Multimodal Gait Recognition With Inertial Sensor Data and Video Using Evolutionary Algorithm

Evolutionary decision fusion has applications in biometric authentication and verification. Gray wolf optimizer (GWO) is one such evolutionary decision fusion approach that can be used to tune the fusion parameters in a multimodal data acquisition system. Human gait is a proven biometric trait with applications in security and authentication. However, acquiring human-gait data can be erroneous due to various factors and multimodal fusion of such erroneous gait data can be challenging. In this paper, we propose a new decision fusion-based approach to solve the above problem. Gait data is recorded simultaneously using motion sensors and visible-light camera. The signals of the motion sensors are modeled using a long short-term memory neural network and corresponding video recordings are processed using a three-dimensional convolutional neural network. GWO has been used to optimize the parameters during fusion. It has been chosen based on the underlying hunting strategy that leads to better approximation of the solution. Interestingly, in our case it converges quicker than other optimization techniques such as genetic algorithm or particle swarm optimization. To test the model, a dataset involving 23 males and females has been recorded while they perform four different types of walks, including, normal walk, fast walk, walking while listening to music, and walking while watching multimedia content on a mobile. An overall accuracy of 91.3% has been recorded across all test scenarios. Results reveal that the proposed study can further be explored to design robust gait biometric systems

The Place of Fieldwork in Geography and Science Qualifications

1. The place of fieldwork in geography and science qualifications across the 14-19 age range remains contested, unclear and sometimes under threat. This report explores these issues and was informed by a one-day, invitation-only workshop that we ran at the behest of the Field Studies Council. 2. We focus on issues relevant for those countries that use GCSEs (General Certificates of Secondary Education) as qualifications for 14-16 year-olds and Advanced Subsidiary (AS) and General Certificate of Education Advanced (A) levels as qualifications for 16-19 year-olds. We hope that this report will also be of value to those working in other jurisdictions that have or are introducing fieldwork at school level. 3. Fieldwork, which can be defined as any curriculum component that involves leaving the classroom and engaging in teaching and learning activities through first-hand experience of phenomena out-of-doors, has a long tradition in geography and in certain of the sciences, notably biology and environmental science/studies. 4. In geography, learning in the ‘real world’ is thought to be absolutely essential, contributing particular qualities that run through geography’s identity as a subject discipline from primary education to undergraduate study. It expresses a commitment to exploration and enquiry, and geography’s concern to discover and to be curious about the world. 5. In the sciences too, fieldwork is crucial. It can be regarded as that sub-set of practical science that is particularly valuable for introducing students to investigating the complexity and messiness of the real world. 6. Despite its benefits for student learning and motivation, fieldwork is perceived by some school managers as expendable; desirable but not a core requirement. 7. High quality qualifications in geography at GCSE and AS/A level require that students have experienced, from start to finish, a first-hand geographical investigation of a specific aspect of the world. 8. In geography, the individual study should be the method of assessment of fieldwork at AS/A level. At GCSE, where the 2014 criteria provide no option but to assess fieldwork through terminal examination, the potential of enhancing the place of fieldwork in specifications in a way that invests in curriculum and pedagogic advancement should be examined further, for example through the use of moderated student portfolios. 9. In the sciences, at both GCSE and AS/A level, it is important that practical work, of which fieldwork is a unique component, is subject to high quality assessment. The use of moderated student portfolios for the assessment of fieldwork has many strengths and should be explored to see if it can be introduced as a component within formal, summative assessment. 10. The more widespread practice of excellent fieldwork in the sciences and geography will require enhanced initial teacher education and subsequent teacher professional development. It takes time to become a teacher who can ensure that students have an outstanding fieldwork experience

Impact of an International Nosocomial Infection Control Consortium multidimensional approach on central line-associated bloodstream infection rates in adult intensive care units in eight cities in India

SummaryObjectiveTo evaluate the impact of the International Nosocomial Infection Control Consortium (INICC) multidimensional infection control approach on central line-associated bloodstream infection (CLABSI) rates in eight cities of India.MethodsThis was a prospective, before-and-after cohort study of 35650 patients hospitalized in 16 adult intensive care units of 11 hospitals. During the baseline period, outcome surveillance of CLABSI was performed, applying the definitions of the CDC/NHSN (US Centers for Disease Control and Prevention/National Healthcare Safety Network). During the intervention, the INICC approach was implemented, which included a bundle of interventions, education, outcome surveillance, process surveillance, feedback on CLABSI rates and consequences, and performance feedback. Random effects Poisson regression was used for clustering of CLABSI rates across time periods.ResultsDuring the baseline period, 9472 central line (CL)-days and 61 CLABSIs were recorded; during the intervention period, 80898 CL-days and 404 CLABSIs were recorded. The baseline rate was 6.4 CLABSIs per 1000 CL-days, which was reduced to 3.9 CLABSIs per 1000 CL-days in the second year and maintained for 36 months of follow-up, accounting for a 53% CLABSI rate reduction (incidence rate ratio 0.47, 95% confidence interval 0.31–0.70; p=0.0001).ConclusionsImplementing the six components of the INICC approach simultaneously was associated with a significant reduction in the CLABSI rate in India, which remained stable during 36 months of follow-up

Impact of varicocele repair on semen parameters in infertile men: A systematic review and meta-analysis

Purpose: Despite the significant role of varicocele in the pathogenesis of male infertility, the impact of varicocele repair (VR) on conventional semen parameters remains controversial. Only a few systematic reviews and meta-analyses (SRMAs) have evaluated the impact of VR on sperm concentration, total motility, and progressive motility, mostly using a before-after analytic approach. No SRMA to date has evaluated the change in conventional semen parameters after VR compared to untreated controls. This study aimed to evaluate the effect of VR on conventional semen parameters in infertile patients with clinical varicocele compared to untreated controls. Materials and Methods: A literature search was performed using Scopus, PubMed, Embase, and Cochrane databases following the Population Intervention Comparison Outcome (PICOS) model (Population: infertile patients with clinical varicocele; Intervention: VR [any technique]; Comparison: infertile patients with clinical varicocele that were untreated; Outcome: sperm concentration, sperm total count, progressive sperm motility, total sperm motility, sperm morphology, and semen volume; Study type: randomized controlled trials and observational studies). Results: A total of 1,632 abstracts were initially assessed for eligibility. Sixteen studies were finally included with a total of 2,420 infertile men with clinical varicocele (1,424 patients treated with VR vs. 996 untreated controls). The analysis showed significantly improved post-operative semen parameters in patients compared to controls with regards to sperm concentration (standardized mean difference [SMD] 1.739; 95% CI 1.129 to 2.349; p<0.001; I2=97.6%), total sperm count (SMD 1.894; 95% CI 0.566 to 3.222; p<0.05; I2=97.8%), progressive sperm motility (SMD 3.301; 95% CI 2.164 to 4.437; p<0.01; I2=98.5%), total sperm motility (SMD 0.887; 95% CI 0.036 to 1.738; p=0.04; I2=97.3%) and normal sperm morphology (SMD 1.673; 95% CI 0.876 to 2.470; p<0.05; I2=98.5%). All the outcomes showed a high inter-study heterogeneity, but the sensitivity analysis showed that no study was sensitive enough to change these results. Publication bias was present only in the analysis of the sperm concentration and progressive motility. No significant difference was found for the semen volume (SMD 0.313; 95% CI -0.242 to 0.868; I2=89.7%). Conclusions: This study provides a high level of evidence in favor of a positive effect of VR to improve conventional semen parameters in infertile men with clinical varicocele. To the best of our knowledge, this is the first SRMA to compare changes in conventional semen parameters after VR with changes in parameters of a control group over the same period. This is in contrast to other SRMAs which have compared semen parameters before and after VR, without reference to a control group. Our findings strengthen the available evidence and have a potential to upgrade professional societies’ practice recommendations favoring VR to improve conventional semen parameters in infertile men

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Impact of Varicocele Repair on Semen Parameters in Infertile Men: A Systematic Review and Meta-Analysis

Purpose:Despite the significant role of varicocele in the pathogenesis of male infertility, the impact of varicocele repair (VR) on conventional semen parameters remains controversial. Only a few systematic reviews and meta-analyses (SRMAs) have evaluated the impact of VR on sperm concentration, total motility, and progressive motility, mostly using a before-after analytic approach. No SRMA to date has evaluated the change in conventional semen parameters after VR compared to untreated controls. This study aimed to evaluate the effect of VR on conventional semen parameters in infertile patients with clinical varicocele compared to untreated controls.Materials and Methods:A literature search was performed using Scopus, PubMed, Embase, and Cochrane databases following the Population Intervention Comparison Outcome (PICOS) model (Population: infertile patients with clinical varicocele; Intervention: VR [any technique]; Comparison: infertile patients with clinical varicocele that were untreated; Outcome: sperm concentration, sperm total count, progressive sperm motility, total sperm motility, sperm morphology, and semen volume; Study type: randomized controlled trials and observational studies).Results:A total of 1,632 abstracts were initially assessed for eligibility. Sixteen studies were finally included with a total of 2,420 infertile men with clinical varicocele (1,424 patients treated with VR vs. 996 untreated controls). The analysis showed significantly improved post-operative semen parameters in patients compared to controls with regards to sperm concentration (standardized mean difference [SMD] 1.739; 95% CI 1.129 to 2.349; pConclusions:This study provides a high level of evidence in favor of a positive effect of VR to improve conventional semen parameters in infertile men with clinical varicocele. To the best of our knowledge, this is the first SRMA to compare changes in conventional semen parameters after VR with changes in parameters of a control group over the same period. This is in contrast to other SRMAs which have compared semen parameters before and after VR, without reference to a control group. Our findings strengthen the available evidence and have a potential to upgrade professional societies' practice recommendations favoring VR to improve conventional semen parameters in infertile men.</p

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Organic Farming for Sustainable Agriculture Using Water and Soil Nutrients

The agricultural community/farmers are struggling to obtain higher rate of yield due to lack of poor knowledge about the soil and water nutrients and suitability of the organic crop for the soil. Most of the farmers use excessive chemical fertilizers in-order to increase productivity of their yield, without aware of side effects. The excess usage of chemical fertilizers by the farmers will have impact on the quality, fertility, and salinity of the soil. To overcome these issues and to promote Digital Agriculture concept we propose an IoT enabled sensor system for monitoring soil nutrient [NPK] and pH of irrigation water to reduce the manual laboratory method of testing and get the results via mobile application and to promote organic farming in the agricultural field. Smart organic farming based mobile application will further process these nutrients value to predict and suggests the suitable crop to grow and the usage of appropriate amount of fertilizer to maintain the soil fertility there by achieving optimum usage of chemical fertilizer because continuous and wrong usage of these chemical fertilizer have a harmful effect not only on soil but also on crops, we consume leading to unhealthy human life. The proposed mobile application also helps in establishing the connection between farmers and Agricultural Produce Market Committee (APMC) in order to avoid fragmentation of profit shares and attain Pricing uncertainty and marketing of the yields by avoiding the middle man. APMC is a state government body which ensures safeguard to the farmers from exploitation by large retailers and suggest the kind of crop to be grown with organic farming. India is well known to produce organic fertilizer which is produced by the waste of slaughterhouses, plant and animal residues, biological products and other natural resources. Thus, the proposed work helps the farmers in adopting stress-free organic farming practice by self-testing their field soil parameters for generating quick soil analysis reports and also helps in connecting with APMC to know the suitable crop for their agriculture land based on the soil and water analysis (SWA) report, dispensing the required amount of organic fertilizer to the soil based on soil and water nutrients analysis using IoT enabled sensor, funding/insurance to the crops in case of occurrence of unpredictable natural disaster in future and direct marketing facility without middle man and maintain sustainable agriculture. In the present era, the industry is at 5.0 levels but agricultural production is still at 2.0 levels. In this chapter a methodology for sustainable agriculture and increase the organic yield of the organic farming using the mobile and IoT technological approaches is presented. A former can obtain the advice and other information for growing the organic crop, organic certification, pricing for the organic yield, selling and other activities by using mobile application in his/her local language. By the proposed work with the ease of mobile application the farmers can perform self-test of their field parameters for generating quick soil and water analysis report, predicts and suggest the suitable organic crop, obtaining the suitable pricing by the APMC and organic certification and agreement to meet the sustainable agriculture. Further the soil fertility of the organic farm can be monitored using IoT enabled sensors which are remotely connected with the mobile application. The experimentation is performed at different agriculture fields with organic farming at six geographical separated villages at Bagalkot district of Karnataka state, India. The different agricultural lands with variety of soil samples is tested to measure the soil parameter such as moisture, temperature, humidity and NPK nutrient values. The pH value of the irrigation water is also determined including borewell, pond, rain, river water etc. available in the reservoirs and promising sustainability in the organic yield is obtained