Unpredictable Times Research Articles

The Safe Campus Project— Resilience of Academic Institutions during the COVID-19 Crisis

In this study, we describe how to keep a campus safe and “open” by implementing a proactive, as opposed to reactive, strategy (the Green Zone strategy). The pillars are leadership, clear communication, clean air, vaccination campaigns, and intense efforts in mass testing. Over a period of 12 months, about 277,000 pooled real-time polymerase chain reaction (RT-PCR) samples and lateral flow tests (LFTs) were collected, and 201 people were identified as COVID-19-positive. For the PCRs, we use the Lollipop technique, combined with nose swabs and gargle samples, to minimize sample-collection efforts. Importantly, not only staff, students, and contractors, but also their family members, friends, and partners; daycare centers; and local sports and arts teams, etc., were invited and participated. This outreach made it possible to propagate the tests more widely and monitor a larger network. At times of larger social gatherings—most prominently, on 23 December 2021 before Christmas (during the rise of the Omicron wave)—testing capacities were increased. The results not only demonstrate the great power of mass testing in providing an open-but-safe work environment, even if the surroundings are highly infectious (red zone), but also the strength and resilience of a university. It shows how the unique pillars of science, infrastructure, students, and independency make it possible to maneuver a community, even through unpredictable times.

Prepared and reactive inhibition in smokers and non-smokers

IntroductionModels of addiction have identified deficits in inhibitory control, or the ability to inhibit inappropriate or unwanted behaviors, as one factor in the development and maintenance of addictive behaviors. Current literature supports disruption of the prefrontal circuits that mediate reactive inhibitory control processes (i.e., inhibition in response to sudden, unplanned changes in environmental demands) in substance use disorders. However, the relationship between disorders of addiction, such as nicotine dependence, and planned inhibitory processes (i.e., inhibition that occurs after advance warning) is unclear. The goal of the present study was to examine the extent to which reactive and planned inhibitory processes are differentially disrupted in nicotine dependent individuals. MethodWe employed an internet-based novel stop signal task wherein participants were instructed to stop a continuous movement at either a predictable or unpredictable time. This task explicitly separated planned and reactive inhibitory processes and assessed group differences in task performance between smokers (N = 281) and non-smokers (N = 164). The smoker group was defined as any participant that identified as a smoker and reported an average daily nicotine consumption of at least 2 mg. The non-smoker group was defined as any participant that identified as a non-smoker and had not been a former smoker that quit within the last year. The smoker group also completed a questionnaire regarding smoking behaviors which included the Fägerstrom Test of Nicotine Dependence (FTND). We used these data to assess the continuous relation between planned stopping, unplanned stopping, and smoking behaviors. ResultsWe found significant differences in stop times for both reactive and planned stopping between groups as well as within the smoker group. Additionally, in the smoker group, dependence as measured by the FTND was associated with longer stop times on planned stop trials. Surprisingly, greater daily average consumption of nicotine was related to faster stopping for both trial types. ConclusionThese results indicate the relevance of measuring both reactive and planned inhibitory processes for elucidating the relationship between nicotine addiction and mechanisms of inhibitory control.

Scheduling of Multi-Robot Job Shop Systems in Dynamic Environments: Mixed-Integer Linear Programming and Constraint Programming Approaches

This paper investigates a dynamic scheduling problem within a job shop robotic cell, wherein multiple robotic arms are responsible for material handling in a U-shaped arrangement. Each robotic arm has access to specific workstations based on their distance in the cell layout. Therefore, a part may need to be exchanged between several robots according to its process plan. For this purpose, intermediate buffers are positioned between each pair of consecutive robots. Due to the dynamic nature of the problem, new jobs arrive at unpredictable times, which in turn necessitates rescheduling taking the system’s current state into account. To tackle this problem, firstly, a Mixed-Integer Linear Programming (MILP) model is devised. Secondly, three distinct Speed-up Constraints (SCs) derived from the problem’s inherent characteristics are designed and implemented to accelerate the MILP model’s solving procedure. Afterward, the problem is formulated using Constraint Programming (CP) approach. The performance of the CP model and the MILP model in presence of all possible combinations of the SCs are evaluated and compared through solving various random instances. Next, an analysis is performed on the buffers’ pick-up criterion and how it is affected by the problem’s size. Besides, the impact of changes in the robots’ speed on the productivity of the cell is assessed. Finally, the extent to which the rescheduling priority affects the output of the model is studied.

Performance Prediction in EDM Process for Al 6061 Alloy Using Response Surface Methodology and Genetic Algorithm

The Electric Discharge (EDM) method is a novel thermoelectric manufacturing technique in which materials are removed by a controlled spark erosion process between two electrodes immersed in a dielectric medium. Because of the difficulties of EDM, determining the optimum cutting parameters to improve cutting performance is extremely tough. As a result, optimizing operating parameters is a critical processing step, particularly for non-traditional machining process like EDM. Adequate selection of processing parameters for the EDM process does not provide ideal conditions, due to the unpredictable processing time required for a given function. Models of Multiple Regression and Genetic Algorithm are considered as effective methods for determining the optimal processing variables of Electrical Discharge Machining. The material removal rate (MRR) and tool wear (Tw) were investigated using the process variables of pulse on time (Ton), pulse off time (Toff), and current intensity (Ip). The established empirical models were used to perform Genetic Algorithm (GA) to maximize (MRR) and minimize (Tw). The optimization results were utilized to establish machining conditions, validate empirical models, and obtain optimization outcomes. The optimal result that appears in this work was the pulse on (176.261 μs), pulse off (39.42 μs), and current intensity (23.62 Amp.) to maximize the MRR to (0.78391 g/min) and reduce tool wear to (0.0451 g/min).

Understanding COVID-19's Impact on Nursing Students' Education, Professional Perceptions, Stress, and Coping.

This descriptive mixed-methods study was conducted to understand the impact of COVID-19 on undergraduate nursing students' education, perceptions of nursing, stress, and coping. A convenience sample (N = 150) reported high levels of stress with higher stress levels significantly associated with lower coping. The essence of nursing, emotions surrounding the transition to nursing, educational barriers, and faculty-student relationships emerged as themes. The findings can be used to revise curricula and better support nursing students in future disasters and transitions to practice during unpredictable times.

A disaster classification application using convolutional neural network by performing data augmentation

Natural disasters are catastrophic events and cause havoc to human life. These events occur in the most unpredictable times and are beyond human control. The aftermath of the disasters is devastating ranging from loss of life to relocation of large groups of the population. With the development in the domains of computer vision (CV) and Image processing, machine learning and deep learning models can integrate images and perform predictions. Deep learning techniques employ many robust techniques and provide significant results even in the case of images. The detection of natural disasters without human intervention requires the help of deep learning techniques. The project aims to employ a multi-layered convolutional neural network (CNN) organization to classify the images related to natural disasters related to earthquakes, floods, cyclones, and wildfires.

Toward the Predictability of Dynamic Real-Time DNN Inference

Deep neural networks (DNNs) have been widely used in many cyber–physical systems (CPSs). However, it is still a challenging work to deploy DNNs in real-time systems. In particular, the execution time of DNN inference must be predictable, s.t. it could be known whether the runtime inference can complete within a required timing constraint. Moreover, the timing constraints may change dynamically with the runtime environment in many embedded applications, such as autonomous cars. A possible way to meet such dynamic real-time requirements is to execute different subnetworks of a DNN at runtime. However, improper construction of subnetworks may not only introduce unpredictable inference time, s.t. the real-timing constraints could be violated unexpectedly, but also has poor compatibility with the well-optimized machine learning framework (e.g., TensorFlow). In this article, we study the predictability when executing different subnetworks of a DNN. In particular, we present a featurewise runtime adaptation framework for DNN inference, which is implemented and validated on NVIDIA Jetson TX2 and Nano with TensorFlow. The experimental results show that our method can achieve predictable inference time in comparison with the state-of-the-art methods.

A Short Review on Floating Drug Delivery System

In the latest years, scientific and technical progresses have been made in the study and progress of novel drug delivery systems by overcoming physiological difficulties such as short gastric residence times and unpredictable gastric emptying times. Gastric emptying of dose form is extremely variable process and ability to prolong and control the emptying time. Gastric transit time is valuable asset for dosage forms, which reside in the stomach for a long period of time than conventional dosage form. Several approaches are currently utilized in the prolongation of the gastric residence times, including floating drug delivery systems, swelling and expanding systems, polymeric bioadhesive systems, modified-shape systems, high-density systems and other delayed gastric emptying devices. The purpose of writing this review on floating drug delivery systems (FDDS) was to compile the recent literature with special focus on the principal mechanism of floatation to achieve gastric retention. This review explains briefly about types of floating system, advantages, limitation, floating mechanism, factors affecting floating system, drug candidates suitable for floating, evaluation parameters and application of the system.

Secure random number generation from parity symmetric radiations

The random number generators (RNGs) are an indispensable tool for information security. Among various approaches, the radioactive decay has been considered as a promising candidate of RNGs for over half a century, on account of its seemingly unpredictable decay timings as quantum phenomena. However, the security of these radioactive RNGs has not been proven so far. Here we prove the security by a change of tactics, that is, by rewriting decay timings into decay directions, which allows us to ensure the secrecy with the help of the parity invariance deeply rooted in the fundamental law of nature. Our result demonstrates that the foundational properties of particle physics, such as the symmetry of interactions, can be used as a firm basis for the RNGs.

Talent Identification and Development in Paralympic Contexts: Current Challenges.

This short review explores the state of talent identification and development of athletes in Paralympic contexts. While talent identification typically occurs during adolescence, this practice is more complex and variable in Paralympic contexts compared to non-Paralympic contexts. For example, Paralympic athletes can have impairments that are congenital or acquired at any time across their lives. Therefore, they can enter performance pathways at unpredictable times. Furthermore, differences and nuances associated with athlete impairments (type and severity), compounded by other systematic complexities (e.g., systems of classification) highlight the need to consider alternative and creative approaches to talent identification and development. We provide an overview of some of these complexities, highlight areas for future research, and provide recommendations for practitioners.

A space-time generalized finite difference method for solving unsteady double-diffusive natural convection in fluid-saturated porous media

In this paper, the space-time generalized finite difference scheme is proposed to effectively solve the unsteady double-diffusive natural convection problem in the fluid-saturated porous media. In such a case, it is mathematically described by nonlinear time-dependent partial differential equations based on Darcy's law. In this work, the space-time approach is applied using a combination of the generalized finite difference, Newton-Raphson, and time-marching methods. In the space-time generalized finite difference scheme, the spatial and temporal derivatives can be performed using the technique for spatial discretization. Thus, the stability of the proposed numerical scheme is determined by the generalized finite difference method. Due to the property of this numerical method, which is based on the Taylor series expansion and the moving-least square method, the resultant matrix system is a sparse matrix. Then, the Newton-Raphson method is used to solve the nonlinear system efficiently. Furthermore, the time-marching method is utilized to proceed along the time axis after a numerical process in one space-time domain. By using this method, the proposed numerical scheme can efficiently simulate the problems which have an unpredictable end time. In this study, three benchmark examples are tested to verify the capability of the proposed meshless scheme.

Pengaruh PPKM Terhadap Peningkatan Peran Guru pada Pembelajaran di Masa Pandemi Covid-19

Pandemic During the Covid-19 pandemic that is currently engulfing the world, including Indonesia, there have been various social movements aimed at tackling the outbreak. One of the social movements as a result of policies from the government in the context of dealing with Covid-19 is the implementation of PPKM which is always extended to an unpredictable time limit before Covid-19 disappears. PPKM has an impact on the implementation of learning in Indonesia. The purpose of writing this article is to find out how PPKM influences the increase in the role of teachers in learning in the Covid-19 pandemic era. In this study, the author uses qualitative methods through literature studies and documentation studies related to the influence of the PPKM social movement on increasing the role of teachers in the Covid-19 pandemic era from various relevant sources. The results of the discussion show the influence of PPKM on increasing the role of teachers in learning in the era of the covid-19 pandemic.

Perceptions and attitudes towards climate change in fishing communities of the Sudd Wetlands, South Sudan.

The Sudd in South Sudan, formed by the White Nile’s Baḥr al-Jabal section, is one of the largest and most important wetlands in the world. Communities in the region almost exclusively depend on fisheries for food and livelihoods. Although threatened by over-exploitation and habitat changes, fish populations are also affected by climate change. Using semi-structured questionnaires, we assessed fisherfolk’s opinions of how recent variation in climate affected their livelihoods and the environment. Fisherfolk perceived that climate had changed in the past decade and were negatively impacted by this. Interviewees reported average higher temperatures, a greater frequency of floods and droughts, unpredictable timing of seasons, and erratic rainfall. Destruction of fishing villages/camps, loss and damage of fishing equipment, shifts in the fishing calendar, reduction of fish trade, and fish catch declines as well as psycho-social problems were given as the major consequences of climate change. Causes of climate change and variability were perceived to be linked to uncontrolled harvest of forest resources, anger of God and ancestors, and natural variability in climate. Most respondents expressed a desire to adopt more responsible behavior such as planting trees and establishing community nurseries, being educated on climate change risks, and sustainable fisheries management. Our results show that fisherfolk in the Sudd are troubled by climate change impacts on their livelihoods and on fish populations. In South Sudan, climate change has been reported from hydroclimatological data but concrete impacts on people remain largely unknown and of little concern because of recent wars and the poor economy. Our study provides an example of how fisherfolks’ local ecological knowledge (LEK) can be used as an early warning system of the negative impacts on livelihoods and fish populations and support adaptation to the changing climate.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10113-022-01928-w.

A GAN-based method for time-dependent cloud workload generation

To design repeatable and comparable resource management policies for data centers, researchers mainly conduct experiments in the simulation environment, which requires large-scale workload traces to simulate real scenes. However, issues related to data collection, security and privacy hinder the public availability of cloud workload datasets. Though workload generation is a promising solution, due to the unpredictable time dependency, cloud workloads are difficult to model. In light of this, we propose a novel end-to-end model for time-dependent cloud workload generation using Generative Adversarial Networks, which adopts improved Temporal Convolution Networks and Spectral Normalization to capture the time dependency and stabilize the adversarial training. Experimental results on real cloud datasets demonstrate that our model can efficiently generate realistic workloads that fulfill the diversity, fidelity and usefulness. Further, we also propose a conditional GAN which is trained with labeled data and can generate specific kind of workloads according to the input.

Syndesmosis injuries in professional rugby players: associated injuries and complications can lead to an unpredictable time to return to play

ObjectivesSyndesmosis injuries are common and increasing in contact sports with a marked impact on players and teams alike. They can result in an unpredictable and often prolonged return to pre-injury level. We aim to evaluate the time to return to play (RTP) after syndesmosis injuries in professional male rugby players. MethodsA cohort study including all professional rugby players with syndesmosis injuries, treated both operatively and non-operatively by the senior author was performed. The follow up period was a minimum of 12 months or until RTP. Players with previous ankle injuries or associated ankle fractures were excluded. Outcome measures included players age, body mass index (BMI), field position, seven-a-side or 15-a-side match, mechanism of injury, clinical findings, radiological findings, return to training (RTT), and RTP dates. ResultsFor the period July 2015 to July 2019, a total of 13 professional male rugby players were included in the study. The leading mechanism of syndesmosis injury was in contact during a tackle. Six players had a grade 3 injury (40%), 4 players had a grade 2B injury (27%), 2 players had a grade 2A injury (13%) and 3 players had a grade 1 injury (20%). Two of the aforementioned players presented with new contralateral syndesmosis injuries during the study period. The median time for RTT and RTP was 97 days (IQR: 36) and 112 days (IQR: 54), respectively. All players with syndesmosis injuries were able to return to play. No discrepancy was found between seven-a-side and 15-a-side players regarding injury mechanism, injury severity, and RTP. ConclusionMost syndesmosis injuries in professional rugby players are acquired during a tackle. These injuries are often unstable, requiring surgical intervention, with an unpredictable recovery period. Injury severity, surgical complications, delayed diagnosis, and associated injuries can prolong this period. The subtype of rugby (seven-a-side and 15-a-side) does not affect the injury severity or return to play. Level of evidenceLevel 4

Heart Transplantation Utilizing Organs Following Donation After Circulatory Death (DCD)

<h3>Purpose</h3> Donation after circulatory death (DCD) is gaining momentum for heart transplantation worldwide. Central to the process is a detailed assessment of the donor heart following the circulatory arrest and unpredictable warm ischemia time. Our team used the Organ Care System as the principal means of assessment and preservation of the DCD hearts. Here we describe our single centre experience with DCD heart transplantation. <h3>Methods</h3> Between 2015 and 2021, 36 donor hearts were procured via the DCD pathway. Donor management involved the withdrawal of treatment (WoT) in the donor, stand-down time of 5 minutes following circulatory arrest (asystole), transfer of the patient to the operating theatre, collection of 1.2L donor blood from right atrium for Organ Care System (OCS) prime, delivery of cardioplegia and its procurement. The organs were assessed utilizing OCS and 25 suitable hearts that met the criteria of suitability were transplanted. <h3>Results</h3> The mean age of donors was 30.6±10.7 years (12% females) with intracranial haemorrhage and hypoxic brain damage as main causes of WoT. 52% of the donors suffered cardiac arrest before hospital admission, 25% had a history of cocaine abuse and 48% were current smokers. Mean WoT to asystole time was 11.8±4.1 min, mean WoT to cardioplegia time was 23.1±4.6 min and a mean functional warm ischemia time was 16.3±3.5 min. The mean OCS perfusion time was 245 ±81min.The mean recipient age was 42.6±13.2 years (12% females) with dilated (64%) and ischemic (24%) cardiomyopathy as an aetiology of heart failure in the majority. 6 (24%) patients had long term LVAD while 2(8%) were supported on short-term mechanical support per-operatively.9 (36%) patients required mechanical circulatory support postoperatively with ECMO and IABP and 20 (80%) required continuous renal replacement therapy. The mean ICU stay was 17.8±16.5 days and the mean hospital stay was 45±40 days. Postoperative survival at 30-days was 88%. <h3>Conclusion</h3> Donor hearts procured following donation after circulatory death were transplanted with acceptable short-term survival especially given the high proportion of preoperative mechanical circulatory support and urgency of transplantation in our cohort. ‘Direct procurement and perfusion' method of procurement in DCD offers an opportunity to assess the donor heart for the suitability of transplantation.

Spatial- and time- division multiplexing in CNN accelerator

With the widespread use of real-time data analysis by artificial intelligence (AI), the integration of accelerators is attracting attention from the perspectives of their low power consumption and low latency. The objective of this research is to increase accelerator resource efficiency and further reduce power consumption by sharing accelerators among multiple users while maintaining real-time performance. To achieve the accelerator-sharing system, we define three requirements: high device utilization, fair device utilization among users, and real-time performance. Targeting the AI inference use case, this paper proposes a system that shares a field-programmable gate array (FPGA) among multiple users by switching the convolutional neural network (CNN) models stored in the device memory on the FPGA, while satisfying the three requirements. The proposed system uses different behavioral models for workloads with predictable and unpredictable data arrival timing. For the workloads with predictable data arrival timing, the system uses spatial-division multiplexing of the FPGA device memory to achieve real-time performance and high device utilization. Specifically, the FPGA device memory controller of the system transparently preloads and caches the CNN models into the FPGA device memory before the data arrival. For workloads with unpredictable data arrival timing, the system transfers CNN models to the FPGA device memory upon data arrival using time-division multiplexing of FPGA device memory. In the latter case of unpredictable workloads, the switch cost between CNN models is non-negligible to achieve real-time performance and high device utilization, so the system integrates a new scheduling algorithm that considers the switch time of the CNN models. For both predictable and unpredictable workloads, user fairness is achieved by using an ageing technique in the scheduling algorithm that increases the priority of jobs in accordance with the job waiting time. The evaluation results show that the scheduling overhead of the proposed system is negligible for both predictable and unpredictable workloads providing practical real-time performance. For unpredictable workloads, the new scheduling algorithm improves fairness by 24%–94% and resource efficiency by 31%–33% compared to traditional algorithms using first-come first-served or round-robin. For predictable workloads, the system improves fairness by 50.5 % compared to first-come first-served and achieves 99.5 % resource efficiency.

“How long do I have, and what should I expect?” Prognostication and care planning in cognitive loss and dementia

Upon receiving a diagnosis of dementia, patients and their caregivers often want to know what the road ahead holds - how long will they live and when will they no longer be able to do the things they enjoy or recognize the people they love. Clinicians must balance the provision of specific and practical information with acknowledgment that many aspects of the disease course will vary between individuals. While prognostic models can provide some guidelines in overall timeframes and the impact of other medical comorbidities on disease course, there are significant limitations in their generalizability and application to the individual. Symptoms such as apathy, agitation, and psychosis can manifest at unpredictable times in disease progression, and are often more challenging for caregivers to manage than cognitive loss per se. Racial, cultural, socioeconomic and geographic disparities in access to and quality of care add further complexity that clinicians must consider as they work with patients and their support system.In this session, we will review literature on prognosis in cognitive decline and dementia. We will present data on a cohort of Veterans who initially underwent cognitive screening in outpatient geriatric mental health, and how inclusion of cognitive measures informs mortality prediction in the context of multiple medical comorbidities. We will discuss specific approaches to care and crucial conversations at different stages of cognitive and functional decline. We will also highlight key principles of patient- and family-centered care with consideration of addressing disparities. Finally, we will describe and show videos of a case that represents the challenges of working with behavioral and psychological symptoms of dementia (BPSD) in a patient with advanced disease in a care facility. This case will illustrate how palliative and person-centered strategies to engage flexibly with BPSD, which are often influenced by a one's identity and roles from earlier stages of life, can enhance care and quality of life.

Utilization of Viral Vector Vaccines in Preparing for Future Pandemics.

As the global response to COVID-19 continues, government stakeholders and private partners must keep an eye on the future for the next emerging viral threat with pandemic potential. Many of the virus families considered to be among these threats currently cause sporadic outbreaks of unpredictable size and timing. This represents a major challenge in terms of both obtaining sufficient funding to develop vaccines, and the ability to evaluate clinical efficacy in the field. However, this also presents an opportunity in which vaccines, along with robust diagnostics and contact tracing, can be utilized to respond to outbreaks as they occur, and limit the potential for further spread of the disease in question. While mRNA-based vaccines have proven, during the COVID-19 response, to be an effective and safe solution in terms of providing a rapid response to vaccine development, virus vector-based vaccines represent a class of vaccines that can offer key advantages in certain performance characteristics with regard to viruses of pandemic potential. Here, we will discuss some of the key pros and cons of viral vector vaccines in the context of preparing for future pandemics.

A Constructive Heuristic for Automated Parallel Tests Assembly

Parallel tests contain different items but have the same measurement properties. They are administered at the same or different time slots and their measurement results must be comparable. The problem of automated parallel tests assembly is studied for a long time, and many (mostly improvement) heuristic solutions are proposed and elaborated in literature. Such approaches frequently suffer from algorithms of unpredictable execution time, forcing the methods to terminate execution when some time limit or solution quality is reached. This paper proposes an efficient method of polynomial complexity, as a complete solution to the automated parallel tests assembly problem. The method uses the idea of Nawaz, Enscore, and Ham constructive heuristic algorithm to reduce the number of examined permutations, originally exploited for solving the permutation flow-shop sequencing problem. We compared the experimental results of the proposed method with two methods based on improvement heuristics that solve the same problem formulation, simulated annealing and variable neighborhood search. The main advantages of the proposed method are predictable execution time and implementation simplicity. Achieved quality of assembled tests, combined with predictable test assembly execution time, may be of particular interest in cases when computational resources for test assembly and administering are overloaded.