Set Of Intervals Research Articles

Cortical Activation during Swallowing Exercise Tasks: an fNIRS Pilot Study.

This pilot study used functional near-infrared spectroscopy (fNIRS) to examine brain activity in selected regions of the left motor and sensory cortex while doing swallowing-related tasks. Specifically, differences in cortical activation during normal saliva swallows, effortful swallows, and tongue pressing were investigated. Nine healthy, right-handed adults (5 female, 4 male; Age: 22-30 years) were recruited. The tasks included were (1) normal saliva swallowing, (2) effortful saliva swallowing, and (3) lingual pressing against the palate. Each task was completed three times in a block, for a total of five blocks. Blocks were randomized and presented with set time intervals using PsychoPy. Motor activity was highest during effortful swallows, followed by normal swallows, and lingual presses. Activation in the sensory region was not significantly different across tasks; however, effortful swallows elicited the highest mean peak activation. Our findings suggest that fNIRS can be a viable imaging method used to examine differences in cortical activity in the context of swallowing. Its applicability in future dysphagia research should be explored.

Testing of Permeability of RFID Access Control System for the Needs of Security Management

Access control systems are part of the overall protection of objects. It is often the first system with which it is necessary to start the contact system; therefore, it is necessary to ensure its proper functioning. In the event of a malfunction, it can cause downtime in production, and it is triggered by a bad replacement of workers. Access control systems have their own specificities that need to be considered when designing security. Poor selection of access control devices can cause inefficient system functionality, resulting in downtime and loss. Based on experimental testing and related work in access control systems, this paper discusses the possibilities of testing the throughput of access control systems. The manuscript presents the design of a unique test device that can be used in the assessment of the reliability and throughput of access control systems. With the help of tests, we were able to determine the probability of downtime due to inappropriately set time intervals for changing employees on a work shift.

Adapting Methods For Bed Level Assessment In And Around Submerged Vegetation

Coastal vegetation has the capacity to reduce flow and orbital velocities near the bed and stabilize the sediment with its root network. As a result it has an effect on sediment dynamics which gains increasing attention as ecosystem service in coastal protection. To quantify and predict this ecosystem service, laboratory experiments with live or artificial vegetation are often conducted. During these experiments the assessment of bed level change is challenged by the presence of the vegetation. Standard optical and acoustic measurement techniques cannot obtain data below vegetation canopies. Especially for submerged flexible vegetation like seagrass, this challenge is aggravated for airborne methods that require flume drainage (e.g. terrestrial laser scanning) (Follett and Nepf, 2012). Flexible blades will spread on the ground during drainage, potentially covering meadow edges and thus excluding areas of interest from the bed level analysis. Moreover, live aquatic vegetation may be stressed by air exposure, if the facility is drained for bed level measurements. This potentially leads to different, non-natural behaviour in consecutive experiments. And finally, draining and refilling the facility is time consuming, especially as it needs to be done very carefully as not to disturb any generated bedforms. This time aspect hampers the collection of time series and thus the assessment of the development of bed level changes with airborne methods. Underwater technology like sonar and echo sounder avoid shading of areas by spread-out vegetation, but are equally not capable of obtaining data below vegetation canopies. Moreover, instruments that can obtain spatially resolved data underwater often require a minimum water depth which may exceed the water depth relevant for experiments with vegetation. In intertidal areas (e.g. salt marshes) the challenge of obtaining bed level data below vegetation canopies is overcome by the use of a sediment-erosion-bar (SEB) (Cahoon et al., 2002). For this method a horizontal bar is installed at a fixed height above the ground and the distance between this bar and the ground is measured at defined locations along the bar and set time intervals to obtain information on the bed level change. SEBs have successfully been adapted to laboratory settings in the past (Spencer et al., 2016), but still required flume drainage. We applied this method in an undrained flume to assess sediment dynamics in and around an artificial seagrass meadow. Additionally, we tested underwater photogrammetry to obtain 3D-spatial information (e.g., 3D models) on bed level and bedforms at and near the vegetation edges. Photogrammetry has been successfully applied to obtain bedform information in the presence of seagrass stands, but to date still required the drainage of the facility (Meysick et al., 2022).

Association between the RTS,S/AS01 malaria vaccine and under-nutrition in children aged 10 to 59 months in Siaya County, Kenya

Background: Under-nutrition is a global problem associated with infectious diseases including malaria. In this study, we explored the association between the newly introduced malaria vaccine, RTS,S/AS01 and measles vaccine with under-nutrition in children. Methods: We conducted a case-control study using anthropometric data for children aged between 10 and 59 months in Siaya County, Kenya collected from September to December 2021. Malaria and measles vaccines were the exposure variables, and under-nutrition, which is a composite variable including stunting and/or underweight was the outcome. Chi-square was used to test association between under-nutrition and the two vaccines. Further, a binary logistic regression was used to assess association between the vaccines, and independent variables with a confidence interval set at 95%. Results: From a total of 1,701 children, 185 (16.8%) were undernourished. Among the undernourished children, 121 (11.0%) were stunted and 64 (5.81%) were underweight. The undernourished children were matched with children of normal nutritional status at a ratio of 1:1 giving a total of 370. Malaria vaccine coverage was low at 21.1% (39/370) and 17.3% (32/370) among undernourished and children with normal nutritional status respectively. Similarly, the measles vaccine coverage was 30.8% (57/370) and 36.2% (67/370) among cases and controls respectively. Neither exposure to malaria nor measles vaccines showed a statistically significant difference between the cases and controls. Conclusions: We found no statistically significant association between malaria vaccine and under-nutrition in children. While malaria vaccine is important in protecting children from the malaria disease effects, it is not a proxy intervention for under-nutrition.

Physics-informed neural nets for control of dynamical systems

Physics-informed neural networks (PINNs) incorporate established physical principles into the training of deep neural networks, ensuring that they adhere to the underlying physics of the process while reducing the need for labeled data, since the desired output is not a prerequisite for physics-informed training. For modeling systems described by Ordinary Differential Equations (ODEs), traditional PINNs typically take continuous time as an input variable and produce the solution to the corresponding ODE. However, in their original form, PINNs neither accommodate control inputs nor do they effectively simulate variable long-range intervals without experiencing a significant decline in prediction accuracy. In this context, this work introduces a novel framework known as “Physics-Informed Neural Nets for Control” (PINC). PINC presents an innovative PINN-based architecture tailored to control problems, capable of simulating longer-range time horizons that are not predetermined during training. This increased flexibility sets it apart from traditional PINNs. The variable simulation time is achieved by adding inputs to the PINC network that convey the initial condition and the control signal for a particular time interval. Simulating variable long-range intervals involves running the PINC net across a sequence of shorter intervals. In this autoregressive process, the network predictions are linked in a self-feedback mode, with the initial state (input) of the next interval set to the last predicted state (network output) of the previous interval. We showcase the effectiveness of our proposal in identifying and controlling three nonlinear dynamic systems: the Van der Pol oscillator, the four-tank system, and an electric submersible pump. Crucially, these experiments demonstrate that learning the dynamics of these systems can be achieved without relying on any sample collected from the actual process, and it offers faster inference speed compared to numerical simulations.

Communication and Interaction Practices in Czech Classrooms with a Teaching Assistant

The presented study focuses on pedagogical communication and interaction occurring in teaching with the presence of a teaching assistant. The aim was to enhance understanding of pedagogical communication and interaction in these classrooms. To achieve this goal, an analysis of teaching sessions was undertaken from the perspective of activities (interactions and communication) assumed by the teaching assistant during instruction. The research methodology relies on standardized observation, specifically employing the Flanders Interaction Analysis System, which investigates communication and interaction within the classroom environment. For observing teaching sessions with teaching assistants, 16 categories were developed and refined through pilot testing. Validation was conducted using video analysis. In total, 15 teaching sessions were recorded and subsequently analyzed. Specialized software Codenet was utilized for video data analysis, with a set time interval of 3 s. The data analysis revealed several crucial insights into the communication and interaction of teaching assistants and teachers in an educational context. Some categories, such as the preparation of teaching aids and those related to passivity, predominate, while others, including active involvement in teaching or introducing new educational content, are notably absent. Understanding these patterns may lead to optimizing the role of the teaching assistant in supporting individual students and classroom dynamics. The article discusses inclusive practices of teaching assistants, without distinguishing between students with special educational needs and typically developing peers. Inclusive education promotes social and environmental sustainability by fostering a sense of belonging and equality among students.

Managing congestion at the entrance of subway stations with reserved inbound service

During peak hours at many entrances of subway stations, huge queues of passengers try to enter. To address this congestion, the city of Beijing has introduced a reserved inbound services in several subway stations. Passengers can reserve a time interval via which they can enter with reduced queuing. We aim to analyse and optimize this system while modelling the congestion and choices of travellers using a Vickrey bottleneck model approach. We develop a reserved system where there can be a different number of reservation time intervals before and after the preferred arrival time, and for common preferences for schedule delay adding intervals before the preferred arrival time reduces travel costs more than adding them after it. Results show that the time interval set can influence both implementation conditions and the efficiency of the reserved system. Suggestions about the time interval set are given. Passengers’ inbound choices with different inbound peak reservation costs are analysed.

Extending the concepts of complex interval valued neutrosophic subbisemiring of bisemiring

The objective of this paper is to investigate the innovative concept of complex neutrosophic subbisemiring. The novelty of the complex neutrosophic subbisemiring lies in its wide range of truth, indeterminacy, and false function values. It goes beyond the range of [0,1] in the complex plane in contrast to the traditional range [0,1]. Therefore, these three functions can be described mathematically using a complex number in the complex neutrosophic subbisemiring. We develop and analyze the concept of complex interval-valued neutrosophic subbisemiring (CIVNSBS). Moreover, we study homomorphic characteristics and important properties of CIVNSBS. We propose the level sets of CIVNSBS and complex interval valued neutrosophic normal subbisemiring (CIVNNSBS) of bisemirings. Moreover, we introduce CIVNNSBS of bisemiring. Let ¡ be a complex neutrosophic subset of bisemiring S. Then is a CIVNSBS of S if and only if all non empty level set is a subbisemiring, where . Let ¡be a CIVNSBS of bisemiring S and V be the strongest complex neutrosophic relation of bisemiring S. Then ¡ is a CIVNSBS of bisemiring S if and only if V is a CIVNSBS of . We illustrate that homomorphic images of every CIVNSBS is a CIVNSBS and homomorphic pre-images of every CIVNSBS is a CIVNSBS. Examples are provided to illustrate our results.\\

Turbidity effect derived from palm oil mill effluent altered predation period of siamese fighting fish (Betta splendens, Regan 1910)

Contamination of palm oil mill effluent (POME) into water bodies potentially increases turbidity, resulting in disrupting aquatic organisms behaviour, including predation period. However, the effect of increased turbidity due to POME contamination toward the fish predation is still unexplored. Siamese fighting fish (Betta splendens) is one of the ideal test fish to investigate this effect. This study aims to analyze the effect of increased turbidity due to POME contamination toward the fish predation period of Siamese fighting fish. The research design was completely randomized with six treatments (0, 20, 40, 60, 80 and 100 NTU) followed by five repetitions for each treatment. The experimental data parameters include the predation rate of Siamese fighting fish during foraging activity, which was calculated from the percentage of the number of mosquito larvae consumed per 2 min during 15 min. The significance level of predation between treatments in each observation time period was analyzed using a one-way Analysis of Variance (ANOVA) with the confidence interval set at the 95% level. The results showed that at a turbidity level of 100 NTU, the predation period of Siamese fighting fish had been altered. In the period of 0-2 min, the predation rate decreased significantly. Meanwhile, the predation rate increased significantly during the period of 2-4 and 8-10 min. This finding should be considered to support POME remediation management, especially turbidity parameters.Keywords:POMEFeeding periodForaging areaTurbidity

Guaranteed Set-Membership Estimation for Local Nonlinear Uncertain Fuzzy Systems Subject to Partially Decouplable Unknown Inputs

Local nonlinear fuzzy systems are useful for control solutions due to their ability to handle unmeasurable/inexact premise variables and reduce computational complexity. However, their estimation problems still require further development. This paper addresses this issue by investigating set-membership estimation for a class of discrete-time local nonlinear uncertain Takagi–Sugeno fuzzy systems with guaranteed performance. We propose a new observer architecture that solves partially decouplable unknown inputs and converts nonlinear error dynamics into a linear parameter-varying type. Using the systematic <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _\infty$</tex-math></inline-formula> -technique, the design conditions in the form of linear matrix inequalities ensure stability and output performance of the state estimation. We also derive a straightforward and effective zonotopic analysis method, considering the fuzzy and local nonlinear context, for less conservative results without using any specific interval set computation. Furthermore, a fast fault detection logic is proposed as an application of the set-membership estimation. Finally, we demonstrate the feasibility and advantages of our approach through three compelling examples, showcasing its efficacy in different scenarios.

On the T(ense) and Asp(ect) in the derivation of infinitives in Portuguese

In this paper, we discuss the morphosemantics of aspect and tense in the Portuguese infinitives. Specifically, within Distributed Morphology framework (Halle &amp; Marantz, 1993), in advancing Resende &amp; Pires de Oliveira (2022)’s recent proposal, we approach the internal constituency of Portuguese infinitives (mostly Tense and Aspect) and discuss what exactly these pieces of structure denote. Firstly, we argue that all infinitives contain AspP and that its head is featured as neutral, in the sense of Smith (1997). Secondly, we approach TP and argue for a tree-way system (T[+TENSE], T[–TENSE], T[ ]), which can be associated to two different syntactic environments (in this case, DP vs. no DP), but not to different syntactic settings (for instance, raising vs. ECM). On the sematic side, Asp[neutral] denotes a set of time intervals where the event denoted by the verbal stem has started. As regards Tense, if T is [–TENSE], then it renders a simultaneous reading in relation to the matrix verb and if it is [+TENSE], the infinitive is futurate in relation to the matrix verb. However, if T lacks a tense-value, the TP will denote a property of events, almost like the nP-denotation in the nominal domain.

Enhancing bitcoin transaction confirmation prediction: a hybrid model combining neural networks and XGBoost

With Bitcoin being universally recognized as the most popular cryptocurrency, more Bitcoin transactions are expected to be populated to the Bitcoin blockchain system. As a result, many transactions can encounter different confirmation delays. Concerned about this, it becomes vital to help a user understand (if possible) how long it may take for a transaction to be confirmed in the Bitcoin blockchain. In this work, we address the issue of predicting confirmation time within a block interval rather than pinpointing a specific timestamp. After dividing the future into a set of block intervals (i.e., classes), the prediction of a transaction’s confirmation is treated as a classification problem. To solve it, we propose a framework, Hybrid Confirmation Time Estimation Network (Hybrid-CTEN), based on neural networks and XGBoost to predict transaction confirmation time in the Bitcoin blockchain system using three different sources of information: historical transactions in the blockchain, unconfirmed transactions in the mempool, as well as the estimated transaction itself. Finally, experiments on real-world blockchain data demonstrate that, other than XGBoost excelling in the binary classification case (to predict whether a transaction will be confirmed in the next generated block), our proposed framework Hybrid-CTEN outperforms state-of-the-art methods on precision, recall and f1-score on all the multiclass classification cases (4-class, 6-class and 8-class) to predict in which future block interval a transaction will be confirmed.

Adaptive event‐triggered efficient output feedback model predictive control for networked interval type‐2 T–S fuzzy system with data loss

AbstractThis article is concerned with the adaptive event‐triggered (AET) efficient output feedback model predictive control (EOFMPC) for a set of nonlinear networked interval type‐2 Takagi–Sugeno (IT2 T–S) fuzzy control systems with data loss. For the reduction in data transmission burden and required control performance, the AET scheme and a novel dynamic triggering law are introduced jointly in this article. The efficient model predictive control approach as in Kouvaritakis et al. (IEEE Trans Automat Contr. 2000;45(8):1545–1549.), which transfers the main part of online optimization work to offline stage by incorporating an extra perturbation into the fixed feedback law, is extended to networked control systems to reduce their online computational burden. This article proposes the following: (1) an unified closed‐loop model representation of data loss process and IT2 T–S fuzzy nonlinearities is established under the framework of EOFMPC, which explicitly considers the AET condition; (2) a new design method for the state observer gain is presented based on an ellipsoidal invariant set, so that a smaller bound of estimation error can be obtained through an extra optimization problem; (3) some optimization problems are formulated to design the proposed controllers, and the corresponding EOFMPC algorithm is summarized; (4) by means of the extra matrix partition technique, the online optimization problem subject to one ellipsoidal constraint is transformed into a novel convex form to solve the uncertainty of the estimation error. A simulation example and a few comparative simulation experiments illustrate the effectiveness and availability of the aforementioned approach.

Interval-Valued Fermatean Neutrosophic Shortest Path Problem via Score Function

&#x0D; Contemporary mathematical techniques have been crafted to address the uncertainty of numerous real-world settings, including Fermatean neutrosophic fuzzy set theory. Fermatean neutrosophic fuzzy set is an extension of combining Fermatean and neutrosophic sets. A Fermatean neutrosophic set was developed to enable the analytical management of ambiguous data from relatively typical real-world decision-making scenarios. Decision-makers find it challenging to determine the degree of membership (MG) and non-membership (NG) with sharp values due to the insufficient data provided. Intervals MG and NG are suitable options in these circumstances. In this article, the shortest route issue is formulated using an interval set of values in a Fermatean neutrosophic setting. A de-neutrosophication technique utilizing a scoring function is then suggested. A mathematical version is also included to show the framework's usefulness and viability in more detail.&#x0D;

Challenges in Achieving Optimal Warfarin Anticoagulation Control: A Comprehensive Exploration and Proposed Holistic Approaches

In Indonesia warfarin remains the most widely oral anticoagulation prescribed. However, the time in therapeutic range remain low in Indonesia and also in some countries in throughout the world. This study aimed to analyse the challenges of optimal warfarin control from the perspectives of healthcare professionals, patients, health system, and to propose strategies to address these challenges. This research consists of two stages. The first stage was a prospective observational study aiming to analyse the challenges and the second stage of the research was focus group discussion to propose strategies to overcome the challenges. The result of this study showed that the average of TTR was 49.4 ± 32.1, where only 37.5% patients had TTR ≥ 65%. The average dosing consistency obtained was 62.33 ± 32.42%. With regard to INR monitoring interval, almost all patient who had supraterapeutic INR had their INR monitoring interval set to 1 month after the dose adjustment. There were also identified that only 43.8% patients classified as adherent and only 39.5 % patients had good knowledge of warfarin, indicating that the level of medication adherence and knowledge was not optimal. In this study, 14.6% of patients received other drugs that potentially interacted with warfarin that cause fluctuate INR. In conclusion, this study's results showed that there were multiple challenges for optimal warfarin control including barrier for dosing consistency, INR monitoring, patients’ adherence and knowledge, as well as drug interactions. However, there will be still room for improvement. The proposed strategy that likely will achieve optimal benefit was a holistic approach that combine multifaceted strategies to address each barrier.

Interval interaction moving window partial least squares for wavelength interval selection in near infrared spectroscopy

Variable selection is a highly effective approach to improve model performance when analyzing and modeling data from near-infrared spectroscopy. Moving window partial least squares (MWPLS), a well-known and classical wavelength interval selection algorithm, was widely used in the field of near-infrared spectroscopy. However, it has gradually faded from the spotlight due to its limitations. In this study, a modified MWPLS method called interval interaction moving window partial least squares (iMWPLS), was proposed to improve some limitations of MWPLS, including the lack of consideration for the combination effects of variables, the tendency to select redundant wavelength intervals, and the need for artificial intervention. By utilizing the framework of MWPLS, it could iteratively search, test, and add the intervals with the local minimum errors to the set of basic intervals. The method has high sensitivity to the combination effects of variables and enables accurate selection of intervals. Three NIR datasets, including corn starch, pharmaceutical tablets, and soil dataset were tested to evaluate the performance of iMWPLS. The results show that iMWPLS has better performance than other seven wavelength selection methods, including MWPLS, SEPA-MWPLS, fiPLS, biPLS, siPLS, iVISSA, and iRF.

Adaptive error bounded piecewise linear approximation for time-series representation

Error-bounded piecewise linear approximation (l∞-PLA) has been proven effective in addressing challenges in data management and analytics. It works by approximating the original time series with linear segments such that the approximation error on each data point does not exceed a pre-defined threshold. To achieve this goal, most prior works on l∞-PLA use a fixed error threshold during the entire approximation process, assuming a stable time series. However, in many real-world applications, the distribution of values on the time series undergoes substantial changes in different temporal stages. This introduces a need to adaptively tune the error threshold based on the properties of the time series while considering the trade-off between representation error and storage resources. In this work, we propose a general framework for constructing l∞-PLA with adaptive error bounds (AEPLA). It works by dividing the original time series into a set of intervals and assigns adaptive error bounds to these sub-sequences based on their fluctuation levels. Next, these sub-sequences are approximated using a user-defined l∞-PLA method. We implement this framework by employing three different types of l∞-PLA methods and evaluate the performance of our approach on an extensive set of real-world time-series datasets from industrial and scientific domains. Our experiments show that constructing l∞-PLA using the AEPLA framework can provide better a trade-off between representation error and storage resources and achieves lower time and space costs than applying the original l∞-PLA methods.

An Investigative Study on Quick Switching System using Fuzzy and Neutrosophic Poisson Distribution

&#x0D; Stephens and Larson (1967) stated the sampling system as an allotted grouping of two or three sampling plans and the rules for switching between the plans for sentencing the lots of manufactured products. Quick Switching System (QSS) by Romboski (1969) is a sampling system with reference to the Single Sampling Plan (SSP) involves normal and tightened plans by adopting a switching rule. QSS provides quality protection and a reduction in the cost of inspection. The idea of fuzzy logic is adopted in the system to handle situations of fraction non-confirmation, uncertainty, or vagueness present in the parameters. The sampling plans based on fuzzy set theory in the literature are used for the defuzzification of fuzzy numbers into interval sets, able to solve problems with certain values for the upper and lower limits of intervals. As one of the expansions of fuzzy sets in this era, a new philosophy is stimulated as a substantial new theoretical development: neutroosophy sets (NSs) and used for a rise in sensitiveness that makes flexibility in the plans and systems that can be applied in the manufacturing industries. This paper enhances the determination of QSS using Neutrosophic Poisson distribution and compares it with the existing fuzzy Poisson distribution through Operating Characteristic (OC) curves.&#x0D;

High throughput LC-MS/MS method for steroid hormone analysis in rat liver and plasma – unraveling methodological challenges

Comprehensive reference data for steroid hormones are lacking in rat models, particularly for early developmental stages and unconventional matrices as the liver. Therefore, we developed and validated an enzymatic, solid-phase extraction (SPE) and liquid chromatography-tandem mass spectrometry (LC-MS/MS) method to quantify a panel of 23 steroid hormones in liver and plasma from adult and neonatal rats. Our approach tackles methodological challenges, focusing on undesired byproducts associated with specific enzymatic treatment, and enables a thorough assessment of potential interferences in complex matrices by utilizing unstripped plasma and liver. We propose an optimized enzymatic hydrolysis protocol using a recombinant β-glucuronidase/sulfatase mix (BGS mix) to efficiently deconjugate steroid phase II conjugates. The streamlined sample preparation and high-throughput solid phase extraction in a 96-well plate significantly accelerate sample processing for complex matrices and alarge number of samples. We were able to achieve the necessary sensitivity for accurately measuring the target analytes, particularly estrogens, in small sample sizes of 5–20 mg of liver tissue and 100 μL of plasma. Through the analysis of liver and plasma samples from adult and neonatal rats, including both sexes, our study showed a novel set of steroid hormone reference intervals. This study provides a reliable diagnostic tool for the quantification of steroids in rat models and gives insight in liver and plasma-related steroid hormone dynamics at early developmental stages. In addition, the method covers several pathway intermediates and extend the list of steroid hormones to be investigated.

Nontoxic Levels of Se-Containing Compounds Increase Survival by Blocking Oxidative and Inflammatory Stresses via Signal Pathways Whereas High Levels of Se Induce Apoptosis.

Selenium is a main group element and an essential trace element in human health. It was discovered in selenocysteine (SeC) by Stadtman in 1974. SeC is an encoded natural amino acid hailed as the 21st naturally occurring amino acid (U) present in several enzymes and which exquisitely participates in redox biology. As it turns out, selenium bears a U-shaped toxicity curve wherein too little of the nutrient present in biology leads to disorders; concentrations that are too great, on the other hand, pose toxicity to biological systems. In light of many excellent previous reviews and the corpus of literature, we wanted to offer this current review, in which we present aspects of the clinical and biological literature and justify why we should further investigate Se-containing species in biological and medicinal contexts, especially small molecule-containing species in biomedical research and clinical medicine. Of central interest is how selenium participates in biological signaling pathways. Several clinical medical cases are recounted; these reports are mainly pertinent to human cancer and changes in pathology and cases in which the patients are often terminal. Selenium was an option chosen in light of earlier chemotherapeutic treatment courses which lost their effectiveness. We describe apoptosis, and also ferroptosis, and senescence clearly in the context of selenium. Other contemporary issues in research also compelled us to form this review: issues with CoV-2 SARS infection which abound in the literature, and we described findings with human patients in this context. Laboratory scientific studies and clinical studies dealing with two main divisions of selenium, organic (e.g., methyl selenol) or inorganic selenium (e.g., sodium selenite), are discussed. The future seems bright with the research and clinical possibilities of selenium as a trace element, whose recent experimental clinical treatments have so far involved dosing simply and inexpensively over a set of days, amounts, and time intervals.