Search

Tractor engine performance is critical for optimizing energy efficiency in tillage operations across various soil conditions. This study introduces an artificial neural network (ANN) based novel approach for predicting and optimizing specific fuel consumption (SFC) and fuel consumption per tilled area (FCA), during tillage operations. Field tests were conducted to collect the data on rear axle torque, forward velocity, fuel consumption, throttle position, engine speed, and depth of operation under soft, medium, and hard soil conditions while using a reversible MB plough, offset disc harrow, and rigid tyne cultivator. Tractor engine performance was predicted using ANN and optimized by matching the required rear axle power with the available rear axle power. Optimal operating parameters for three distinct tillage implements, each associated with varying soil types and tillage depths, were determined based on minimizing SFC and FCA. An integrated ANN-based user advisory system was developed to provide tractor engine performance predictions and recommendations for optimal operating parameters to maximize fuel efficiency. The recommendations were validated with actual field data, resulting in Mean Absolute Percentage Errors of 7.56% and 8.98% for MB plough, 11.19% and 9.36% for offset disc Harrow, and 10.90% and 10.57% for cultivator, respectively, for SFC and FCA.Highlights ANN-based novel approach is proposed to optimize fuel efficiency during tillageAdvisory system is developed to recommend parameters for optimum tillageSuggestions for minimum SFC and FCA are provided in soft, medium and hard soilThe accuracy and reliability of advisory system are proven by field validation

Periodontitis can be defined as a multifactorial inflammatory disease of bacterial origin causing destruction of periodontal tissues. Achieving effective regeneration in periodontal therapy still remains a foremost goal. Chemical modification of tooth surface has been the most persistent effort to achieve periodontal regeneration. There are controversies regarding effectiveness of root biomodification due to a mixture of results from clinical and histological studies. This review provides an acumen about root biomodification in clinical conditions as well as a summary on the novel biomodification agents.

Abstract Across the world, the officially reported number of COVID-19 deaths is likely an undercount. Establishing true mortality is key to improving data transparency and strengthening public health systems to tackle future disease outbreaks. In this study, we estimated excess deaths during the COVID-19 pandemic in the Pune region of India. Excess deaths are defined as the number of additional deaths relative to those expected from pre-COVID-19-pandemic trends. We integrated data from: (a) epidemiological modeling using pre-pandemic all-cause mortality data, (b) discrepancies between media-reported death compensation claims and official reported mortality, and (c) the “wisdom of crowds” public surveying. Our results point to an estimated 14,770 excess deaths [95% CI 9820–22,790] in Pune from March 2020 to December 2021, of which 9093 were officially counted as COVID-19 deaths. We further calculated the undercount factor—the ratio of excess deaths to officially reported COVID-19 deaths. Our results point to an estimated undercount factor of 1.6 [95% CI 1.1–2.5]. Besides providing similar conclusions about excess deaths estimates across different methods, our study demonstrates the utility of frugal methods such as the analysis of death compensation claims and the wisdom of crowds in estimating excess mortality.

Periodontitis, a common inflammatory disease affecting the periodontal tissue, poses diagnostic and management challenges. Conventional methods are not capable of assessing disease activity or identifying individuals who are prone to progression. Biomarkers, measurable substances that indicate the presence, severity, or progression of disease, offer a promising approach to diagnosing periodontitis. Recent discoveries in molecular diagnostics have prompted the search for novel biomarkers, including cell-free DNA (cfDNA). This review article gives an insight into current knowledge regarding the use of cfDNA as a diagnostic marker in periodontal disease diagnosis.

BackgroundSpeech and language therapy involves the identification, assessment, and treatment of children and adults who have difficulties with communication, eating, drinking, and swallowing. Globally, pressing needs outstrip the availability of qualified practitioners who, of necessity, focus on individuals with advanced needs. The potential of voice-assisted technology (VAT) to assist people with speech impairments is an emerging area of research but empirical work exploring its professional adoption is limited. ObjectiveThis study aims to explore the professional experiences of speech and language therapists (SaLTs) using VAT with their clients to identify the potential applications and barriers to VAT adoption and thereby inform future directions of research. MethodsA 23-question survey was distributed to the SaLTs from the United Kingdom using a web-based platform, eliciting both checkbox and free-text responses, to questions on perceptions and any use experiences of VAT. Data were analyzed descriptively with content analysis of free text, providing context to their specific experiences of using VAT in practice, including barriers and opportunities for future use. ResultsA total of 230 UK-based professionals fully completed the survey; most were technologically competent and were aware of commercial VATs (such as Alexa and Google Assistant). However, only 49 (21.3%) SaLTs had used VAT with their clients and described 57 use cases. They reported using VAT with 10 different client groups, such as people with dysarthria and users of augmentative and alternative communication technologies. Of these, almost half (28/57, 49%) used the technology to assist their clients with day-to-day tasks, such as web browsing, setting up reminders, sending messages, and playing music. Many respondents (21/57, 37%) also reported using the technology to improve client speech, to facilitate speech practice at home, and to enhance articulation and volume. Most reported a positive impact of VAT use, stating improved independence (22/57, 39%), accessibility (6/57, 10%), and confidence (5/57, 8%). Some respondents reported increased client communication (5/57, 9%) and sociability (3/57, 5%). Reasons given for not using VAT in practice included lack of opportunity (131/181, 72.4%) and training (63/181, 34.8%). Most respondents (154/181, 85.1%) indicated that they would like to try VAT in the future, stating that it could have a positive impact on their clients’ speech, independence, and confidence. ConclusionsVAT is used by some UK-based SaLTs to enable communication tasks at home with their clients. However, its wider adoption may be limited by a lack of professional opportunity. Looking forward, additional benefits are promised, as the data show a level of engagement, empowerment, and the possibility of achieving therapeutic outcomes in communication impairment. The disparate responses suggest that this area is ripe for the development of evidence-based clinical practice, starting with a clear definition, outcome measurement, and professional standardization.

Recent years have seen significant advances in the sensing capabilities of smartphones, enabling them to collect rich contextual information such as location, device usage, and human activity at a given point in time. Combined with widespread user adoption and the ability to gather user data remotely, smartphone-based sensing has become an appealing choice for health research. Numerous studies over the years have demonstrated the promise of using smartphone-based sensing to monitor a range of health conditions, particularly mental health conditions. However, as research is progressing to develop the predictive capabilities of smartphones, it becomes even more crucial to fully understand the capabilities and limitations of using this technology, given its potential impact on human health. To this end, this paper presents a narrative review of smartphone-sensing literature from the past 5 years, to highlight the opportunities and challenges of this approach in healthcare. It provides an overview of the type of health conditions studied, the types of data collected, tools used, and the challenges encountered in using smartphones for healthcare studies, which aims to serve as a guide for researchers wishing to embark on similar research in the future. Our findings highlight the predominance of mental health studies, discuss the opportunities of using standardized sensing approaches and machine-learning advancements, and present the trends of smartphone sensing in healthcare over the years.

Abstract Background Recent cancer genome studies on many human cancer types have relied on multiple molecular high-throughput technologies. Given the vast amount of data that has been generated, there are surprisingly few databases which facilitate access to these data and make them available for flexible analysis queries in the broad research community. If used in their entirety and provided at a high structural level, these data can be directed into constantly increasing databases which bear an enormous potential to serve as a basis for machine learning technologies with the goal to support research and healthcare with predictions of clinically relevant traits. Results We have developed the Cancer Systems Biology Database (CancerSysDB), a resource for highly flexible queries and analysis of cancer-related data across multiple data types and multiple studies. The CancerSysDB can be adopted by any center for the organization of their locally acquired data and its integration with publicly available data from multiple studies. A publicly available main instance of the CancerSysDB can be used to obtain highly flexible queries across multiple data types as shown by highly relevant use cases. In addition, we demonstrate how the CancerSysDB can be used for predictive cancer classification based on whole-exome data from 9091 patients in The Cancer Genome Atlas (TCGA) research network. Conclusions Our database bears the potential to be used for large-scale integrative queries and predictive analytics of clinically relevant traits.

While Next-Generation Sequencing (NGS) can now be considered an established analysis technology for research applications across the life sciences, the analysis workflows still require substantial bioinformatics expertise. Typical challenges include the appropriate selection of analytical software tools, the speedup of the overall procedure using HPC parallelization and acceleration technology, the development of automation strategies, data storage solutions and finally the development of methods for full exploitation of the analysis results across multiple experimental conditions. Recently, NGS has begun to expand into clinical environments, where it facilitates diagnostics enabling personalized therapeutic approaches, but is also accompanied by new technological, legal and ethical challenges. There are probably as many overall concepts for the analysis of the data as there are academic research institutions. Among these concepts are, for instance, complex IT architectures developed in-house, ready-to-use technologies installed on-site as well as comprehensive Everything as a Service (XaaS) solutions. In this mini-review, we summarize the key points to consider in the setup of the analysis architectures, mostly for scientific rather than diagnostic purposes, and provide an overview of the current state of the art and challenges of the field.

Applications of neural networks have gained significant importance in embedded mobile devices and Internet of Things (IoT) nodes. In particular, convolutional neural networks have emerged as one of the most powerful techniques in computer vision, speech recognition, and AI applications that can improve the mobile user experience. However, satisfying all power and performance requirements of such low power devices is a significant challenge. Recent work has shown that binarizing a neural network can significantly improve the memory requirements of mobile devices at the cost of minor loss in accuracy. This paper proposes MB-CNN, a memristive accelerator for binary convolutional neural networks that perform XNOR convolution in-situ novel 2R memristive data blocks to improve power, performance, and memory requirements of embedded mobile devices. The proposed accelerator achieves at least 13.26 × , 5.91 × , and 3.18 × improvements in the system energy efficiency (computed by energy × delay) over the state-of-the-art software, GPU, and PIM architectures, respectively. The solution architecture which integrates CPU, GPU and MB-CNN outperforms every other configuration in terms of system energy and execution time.