Standard Process Research Articles

Que estandardização para o português: pluricêntrica ou panlusófona?

Portuguese is a pluricentric language with two well-established national varieties – European Portuguese (EP) and Brazilian Portuguese (BP) – and varieties in development, such as Mozambican Portuguese (MP) and Angolan Portuguese (AP). In the present study, we aim to address the normative and political question of whether the most appropriate path of standardization for Portuguese in the 21st century is a pluricentric model of several norms combined, each with its own dynamics, or a pan-Lusophone model consisting of one supranational standard norm, a sort of global Portuguese, or even a combination of the two models. We will start by analyzing past and current processes of both formal and informal standardization in Portuguese, focusing on the endo(exo)normative standardization of BP and the role of Globo TV in the strengthening of informal endonormativity, the lectometrically confirmed divergent BP-PE bicentrism and its (a)symmetries – including the more advanced endonormativity of EP and the greater global influence of BP – and the processes of nativization of African varieties, characterized by contacts with Bantu languages and EP exonormativity, with particular emphasis on the more advanced stage of nativization of MP as well as the strong population increase projected for Angola and Mozambique. We will then discuss the two models of a pluricentric or pan-Lusophone path of standardization for Portuguese, along with their opportunities and threats, and point out guidelines for their operationalization. By adopting the Cognitive Sociolinguistics’ view on pluricentricity and based on previous socio-cognitive and lectometric studies on the pluricentricity of Portuguese, we will argue for the adequacy and necessity of a socio-cognitively oriented, fully pluricentric standardization of Portuguese, which could potentially serve as a basis for the development of a pan-Lusophone norm, especially useful for the internationalization of Portuguese.

Desorientación normativa y variación gramatical en el español de Canarias

In the Spanish of the Canary Islands, it seems that the national standard coexists with a regional norm, which has yet to be defined at the morphosyntactic level. There is a lack of empirical studies on the variational status of many constructions which have been registered in the Canary Islands for decades and which differ from the European standard. The main aim of this paper is to raise the problems of normative disorientation that derive from this situation. To this end, the process of linguistic standardization initiated in the Canary Island is analyzed, a list of morphosyntactic phenomena with undefined status is presented, and, in the case of three variables, the results obtained from a sociolinguistic questionnaire addressed to Canarian university students are discussed.

112 Gbps CMOS-compatible waveguide germanium photodetector for the 2 μm wavelength band with a 3.64 A/W response

The 2 μm wavelength band emerges as a promising candidate for the next communication window to enhance the transmission capacity of data. A high-responsivity and high-speed photodetector operating at 2 μm is crucial for the 2-μm-wavelength-band communication system. Here, we present an on-chip waveguide-coupled germanium photodetector with remarkably high responsivity and data-receiving rate, employing subbandgap light absorption and avalanche multiplication. The device is designed with an ingenious and simple asymmetric lateral p-i-n junction structure and fabricated through a standard CMOS process by a commercial factory. It has a responsivity of 3.64 A/W and a maximum bandwidth of 50 GHz at 2 μm wavelength. For the first time, to the best of our knowledge, an optical receiving rate of up to 112 Gbps is demonstrated at 2 μm, verifying its feasibility in a high-speed 2-μm-band communication system. To the best of our knowledge, the proposed device stands out as the fastest photodiode with the highest responsivity among all group III-V and group IV photodetectors working in the 2 μm wavelength band.

PRODUCTION AND CHARACTERIZATION OF BIOBRIQUETTE FROM BIOMASS OF MANGO LEAF, SAW DUST AND RICE HUSK AS EFFICIENT ENERGY SOURCES FOR COOKING

The increasing demand for sustainable energy solutions has driven interest in alternative biomass sources for energy production. This study investigates the production and characterization of bio briquettes made from different biomass of mango leaves, sawdust, and rice huskusing limestone as binder. The objective is to evaluate the potential of these biomass materials as efficient energy sources for cooking. The bio briquettes were produced using a standard briquetting process, involving mixing, compaction, and drying. Various parameters including ultimate and proximate composition, calorific value, bulk density, hardness and durability as well as mechanical and combustion properties were analyzed to assess their performance. The results demonstrated that the bio briquettes exhibited high calorific values between 20753 to 20506 KJ/kg and high percentage of fixed carbon between 4.18 to 5.58 % which generally indicates good fuel performance.The results also indicated that the bio briquettes has low Sulphur content and high volatile matter content between 50.53 to 62.84 % an indication that they are ecofriendly and can easily burn to yield energy. The bio briquettes of mango leaves, sawdust, and rice husk not only leverages agricultural waste but also contributes to reducing dependency on non-renewable energy sources. This research underscores the potential of utilizing locally available biomass for sustainable energy solutions and offers insights into improving the efficiency of bio briquette production.

Guideline for medical nutritional treatment of adult patients with gastrointestinal dysfunction (2025 edition)

Gastrointestinal dysfunction is a common clinical disease. Due to abnormal gastrointestinal digestive and absorption functions, nutrients, water, and electrolytes cannot be absorbed properly, and therefore, scientific and reasonable nutritional intervention is needed. In order to further standardize medical nutrition treatment of patients with gastrointestinal dysfunction, the Chinese Society of Parenteral and Enteral Nutrition (CSPEN) organized experts and scholars in related fields in China to elaborate on the following topics based on current evidence-based medical evidence: the standard process of medical nutrition treatment for patients with gastrointestinal dysfunction, the time and mode of enteral and parenteral nutrition application, and the nutritional intervention mode for patients with gastrointestinal dysfunction in special disease state. Finally, 29 questions and 58 recommendations were formed to provide reference for the standardized application of medical nutrition therapy for patients with gastrointestinal dysfunction.

Characterization of a wiener process taking values in a Hilbert space

This paper characterize wiener process by taking values in a Hilbert space. A standard wiener process is stochastic process indesed by nonnegative real numbers t with the following properties: W0=0 With probability 1, the function t àWt is continuous in t. The process has stationary, independent increments. The increments - Ws has the NORMAL (0,t) distribution

Selection of Optimum Enhancement Technique for the Discrimination of Alkali Granites and Syenites around Suryamalai Batholith of Central Tamil Nadu, India

Image processing techniques such as Band Ratio, Principal Component Analysis, Supervised, Unsupervised classification and Reclassification techniques are generally used as an enhancement technique for alteration zone demarcation and lithological discrimination in geosciences environment. In the present study, Landsat OLI satellite imagery has been utilized to produce various combinations of image enhancement to discriminate alkali syenite and alkali granite of Proterozoic age. These techniques were also used to differentiate three phases of granites reported in Suryamalai Batholith which is very difficult to map by conventional method due to its rugged topographic nature. 12 types of enhancement techniques were applied to differentiate rock alkali syenite from granite, however only few methods such as Kaufmanns ratio, ratio of 5/7, 3/1 and 3/5 and supervised classification were found to be suitable for the discrimination of those rocky types. Band ratio 5/7, 3/1 and 3/5, Crosta analysis and thermal reclassification techniques significantly brought to light the difference between the metamorphic rocks such as fissile hornblende biotite gneiss and hornblende biotite gneiss of Archaean age. Similarly, phase I and phase III granites were perfectly discriminated from all the image enhancement techniques. It is differentiated mainly based on its grain size and mineral variation between these two rock types. Study revealed that standard digital image processing technique can be effectively used for lithological mapping.

A Review of Approaches to Standardizing Medical Descriptions for Clinical Entity Recognition: Implications for Artificial Intelligence Implementation

This article reviews the current state of standardization in specific areas of the medical sector in Poland, focusing on terminology and the unique context of the Polish language. The primary objective was to analyze the existing resources and examine possibilities, challenges, and opportunities associated with integrating Artificial Intelligence, particularly natural language processing methods, into the healthcare system. The additional goal of this review was to place Poland in the international context by comparing the current state of the Polish standardization of healthcare with those of selected countries with more and less developed systems. The exploration highlights the main challenges that impact integration, including the specificity of the language and challenges in transferring knowledge from other languages, lack of communication between parties, and lack of stakeholder involvement in the standardization processes. This review also presents potential solutions to the mentioned challenges and provides insights into future directions, possibilities, proposals, and recommendations for all stakeholders. The practical application of this research extends beyond Poland. Many countries with underrepresented languages face similar challenges in clinical data processing, and the advances in CER for Polish could serve as a model for implementing AI-driven solutions in these regions. By refining CER models and adapting them to diverse linguistic and healthcare contexts, this research can foster improvements in patient care, medical research, and healthcare administration on a global scale.

Resilience Practices in Health Science and Medical Libraries During the Early Stages of the COVID-19 Pandemic

This paper uses the concept of resilience engineering as an organizing principle to discuss best practices that evolved within health science/medical libraries in the United States during COVID-19 crisis, focusing on the period March – August 2020. Protection of library staff, assistance to medical staff, reducing the circulation of misinformation and public health consumerism all required substantial changes to standard processes. These process changes had to arise in the context of both physical isolation and information overload. Some practices became widespread due to their utility, and these are the focus of this report.

Medical students attitudes towards patients with disability: a scale validation study

BackgroundDisparities in health outcomes among persons with disabilities (PWD) are influenced by preventable factors including negative attitudes and discriminatory practices of healthcare professionals and the limited training of medical students in working with PWD.ObjectiveThe purpose of this study was to validate the “Medical Students’ Attitudes Towards Patients with Disabilities” (MSA-PWD) scale among Lebanese medical students and identify associated factors.MethodA scale validation study was conducted as part of the standard internal evaluation process at the medical school, using a cross-sectional design based on a qualitative content validity process, involving students from years 1 to 4. The study included questions from previous tools, and others encouraging students to reflect on their encounters with PWD and share their insights. Statistical procedures were applied to assess the reliability and validity of the tool, including construct validity, convergent validity, known-group validity, and trend analysis.ResultsAfter a careful selection of the related items, the MSA-PWD scale demonstrated high construct validity and internal consistency, along with good convergent validity and known-group validity. Factor 1 of the scale was able to differentiate between different enrollment years of medical students, correlating with the self-assessment tool for confidence in dealing with PWD and clinical experience scenarios.ConclusionThe “Medical Students’ Attitudes Towards Patients with Disabilities” scale validated in this study is a comprehensive instrument that can distinguish between different medical school enrollment classes. The newly developed scale was valid, and reliable.

Capability of ChatGPT to support the screening process of scoping reviews: A feasibility study

Abstract Background The time-consuming screening phase in health-related evidence syntheses is increasingly supported by artificial intelligence (AI). However, scoping reviews have not benefited as much as systematic reviews from such AI tools as they utilize conceptual rather than keyword-specific search strategies to address broad research questions. Context-understanding chatbots based on large language models could potentially enhance the efficiency of scoping review screenings. This study evaluates the performance of ChatGPT against an open-access AI tool used for abstract screening in systematic reviews and the costs involved. Methods Leveraging data from a prior scoping review, we compared the performance of ChatGPT 4.0 and 3.5 against Rayyan, using human researchers’ decisions as a benchmark. A random set of 50 included and 50 excluded abstracts was used to train Rayyan’s algorithm and to develop ChatGPT’s prompt. ChatGPT 4.0’s evaluation was repeated after 5-7 days to assess response consistency. We computed performance metrics including sensitivity, specificity, and accuracy. Results ChatGPT 4.0 and 3.5 achieved 68% accuracy, 11% precision, 99% negative predictive value, and 67% specificity. Sensitivity was high at 88-89% for ChatGPT 4.0 and 84% for ChatGPT 3.5. ChatGPT 4.0 showed a substantial interrater reliability between the two evaluations and moderate reliability compared to ChatGPT 3.5. The cost of deployment varied, with Rayyan being free, ChatGPT 3.5 costing $9.06 and ChatGPT 4.0 $505.72. Conclusions Given the exponential increase in publications, effective mechanisms to support the screening phase of scoping reviews are needed. Our feasibility study using ChatGPT to decide on abstracts’ inclusion or exclusion achieved good performance metrics. Given further positive evaluation, such chatbots might be incorporated in the standard screening process, possibly replacing a second screener, saving time and costs, and accelerating evidence synthesis. Key messages • ChatGPT performs well in supporting screening for scoping reviews, outperforming a traditional AI tool at reasonable costs. • Employing chatbots like ChatGPT could potentially cut costs and time in scoping reviews.

Patient trust in the use of machine learning-based clinical decision support systems in psychiatric services: A randomized survey experiment.

Clinical decision support systems (CDSS) based on machine-learning (ML) models are emerging within psychiatry. If patients do not trust this technology, its implementation may disrupt the patient-clinician relationship. Therefore, the aim was to examine whether receiving basic information about ML-based CDSS increased trust in them. We conducted an online randomized survey experiment in the Psychiatric Services of the Central Denmark Region. The participating patients were randomized into one of three arms: Intervention=information on clinical decision-making supported by an ML model; Active control=information on a standard clinical decision process, and Blank control=no information. The participants were unaware of the experiment. Subsequently, participants were asked about different aspects of trust and distrust regarding ML-based CDSS. The effect of the intervention was assessed by comparing scores of trust and distrust between the allocation arms. Out of 5800 invitees, 992 completed the survey experiment. The intervention increased trust in ML-based CDSS when compared to the active control (mean increase in trust: 5% [95% CI: 1%; 9%], p=0.0096) and the blank control arm (mean increase in trust: 4% [1%; 8%], p=0.015). Similarly, the intervention reduced distrust in ML-based CDSS when compared to the active control (mean decrease in distrust: -3%[-1%; -5%], p=0.021) and the blank control arm (mean decrease in distrust: -4% [-1%; -8%], p=0.022).No statistically significant differences were observed between the active and the blank control arms. Receiving basic information on ML-based CDSS in hospital psychiatry may increase patient trust in such systems.

The Application and Challenges of New Energy Vehicles in the Logistics and Distribution Sector

With the continuous enhancement of public environmental awareness and the promotion of sustainable development in logistics and distribution, the recognition of new energy vehicles in the logistics and distribution sector is gradually increasing due to their characteristics such as low carbon emissions and low noise. However, new energy vehicles face serious range anxiety, short driving range, low coverage of charging and swapping stations, long charging times, and lack of uniform standards, making it difficult for them to cope with the complex logistics and distribution environment. Additionally, the high initial investment in new energy vehicles hinders their promotion in the logistics and distribution sector. This article proposes suggestions such as increasing research and development efforts for new energy vehicles, accelerating the construction of charging and swapping station infrastructure, and strengthening industry collaboration to promote the standardization process, providing reference for the application of new energy vehicles in the logistics and distribution sector.

Research on the Influence of AI Technology and Optimization Strategy on the Chinese Manufacturing Enterprises about Digital Transformation

Abstract: The rapid evolution of the digital economy has fostered a favorable environment for the growth of the artificial intelligence (AI) industry, catalyzing a transformative shift across diverse sectors. AI technology is driving the digital transformation landscape, contributing to the emergence of a novel economic framework characterized by enhanced unity, fairness, and operational efficiency. Particularly in China's manufacturing domain, substantial governmental support has propelled the widespread adoption of AI, thereby facilitating significant strides towards industrial modernization. However, despite the promising trajectory, notable challenges persist. These include the scarcity of AI talent, concerns regarding data security such as leakage, and the ongoing refinement of compatibility with existing systems. Addressing these challenges necessitates a multifaceted approach. Efforts should prioritize bolstering personnel training initiatives in AI-related domains through collaborative engagements with industry, universities, and research institutes. This cooperative endeavor can nurture a skilled workforce adept at navigating the complexities of AI implementation. Moreover, stringent measures must be implemented to safeguard sensitive data, including robust encryption protocols, secure computing practices, and meticulous access controls to mitigate the risks of data breaches. Furthermore, promoting standardization processes and embracing data format conversion tools are critical steps towards enhancing interoperability across disparate systems. In conclusion, this discourse underscores the pivotal role of AI in driving the digital transformation journey of Chinese manufacturing enterprises. By addressing pertinent challenges and embracing strategic initiatives, these enterprises can fortify their resilience, navigate future complexities, and achieve sustainable development in the digital era.

NOise Reduction with DIstribution Corrected (NORDIC) principal component analysis improves brain activity detection across rodent and human functional MRI contexts.

NOise Reduction with DIstribution Corrected (NORDIC) principal component analysis (PCA) has been shown to selectively suppress thermal noise and improve the temporal signal-to-noise ratio (tSNR) in human functional magnetic resonance imaging (fMRI). However, the feasibility to improve data quality for rodent fMRI using NORDIC PCA remains uncertain. NORDIC PCA may also be particularly beneficial for improving topological brain mapping, as conventional mapping requires precise spatiotemporal signals from large datasets (ideally ~1 hour acquisition) for individual representations. In this study, we evaluated the effects of NORDIC PCA compared with "Standard" processing in various rodent fMRI contexts that range from task-evoked optogenetic fMRI to resting-state fMRI. We also evaluated the effects of NORDIC PCA on human resting-state and retinotopic mapping fMRI via population receptive field (pRF) modeling. In rodent optogenetic fMRI, apart from doubling the tSNR, NORDIC PCA resulted in a larger number of activated voxels and a significant decrease in the variance of evoked brain responses without altering brain morphology. In rodent resting-state fMRI, we found that NORDIC PCA induced a nearly threefold increase in tSNR and preserved task-free relative cerebrovascular reactivity (rCVR) across cortical depth. NORDIC PCA further improved the detection of TGN020-induced aquaporin-4 inhibition on rCVR compared with Standard processing without NORDIC PCA. NORDIC PCA also increased the tSNR for both human resting-state and pRF fMRI, and for the latter also increased activation cluster sizes while retaining retinotopic organization. This suggests that NORDIC PCA preserves the spatiotemporal precision of fMRI signals needed for pRF analysis, and effectively captures small activity changes with high sensitivity. Taken together, these results broadly demonstrate the value of NORDIC PCA for the enhanced detection of neural dynamics across various rodent and human fMRI contexts. This can in turn play an important role in improving fMRI image quality and sensitivity for translational and preclinical neuroimaging research.

Assessing rainfall radar errors with an inverse stochastic modelling framework

Abstract. Weather radar is a crucial tool for rainfall observation and forecasting, providing high-resolution estimates in both space and time. Despite this, radar rainfall estimates are subject to many error sources – including attenuation, ground clutter, beam blockage and drop-size distribution – with the true rainfall field unknown. A flexible stochastic model for simulating errors relating to the radar rainfall estimation process is implemented, inverting standard weather radar processing methods and imposing path-integrated attenuation effects, a stochastic drop-size-distribution field, and sampling and random errors. This can provide realistic weather radar images, of which we know the true rainfall field and the corrected “best-guess” rainfall field which would be obtained if they were observed in a real-world case. The structure of these errors is then investigated, with a focus on the frequency and behaviour of “rainfall shadows”. Half of the simulated weather radar images have at least 3 % of their significant rainfall rates shadowed, and 25 % have at least 45 km2 containing rainfall shadows, resulting in underestimation of the potential impacts of flooding. A model framework for investigating the behaviour of errors relating to the radar rainfall estimation process is demonstrated, with the flexible and efficient tool performing well in generating realistic weather radar images visually for a large range of event types.

Producing antifriction babbitt В83 layer by gas-dynamic cold spraying

The technological processing of antifriction layer from Babbitt by gas-dynamic cold spraying was carried out on the DIMET-403. The coating was sprayed on plain bearings for marine low- and medium-speed diesel engines, turbines, and shaft lines. Technical requirements for antifriction layer materials, standard technological processes of preparation, spraying, heat treatment, and quality control are also considered.

Sub-nA photovoltaic energy harvesting circuit for miniaturized battery-less sensor edges

Abstract In this letter, we report on the photovoltaic energy harvesting circuit for miniaturized, battery-less sensor devices. To accumulate nanowatt power from ultra-small photovoltaic cells, it is essential to reduce the minimum operating current of the energy harvesting circuit. To reduce the minimum operating current, we propose a dual-capacitance voltage monitor (DCVM) which can overcome the issue of the voltage drop caused by the shoot-through current for switching. We designed the energy harvesting circuit including the DCVM in a 180nm CMOS standard process, and successfully demonstrate the minimum operating current of 1 nA.

An Instructional Module for Functional Dissociative Seizures (FDS) in Hindi: Enhancing Communication and Management in India.

Functional dissociative seizures (FDS) are very common in neurology clinics, they straddle both neurology and psychiatry. In India, scarce resources and guidelines are available for effectively conveying their diagnosis and management, which leads to a substantial gap in care. We aimed to provide an instructional module for FDS, tailored to clinicians, patients, and caregivers in Hindi, to enhance communication of diagnosis and facilitate suitable management strategies. The instructional module development process was based on clinical settings and expert opinions. A standard translation process was performed to ensure the linguistic and cultural equivalence. Validation was carried out by a panel of 15 validators, comprising 13 neurologists and two psychiatrists, to identify key psychoeducation components and their significance. They were provided a questionnaire consisting of five questions that were related to completeness, understandability, legibility, clarification, and usefulness of educational material for clinicians, patients, and caregivers. Items were rated using a 5-point Likert scale. There was agreement on four items (Q1, Q3, Q4, and Q5), whereas approximately 85% of the validators agreed on the remaining one item (Q2). The instructional module demonstrated high content validity as indicated by both item level-content validity index (0.98) and scale-level content validity index (0.97), alongside content validity ratio and modified kappa statistics. The developed Hindi instructional module for FDS serves as a valuable tool to enhance the communication of FDS diagnosis and dispel misconceptions. This offers a comprehensive resource for clinicians, patients, and caregivers in India, potentially bridging gap in FDS care.

Advanced biomaterials for regenerative medicine and their possible therapeutic significance in treating COVID-19: a critical overview.

The potential of biomaterials in medical sciences has attracted much interest, especially in promoting tissue regeneration and controlling immune responses. As COVID-19 pandemic broke out, there was an increased interest in understanding more about how biomaterials could be employed to fight this dreaded disease, especially in the context of regenerative medicine. Out of the numerous regenerative medicine possibilities, stem cells and scaffolding (grafting) technology are two major areas in modern medicines and surgery. Mesenchymal stem cells are useful in tissue repair, tailored therapy and treating COVID-19. Using biomaterials in COVID-19 treatment is intricate that needs multi- and cross-disciplinary research. Cell-based therapy and organ transplant pose immunological rejection challenge. Immunomodulation enhanced, tumorigenicity decreased, inflammation addressed and tissue damage restricted bioengineered stem cells need clinical insights and validation. Advanced stem cell based therapies should ideally be effective, safe and scalable. Cost and scalability shall dictate the dawn of technoeconomically feasible regenerative medicine. A globally standard and uniform approval process could accelerate translational regenerative medicine. Researchers, patient advocacy organisations, the regulators and the biopharmaceutical stakeholders need to join hands for easy navigation of regulatory measures and expeditious market entry of regenerative medicine. This article summarises advances in biomaterials for regenerative medicine, and their possible therapeutic benefits in managing infectious diseases like COVID-19. It highlights the significant recent developments in biomaterial design, scaffold construction, and stem cell-based therapies to treat tissue damage and COVID-19 linked immunological disregulation. It also highlights the potential contribution of biomaterials towards creating novel treatment strategies to manage COVID-19.