains controversial. This study explored the safety and efficacy of different antithrombotic strategies after LAAO through a network comparison method. Methods: We systematically searched the MEDLINE, Embase, and Cochrane Library databases for studies that reported the interested efficacy and safety outcomes (stroke, device-related thrombus (DRT), and major bleeding) of different antithrombotic strategies [DAPT (dual antiplatelet therapy), DOACs (direct oral anticoagulants), and VKA (vitamin k antagonist)] in patients who had experienced LAAO. Pairwise comparisons and network meta-analysis were performed for the interested outcomes. Risk ratios (RRs) with their confidence intervals (CIs) were calculated using a random-effects model. The rank of the different strategies was calculated using the surface under the cumulative ranking curve (SUCRA). Results: Finally, 10 observational studies involving 1,674 patients were included. There was no significant difference in stroke, DRT, and major bleeding among the different antithrombotic strategies (DAPT, DOACs, and VKA). Furthermore, DAPT ranked the worst in terms of stroke (SUCRA: 19.8%), DRT (SUCRA: 3.6%), and major bleeding (SUCRA: 6.6%). VKA appeared to be superior to DOACs in terms of stroke (SUCRA: 74.9% vs. 55.3%) and DRT (SUCRA: 82.3% vs. 64.1%) while being slightly inferior to DOACs in terms of major bleeding (SUCRA: 71.0% vs. 72.4%). Conclusion: No significant difference was found among patients receiving DAPT, DOACs, and VKA in terms of stroke, DRT, and major bleeding events after LAAO. The SUCRA indicated that DAPT was ranked the worst among all antithrombotic strategies due to the higher risk of stroke, DRT, and major bleeding events, while VKAs were ranked the preferred antithrombotic strategy. However, DOACs are worthy of consideration due to their advantage of convenience.

The Stability of Sidoarjo Mud Volcano Embankment Induced By The Excess Pore Water And Mud Pressure Based On Numerical Simulation

Sidoarjo mud volcano is a geology phenomenon which is the worst in terms of disaster to the people and environment. This is partly due to the overpressure at depth and as well as the cracks and fracture existence. Furthermore, the overpressurization impacts the increasing of pore water pressure and causing the reduction of effective stress. The complexity of the subsurface’s physical condition causes the instability of the embankment. This paper presents the evaluation of the stability of the embankment under the ideal and the presence of subsurface pressure conditions. The numerical simulation using Plaxis 2D has been conducted to determine the change of pore water pressure due to subsurface pressure and the safety factor of an embankment. The results show the subsurface pressure impact the increasing of pore water pressure around 208 kPa consequently. It reduces the safety factor around 4-30% from the ideal condition. The safety factor of embankment in the existing condition is still less than the minimum requirement which is 1,5 in both modeling conditions. The embankment collapse was caused by the landslide that leads upstream of the embankment as the effect of the tendency of upstream side landslides that is greater than the downstream side.

Dispersion of hydrophobic waste amorphous carbon powder in cementitious environment and its effect on the permeability of cement paste

A Proper dispersion of powder-based hydrophobic admixtures in cementitious composites is challenging due to the presence of water. Studies have shown that amorphous carbon powder (ACP), a hydrophobic by-product of refining waste materials of paraffin production can be used as a hydrophobic agent. However, mixing methods and dispersing agents for modifying cementitious materials with ACP can significantly impact their mechanical properties, workability, water-repellency (hence the durability), and dispersion quality of ACP. Lack of information about the extent of their impact on different aspects mentioned above can hinder the practical use of ACP as a hydrophobic agent. To fill this gap, the objective of this study is to comprehensively investigate factors in the dispersion of ACP in cementitious materials and the tradeoff between mechanical properties and water permeability of ACP-modified cementitious materials when more than one mixing method is adopted. The impacts of different dispersing agents and mixing methods, including ultrasonication, dry mixing, and after mixing, on dispersion quality of ACP in cementitious environment analogous with aqueous solutions and hardened cement paste were assessed. Polycarboxylate ether-based superplasticizer (PCE) proved to be the most effective dispersing agent. Compressive strength of ACP modified cement paste can be increased or decreased with or without the workability reduction effect of ACP along with PCE respectively. Based on volume fraction of ACP clusters in X-ray micro-CT 3D reconstruction of hardened cement paste, ultrasonication was identified as the most effective mixing method and dry mixing was the worst in terms of achieving a uniform distribution of ACP in cement paste. However, all mixing methods presented more than 85% volume fraction of uniformly dispersed ACP areas with no major difference in terms of hydrophobic modification capability. Modifying cement paste with a higher amounts of ACP (5 to 15% by weight of cement) substantially increased water contact angle (up to 171% for w/c ratio of 0.35) and decreased water absorption. The hydrophobic modification capability of ACP increases with a decrease in the quality of cement paste and is diminished by the presence of PCE. Consequently, without PCE, water absorption rate was reduced by up to 92% in similar ACP modified cement paste samples, which also led to a major reduction in compressive strength.

Review of the Efficiency of Ten Different Commercial Kits for Extracting DNA from Soil Mixed Biological Samples

Soil-mixed bodily fluids are the most common kind of evidence at outdoor crime scenes. This biological evidence contains DNA, which is a key component of forensic science’s ability to prove an accused person’s guilt because it connects the victim and suspect to the crime scene and aids in identifying the offender and victim. The yield of DNA is significantly influenced by factors including temperature, humidity, storage environment, time since deposition, etc. DNA degradation is caused by a variety of microbes, bacteria, humic acid, and other substances present in soil. Nowadays for DNA extraction, a variety of commercial DNA extraction kits was used now. This paper’s objective is to compare the efficiency of ten different commercial kits used to extract mixed DNA samples. It has been observed that samples stored at a low temperature (-20 °C) are the best for soil blood mixture samples. Compared to samples paired with other types of soil (silt, clay, and marshland), sand soil had the largest production of DNA using the QIAmp investigator kit (Qiagen). Blood Miniprep kit extractions were mostly inhibited, the control that amplified confirms that this kit was the worst in terms of DNA extraction potency. The samples with fewer dirt particles had a much greater yield of DNA.

DYNAMICS OF THE EXTERIOR CHARACTERISTICS OF FIRST-CALF HEIFERS DAIRY BREEDS

DYNAMICS OF THE EXTERIOR CHARACTERISTICS OF FIRST-CALF HEIFERS DAIRY BREEDS

Gender and racial disparities in the incidence and mortality of pancreatic cancer in Mississippi State from 2003 to 2019.

In the United States, the incidence and prevalence of pancreatic cancer are well-established relative to the factors of gender and race. These rates can be seen to be dictated by biological, behavioral, socio-environmental, socioeconomic, and structural factors. This paper focused on the context of Mississippi, with a particular emphasis on racial and gender-linked mortality and incidence from 2003 to 2019. Data were obtained from the Mississippi Cancer Registry. Specific parameters that were focused upon included the data source in the form of all cancer incidents and cancer mortality, geography in terms of cancer coalition regions, cancer sites in the form of the digestive system as a category to which pancreatic cancer belongs, and the year, ranging from 2003 to 2019. From the findings, the rates were more dominant in blacks than their white counterparts, suggesting racial disparity. Additionally, regardless of race, females exhibited lower rates compared to males. In the state, there were also marked geographical variations in disease incidence and mortality rates, with the Delta cancer coalition region faring the worst in terms of incidence rates for both races and genders. It was concluded that in Mississippi, being a black male poses the highest risk. In the future, certain additional factors that will need to be investigated as per their probable moderating role to inform the coining of health care interventions at the state level. They include lifestyle and behavioral factors, comorbidities, stage of disease, and geographical variations or remoteness.

Neural-fuzzy machine learning approach for the fatigue-creep reliability modeling of SAC305 solder joints

The accuracy of reliability models is one of the most problematic issues that must be considered for the life of electronic assemblies, particularly those used for critical applications. The reliability of electronics is limited by the fatigue life of interconnected solder materials, which is influenced by many factors. This paper provides a method to build a robust machine-learning reliability model to predict the life of solder joints in common applications. The impacts of combined fatigue and creep stresses on solder joints are also investigated in this paper. The common alloy used in solder joint fabrication is SAC305 (Sn–Ag–Cu). The test vehicle includes individual solder joints of SAC305 alloy assembled on a printed circuit board. The effects of testing temperature, stress amplitude, and creep dwell time on the life of solder joints were considered. A two-parameter Weibull distribution was utilized to analyze the fatigue life. Inelastic work and plastic strain were extracted from the stress–strain curves. Then, Artificial Neural Networks (ANNs) were used to build a machine learning model to predict characteristic life obtained from the Weibull analysis. The inelastic work and plastic stains were also considered in the ANN model. Fuzzy logic was used to combine the process parameters and fatigue properties and to construct the final life prediction model. Then a relationship equation between the comprehensive output measure obtained from the fuzzy system and the life was determined using a nonlinear optimizer. The results indicated that increasing the stress level, testing temperature, and creep dwell time decreases reliability. The case of long creep dwell time at elevated temperatures is worst in terms of impact on reliability. Finally, a single robust reliability model was computed as a function of the fatigue properties and process parameters. A significant enhancement of the prediction model was achieved compared to the stress–life equations.

Character traits of students purchasing a specialty in the conditions of learning in an institution of higher medical education: formation trends, features of adaptive changes, hygienic assessment

Annotation. The aim of the work was to carry out a comprehensive hygienic assessment and determine the peculiarities of the formation of the character traits of students who acquire a specialty in the conditions of study at a higher medical education institution. Scientific research involved the identification of the leading character traits of modern students who were at different stages of education (initial (1st year), intermediate (3rd year) and final (5th year) stages) and was carried out using Shmishek and Mini-mult personality questionnaires, personal 16 PF questionnaire Cattell too. The analysis of the obtained results involved the application of descriptive statistics procedures using the package of statistical analysis programs “Statistica 6.1” (license number ВXXR901E245722FA). In the course of the research, it was established that in the structure of the leading signs of accented character traits throughout the entire period of study, both among young girls and young boys, indicators prevailed, which should be divided into several blocks. According to the degree of expression, the indicators of character accentuations according to exalted, hyperthymic and cyclothymic types should be assigned to the first block, to the second block – indicators of character accentuations according to exciting, emotional, pedantic and demonstrative types, to the third block – indicators of character accentuations according to stuck, anxious and dysthymic types, and therefore the data worst in terms of content should not be considered the most pronounced. At the same time, the existence of a number of unfavourable trends, which are registered on the part of the leading properties of the character traits affect the level of functional capabilities of the students’ organism, is determined. The obtained data should be used in the future for the development of methods of comprehensive assessment of the state of adaptation resources of students and scientific substantiation of health-preserving technologies.

Assessment of romaine lettuce cultivars grown in a vertical hydroponic system at two levels of LED light intensity

Romaine lettuce (Lactuca sativa var. longifolia) was cultivated in a hydroponic system, in the free-standing container equipped with two-shelf racks. The luminous intensity of the upper shelves was 200 µmol m−2 s−1, and that of the bottom was 160 µmol m−2 s−1. The plants were exposed to light for 16 h each day. Seven cultivars from two groups were cultivated in this study. There were four cultivars from the midi subtype (‘Casual,’ ‘Pivotal,’ ‘Delcaso,’ and ‘Airton’) and three from the mini subtype (‘Xiomara,’ ‘Coventry,’ and ‘Elizium’). The growth period of lettuce in the hydroponic chamber was 30 days. Immediately after harvest, lettuce was evaluated for plant morphological parameters and for the content of macro- and microelements and metabolites with health-promoting properties. Additionally, sensory evaluation was performed and leaf color parameters were measured.The genotype of romaine lettuce had a major impact on its extrinsic and intrinsic properties. The mini cultivars produced lower fresh mass but contained higher amounts of nitrogen, phosphorus, potassium, copper, and boron than varieties from the midi group. The contents of bioactive compounds were higher for midi romaine lettuce than for the mini type. In terms of nitrate, there were significant differences, and lower nitrate levels occurred in the mini group, except in ‘Coventry.’ The results obtained in this study did not allow us to single out any cultivar from the midi group as the best or the worst in terms of chemical composition. Among the mini cultivars, ‘Elizium’ shown the tendency to accumulate a relatively small amount of nitrate and a relatively large amount of chlorophyll, carotenoid, polyphenol and flavonoid. ‘Coventry’ with the lowest chlorophyll, carotenoid, and flavonoid contents was found to be the poorest in bioactive compounds. The sensory evaluation indicated the good quality of all mini and two midi cultivars, while ‘Delcaso’ (midi cultivar) received the worst marks, because of poor smell and crispness and a bitter aftertaste. The higher light intensity (200 µmol m−2 s−1) used for plant illumination did not unequivocally increase the content of health-promoting components or improve other characteristics of romaine lettuce compared to the light 160 µmol m−2 s−1.

Benchmarking automated cell type annotation tools for single-cell ATAC-seq data.

As single-cell chromatin accessibility profiling methods advance, scATAC-seq has become ever more important in the study of candidate regulatory genomic regions and their roles underlying developmental, evolutionary, and disease processes. At the same time, cell type annotation is critical in understanding the cellular composition of complex tissues and identifying potential novel cell types. However, most existing methods that can perform automated cell type annotation are designed to transfer labels from an annotated scRNA-seq data set to another scRNA-seq data set, and it is not clear whether these methods are adaptable to annotate scATAC-seq data. Several methods have been recently proposed for label transfer from scRNA-seq data to scATAC-seq data, but there is a lack of benchmarking study on the performance of these methods. Here, we evaluated the performance of five scATAC-seq annotation methods on both their classification accuracy and scalability using publicly available single-cell datasets from mouse and human tissues including brain, lung, kidney, PBMC, and BMMC. Using the BMMC data as basis, we further investigated the performance of these methods across different data sizes, mislabeling rates, sequencing depths and the number of cell types unique to scATAC-seq. Bridge integration, which is the only method that requires additional multimodal data and does not need gene activity calculation, was overall the best method and robust to changes in data size, mislabeling rate and sequencing depth. Conos was the most time and memory efficient method but performed the worst in terms of prediction accuracy. scJoint tended to assign cells to similar cell types and performed relatively poorly for complex datasets with deep annotations but performed better for datasets only with major label annotations. The performance of scGCN and Seurat v3 was moderate, but scGCN was the most time-consuming method and had the most similar performance to random classifiers for cell types unique to scATAC-seq.

Are We Paying More to Consume Substandard Drinking Water? Crowd-Sourced Water Education Study in a South-Asian Megacity

South. Environment and health-related data are inadequate and strongly rely on governmental-mediated observation. The present work is the first of its kind attempt to gather relevant information on potable water quality using mass citizen engagement over a south Asian megacity, Kolkata. The city has a long history associated with water-borne diseases and presently numerous reports on sickness from drinking water contamination have been published in the local newspapers. The current work crowdsourced water sampling throughout the city and analyzed information regarding biological contaminants for five existing water types. 300 citizens were mobilized and trained in water collection through audio-visual communication workshops and together 570 samples of water have been collected from different parts of the city. Water sample collecting points were created over the city and samples were taken to the analysis hub where it was analyzed for standard coliform test and for total dissolved solid (TDS) and pH. The results show that the most expensive water types (20l commercialized packaged drinking water bottles) are the worst in terms of water quality. Governmental-free drinking water was found to be much safer as compared to packaged drinking water. 44% of the packaged drinking water was found to be contaminated with coliform bacteria and we did a comparative analysis with reports from different developing countries and found that such high occurrence was not reported in any cities across the world. We communicated the results to the participants and further, conducted semi-structured interviews to understand the perception of water quality and the driving force behind the choice of a particular water type. It was also found that logistical feasibility is the primary reason behind selecting the 20l bottle. The blind faith in commercially packaged drinking water has been reflected in the interview. The present studies confirm the potential of crowdsourcing of environmental-health data in the global south and successfully accomplish potable water analysis through crowdsource sampling over a south Asian megacity. Such robust methods could be easily scaled up and follow-up discussions can definitely improve water literacy among the participants in Global South.

Economic evaluation of three dialysis methods in patients with end-stage renal disease in China.

End-stage renal disease (ESRD) may result in different degrees of physical and psychological pain. Automated peritoneal dialysis (APD), continuous ambulatory peritoneal dialysis (CAPD), and hemodialysis (HD) as the main treatment methods lead to a heavy burden on social economic and family financial. However, there are few studies on the economic evaluation of the three dialysis methods in China. Cost-effectiveness analyses were performed using Markov models based on longitudinal data for 15years of different modalities in Kunshan City, China. Direct cost derived from medical insurance information system, and indirect cost referred to as loss of productivity. Sensitivity analyses were conducted to study uncertainty. The per capita total cost of CAPD was 664,027.00 yuan, the per capita utility is 5.9105. The per capita total cost of APD was 858,800.65 yuan, the per capita utility is 6.4548. The per capita total cost of HD was 1,281,213.64 yuan, the per capita utility is 6.1356. When CAPD was compared with HD, Incremental Cost-Effectiveness Ratio (ICER) was 1,323,389.53 yuan per QALY, compared with APD, ICER was 357,848.13 yuan per QALY. ICER value suggests that APD was cost-effective compared with CAPD and HD at a willingness-to-pay threshold of 538,200 yuan. Our research showed that APD is the most appropriate and HD is the worst in terms of cost-effectiveness. However, in fact, HD accounts for a high proportion in China, so some relevant policy suggestions need to be implemented to change the current situation.

Does Fitzpatrick Score Predict Flap Loss? Microsurgical Breast Reconstruction Outcomes of Varying Skin Color.

This is a retrospective study conducted on patients who underwent any type of autologous flap breast reconstruction performed by the two senior authors at an academic center between January 2010 and December 2021. The sole primary outcome variable was flap loss. Patient skin tone was assessed using the Fitzpatrick scale on clinical photographs of patients. A total of 1115 pateints underwent autologous flap breast reconstruction, of which only 56 met both exclusion and inclusion criteria with 58 individual breasts being included in the final study population. The most common race of subjects was White (n = 33; 56.9%) while the most common Fitzpatrick score skin tone was type II (n = 22; 37.9%). The Cochran-Armitage test of trend showed a statistically significant linear trend, P = 0.006, with darker Fitzpatrick score skin tones associated with a higher proportion of flap loss in patients who had vascular compromise. A logistic regression showed that none of the predictor variables were significant. Patients with darker Fitzpatrick skin tones were associated with flap loss after vascular compromise. To prevent flap loss in patients who have darker Fitzpatrick score skin tones, more aggressive flap monitoring should be taken into consideration in the immediate postoperative setting.

IMPACT OF FUTURE CLIMATE CHANGE ON RUNOFF IN LAKE ONEGO CATCHMENT AREA

The aim of this study is to predict the possible changes in runoff from Lake Onego catchment using the three largest tributaries Shuya, Suna and Vodla as a case study for assessing expected climate change in the 21st century in accordance with the greenhouse gas emission scenarios RCP 2.6 and RCP 8.5 – the best and the worst in terms of environmental impact, respectively. Information about the change in meteorological elements required for runoff calculations was obtained on the basis of the climate models MPI-ESM-MR and IPSL-CM5A. To assess the possible change in runoff as a result of changes in the regional climate, the runoff model ILHM was used. When both climate models are applied, a significant increase in air temperature in the RCP 8.5 scenario leads to an increase in the estimated values of evaporation, which largely compensates for the increase in river runoff due to increased precipitation. In the RCP 2.6 scenario, there are no significant changes in evaporation. A combination of RCP 2.6 and MPI-ESM-MR produces a slight decrease in runoff by the end of the 21st century. It can be assumed that the change in runoff from the catchment of Lake Onego will be in the interval between the estimates made in the two extreme scenarios and one should not expect extreme changes in the hydrological regime of rivers in this region.

Classification of Cotton Genotypes with Mixed Continuous and Categorical Variables: Application of Machine Learning Models

Mixed data is a combination of continuous and categorical variables and occurs frequently in fields such as agriculture, remote sensing, biology, medical science, marketing, etc., but only limited work has been done with this type of data. In this study, data on continuous and categorical characters of 452 genotypes of cotton (Gossypium hirsutum) were obtained from an experiment conducted by the Central Institute of Cotton Research (CICR), Sirsa, Haryana (India) during the Kharif season of the year 2018–2019. The machine learning (ML) classifiers/models, namely k-nearest neighbor (KNN), Classification and Regression Tree (CART), C4.5, Naïve Bayes, random forest (RF), bagging, and boosting were considered for cotton genotypes classification. The performance of these ML classifiers was compared to each other along with the linear discriminant analysis (LDA) and logistic regression. The holdout method was used for cross-validation with an 80:20 ratio of training and testing data. The results of the appraisal based on hold-out cross-validation showed that the RF and AdaBoost performed very well, having only two misclassifications with the same accuracy of 97.26% and the error rate of 2.74%. The LDA classifier performed the worst in terms of accuracy, with nine misclassifications. The other performance measures, namely sensitivity, specificity, precision, F1 score, and G-mean, were all together used to find out the best ML classifier among all those considered. Moreover, the RF and AdaBoost algorithms had the highest value of all the performance measures, with 96.97% sensitivity and 97.50% specificity. Thus, these models were found to be the best in classifying the low- and high-yielding cotton genotypes.

Achieving sustainable transformation in science - green grassroots groups need nurturing from the top.

Climate change is the greatest challenge of our time, and drastic climate action is needed urgently across industries and sectors to prevent the worst in terms of consequences. Although academic research brings great benefits to society, it leaves behind a considerable environmental footprint at the same time. This is particularly true for lab research within the life sciences. To reduce the climate impact of academic research, both bottom-up and top-down strategies are necessary. On the bottom-up side, 'green' grassroots groups are emerging in academia, but most institutions fail to nurture and harness their potential for driving change. We report findings from a survey of 63 such grassroots groups operating in academic environments, highlighting that their main challenges in making research more sustainable include lack of time, budget, involvement in management decisions and support from management. For the first time, we map the inception, goals and structure of green grassroots groups in academia and outline concrete steps in overcoming barriers to harvest their full potential, thus making academic research fit for the future.