Quick Shift Research Articles

Artificial Neural Network controller for Solar PV based Electric Vehicle Charging Station with Supercapacitor

In this article, a hybrid energy system for a solar-powered electric vehicle charging station is proposed. A hybrid energy storage system is made up of a battery and a supercapacitor. If the solar power varies, a fast shift in supply from the storage system is required. Furthermore, power fluctuation is observed when power is drawn from the storage system. A standalone battery cannot adjust for this quick shift in supply. To address these issues, a hybrid energy storage system consisting of a supercapacitor and a battery is proposed for solar-powered electric vehicle charging stations. Power fluctuation is also compensated for using the hybrid energy system. The findings are obtained using a traditional PI controller and an ANN controller. The result taken with ANN controller are found better in comparison to results taken with the conventional PI controller. The results are obtained using MATLAB SIMULINKS.

Rapid tropicalization evidence of subtidal seaweed assemblages along a coastal transitional zone

Anthropogenic climate change, particularly seawater warming, is expected to drive quick shifts in marine species distribution transforming coastal communities. These shifts in distribution will be particularly noticeable in biogeographical transition zones. The continental Portuguese coast stretches from north to south along 900 km. Despite this short spatial scale, the strong physical gradient intensified by the Iberian upwelling creates a transition zone where seaweed species from boreal and Lusitanian-Mediterranean origin coexist. On the northern coast, kelp marine forests thrive in the cold, nutrient-rich oceanic waters. In the south, communities resemble Mediterranean-type seaweed assemblages and are dominated by turfs. Recent evidence suggests that in these coastal areas, marine intertidal species are shifting their distribution edges as a result of rising seawater temperatures. Taking advantage of previous abundance data collected in 2012 from subtidal seaweed communities, a new sampling program was carried out in the same regions in 2018 to assess recent changes. The results confirmed the latitudinal gradient in macroalgal assemblages. More importantly we found significant structural and functional changes in a short period of six years, with regional increases of abundance of warm-affinity species, small seaweeds like turfs. Species richness, diversity, and biomass increase, all accompanied by an increase of community temperature index (CTI). Our findings suggest that subtidal seaweed communities in this transitional area have undergone major changes within a few years. Evidence of “fast tropicalization” of the subtidal communities of the Portuguese coast are strong indication of the effects of anthropic climate change over coastal assemblages.

Multi-muscle synergies in preparation for gait initiation in Parkinson’s disease

ObjectiveWe investigated changes in indices of muscle synergies prior to gait initiation and the effects of gaze shift in patients with Parkinson’s disease (PD). A long-term objective of the study is to develop a method for quantitative assessment of gait-initiation problems in PD. MethodsPD patients without clinical signs of postural instability and two control groups (age-matched and young) performed a gait initiation task in a self-paced manner, with and without a quick prior gaze shift produced by turning the head. Muscle groups with parallel scaling of activation levels (muscle modes) were identified as factors in the muscle activation space. Synergy index stabilizing center of pressure trajectory in the anterior-posterior and medio-lateral directions (indices of stability) was quantified in the muscle mode space. A drop in the synergy index in preparation to gait initiation (anticipatory synergy adjustment, ASA) was quantified. ResultsCompared to the control groups, PD patients showed significantly smaller synergy indices and ASA for both directions of the center of pressure shift. Both PD and age-matched controls, but not younger controls, showed detrimental effects of the prior gaze shift on the ASA indices. ConclusionsPD patients without clinically significant posture or gait disorders show impaired stability of the center of pressure and its diminished adjustment during gait initiation. SignificanceThe indices of stability and ASA may be useful to monitor pre-clinical gait disorders, and lower ASA may be relevant to emergence of freezing of gait in PD.

UEQMS: UMAP Embedded Quick Mean Shift Algorithm for High Dimensional Clustering

The mean shift algorithm is a simple yet very effective clustering method widely used for image and video segmentation as well as other exploratory data analysis applications. Recently, a new algorithm called MeanShift++ (MS++) for low-dimensional clustering was proposed with a speedup of 4000 times over the vanilla mean shift. In this work, starting with a first-of-its-kind theoretical analysis of MS++, we extend its reach to high-dimensional data clustering by integrating the Uniform Manifold Approximation and Projection (UMAP) based dimensionality reduction in the same framework. Analytically, we show that MS++ can indeed converge to a non-critical point. Subsequently, we suggest modifications to MS++ to improve its convergence characteristics. In addition, we propose a way to further speed up MS++ by avoiding the execution of the MS++ iterations for every data point. By incorporating UMAP with modified MS++, we design a faster algorithm, named UMAP embedded quick mean shift (UEQMS), for partitioning data with a relatively large number of recorded features. Through extensive experiments, we showcase the efficacy of UEQMS over other state-of-the-art algorithms in terms of accuracy and runtime.

Dynamic involvement of premotor and supplementary motor areas in bimanual pinch force control

Many activities of daily living require quick shifts between symmetric and asymmetric bimanual actions. Bimanual motor control has been mostly studied during continuous repetitive tasks, while little research has been carried out in experimental settings requiring dynamic changes in motor output generated by both hands. Here, we performed functional magnetic resonance imaging (MRI) while healthy volunteers performed a visually guided, bimanual pinch force task. This enabled us to map functional activity and connectivity of premotor and motor areas during bimanual pinch force control in different task contexts, requiring mirror-symmetric or inverse-asymmetric changes in discrete pinch force exerted with the right and left hand. The bilateral dorsal premotor cortex showed increased activity and effective coupling to the ipsilateral supplementary motor area (SMA) in the inverse-asymmetric context compared to the mirror-symmetric context of bimanual pinch force control while the SMA showed increased negative coupling to visual areas. Task-related activity of a cluster in the left caudal SMA also scaled positively with the degree of synchronous initiation of bilateral pinch force adjustments, irrespectively of the task context. The results suggest that the dorsal premotor cortex mediates increasing complexity of bimanual coordination by increasing coupling to the SMA while SMA provides feedback about motor actions to the sensory system.

Alert System for Emergency State of Vehicle Users

Abstract: One of the main reasons for road collisions is over-speeding. The sharp rise in traffic accidents is currently one of the main causes of human deaths. The worth of human life is above all other factors, and providing immediate assistance is more important than lending a hand help. If emergency personnel had been able to get accident reports and get there in time, more lives might have been saved. When it comes to saving lives, the time it takes from the moment of the accident until the ambulance arrives at the scene is critical. We can reduce the time between an accident occurring and a medical ambulance being dispatched to the scene by lowering death rates. Today, GPS is a crucial part of a vehicle's system. The accelerometer senses a quick shift in the axles of the vehicle. It will be evaluated by Arduino. The warning message and its position are sent to the police control room or a rescue team by the Arduino using the GSM module. Therefore, once they get the information, the authorities can use the GPS module to trace the location automatically. The appropriate action will be carried out after the location has been verified.

Serial and joint processing of conjunctive predictions

ABSTRACT When two jointly presented cues predict different outcomes, people respond faster to the conjunction/overlap of outcomes. Two explanations exist. In the joint account, people prioritize conjunction. In the serial account, people process cues serially and incidentally respond faster to conjunction. We tested these accounts in three experiments using novel web based attention-tracking tools. Participants learned colour-location associations where colorus predicted target locations (Experiment 1). Afterward, two cues appeared jointly and targets followed randomly. Exploratory data showed participants initially prioritized locations consistent with the conjunction, shifting later. Experiment 2 presented complex color-category associations during exposure. Upon seeing joint cues, participants' responses indicated both serial and joint processing. Experiment 3, with imperfect cue-outcome associations during exposure, surprisingly showed robust conjunctive predictions, likely because people expected exceptions to their predictions. Overall, strong learning led to spontaneous conjunctive predictions, but there were quick shifts to alternatives like serial processing when people were not expecting exceptions.

Genomic Surveillance of SARS-CoV-2 in Healthcare Workers: A Critical Sentinel Group for Monitoring the SARS-CoV-2 Variant Shift.

SARS-CoV-2 genome surveillance is important for monitoring risk groups and health workers as well as data on new cases and mortality rate due to COVID-19. We characterized the circulation of SARS-CoV-2 variants from May 2021 to April 2022 in the state of Santa Catarina, southern Brazil, and evaluated the similarity between variants present in the population and healthcare workers (HCW). A total of 5291 sequenced genomes demonstrated the circulation of 55 strains and four variants of concern (Alpha, Delta, Gamma and Omicron-sublineages BA.1 and BA.2). The number of cases was relatively low in May 2021, but the number of deaths was higher with the Gamma variant. There was a significant increase in both numbers between December 2021 and February 2022, peaking in mid-January 2022, when the Omicron variant dominated. After May 2021, two distinct variant groups (Delta and Omicron) were observed, equally distributed among the five Santa Catarina mesoregions. Moreover, from November 2021 to February 2022, similar variant profiles between HCW and the general population were observed, and a quicker shift from Delta to Omicron in HCW than in the general population. This demonstrates the importance of HCW as a sentinel group for monitoring disease trends in the general population.

Building responsive and resilient supply chains: Lessons from the COVID‐19 disruption

Building responsive and resilient supply chains: Lessons from the <scp>COVID</scp>‐19 disruption

Challenges in Drug Surveillance: Strengthening the Analysis of New Psychoactive Substances by Harmonizing Drug Checking Services in Proficiency Testing.

Drug checking is a proven harm reduction strategy and provides real-time information on the market of new psychoactive substances (NPS). It combines chemical analysis of samples with direct engagement with people who use drugs (PWUD), giving the ability to increase preparedness and responsiveness towards NPS. Next to that, it supports rapid identification of potential unwitting consumption. However, NPS cause a toxicological battle for the researchers, as factors such as the unpredictability and quick shift of the market complicate the detection. To evaluate challenges posed towards drug checking services, proficiency testing was set up to evaluate existing analytical techniques and investigate the capability to correctly identify circulating NPS. Twenty blind substances, covering the most common categories of substances, were analyzed according to the existing protocols of the existing drug checking services, including several analytical methods such as gas chromatography-mass spectrometry (GC-MS) and liquid chromatography with diode array detector (LC-DAD). The proficiency test scores range from 80 to 97.5% accuracy. The most common issues and errors are mainly unidentified compounds, presumably due to no up-to-date libraries, and/ or confusion between structural isomers, such as 3- and 4-chloroethcathinone, or structural analogs, such as MIPLA (N-methyl-N-isopropyl lysergamide) and LSD (D-lysergic acid diethylamide). The participating drug checking services have access to adequate analytical tools to provide feedback to drug users and provide up-to-date information on NPS.

Clinician Perspectives on Delivering Medication Treatment for Opioid Use Disorder during the COVID-19 Pandemic: A Qualitative Evaluation.

The coronavirus disease 2019 (COVID-19) pandemic necessitated changes in opioid use disorder care. Little is known about COVID-19's impact on general healthcare clinicians' experiences providing medication treatment for opioid use disorder (MOUD). This qualitative evaluation assessed clinicians' beliefs about and experiences delivering MOUD in general healthcare clinics during COVID-19. Individual semistructured interviews were conducted May through December 2020 with clinicians participating in a Department of Veterans Affairs initiative to implement MOUD in general healthcare clinics. Participants included 30 clinicians from 21 clinics (9 primary care, 10 pain, and 2 mental health). Interviews were analyzed using thematic analysis. The following 4 themes were identified: overall impact of the pandemic on MOUD care and patient well-being, features of MOUD care impacted, MOUD care delivery, and continuance of telehealth for MOUD care. Clinicians reported a rapid shift to telehealth care, resulting in few changes to patient assessments, MOUD initiations, and access to and quality of care. Although technological challenges were noted, clinicians highlighted positive experiences, including treatment destigmatization, more timely visits, and insight into patients' environments. Such changes resulted in more relaxed clinical interactions and improved clinic efficiency. Clinicians reported a preference for in-person and telehealth hybrid care models. After the quick shift to telehealth-based MOUD delivery, general healthcare clinicians reported few impacts on quality of care and highlighted several benefits that may address common barriers to MOUD care. Evaluations of in-person and telehealth hybrid care models, clinical outcomes, equity, and patient perspectives are needed to inform MOUD services moving forward.

Simultaneous pickup and delivery model suggestion for personnel transportation in COVID-19 pandemic conditions

The impact of COVID-19 on the transportation costs of a large-scale company has been examined. Before the pandemic, shift personnel were transported to the factory by shuttles, and after a quick shift change, other shift personnel were transported back to their homes. However, with the implementation of laws mandating the reduction of shuttle seat capacities, transportation costs have risen significantly. To address this issue, a new simultaneous Pickup and Delivery Model is proposed as an alternative to the separate transportation of shift workers. The results of this study indicate that the proposed model provides a substantial advantage in terms of both the number of vehicles used and the total distance travelled, leading to a significant reduction in costs. This research underscores the importance of effective operations research practices for the profitability of companies, particularly in extraordinary circumstances such as the COVID-19 pandemic.

A spectrum of surveillance: Charting functions of epistemic inequality across EdTech platforms in the post-COVID-19 era

Covid-19 and the public health policies emerging in response have laid bare a multiplicity of issues related to educational access and knowledge equity on a global scale. Among these, the quick shift to online and hybrid education models led teachers to adapt a plethora of digital platforms to deliver content and sponsor interactions). Such platforms range from institutionally sanctioned (and subscribed) Learning Management Systems (LMSs) to software provided by organizations beyond the institution and can pose a threat to student data and privacy. Data surveillance in educational contexts is not a new issue, nor is it only a strictly digital problem. However, the current milieu of constant and continuing public health crises has led to more frequent, uncritical, and hurried adoption of learning technologies. This article challenges professionals in higher education specifically to take a more critical look at the various EdTech platforms they are, have, and will adopt in the post-COVID-19 era, and the spectrum of surveillance such platforms enact. Through a review of common entities such as LMSs, Google Workspace for Education, and Zoom video conferencing software, this article demonstrates how these technologies place both teachers and students in a relationship to data and learning characterised by “epistemic inequality” or “unequal access to learning imposed by private commercial mechanisms''. By taking a closer look at the problematic surveillance functioning across EdTech, this article makes a case for Commons-based Peer Production communities as equitable, open educational alternatives that have resisted market-based neoliberalism and surveillance capitalism.

Quick shift in volatile attraction between the third and fifth instar larvae of Endoclita signifier.

Endoclita signifer is a polyphagous lepidopteran species of eucalyptus that selects its hosts in the third-instar larval period. To understand how it adapts to its host during development, we studied the olfactory responses of late-stage (fifth)-instar larvae to the dynamic chemical environment they encounter. Thirty-two volatiles from eucalyptus trunk and soil were identified, among which 14 showed electroantennal activity and five were identified as new. Further behavioral bioassay showed that both β-pinene and the imitation ratio of six key volatile in eucalyptus trunk were attractive to the fifth-instar larvae, but both eliminated and increased β-pinene in the mixture decreased the choice ratio and showed no attraction. Although E. signifer larvae shifted their attraction from o-cymene at the third-instar stage to β-pinene at the fifth-instar stage in a single volatile, the appropriate ratio of the main compounds in eucalyptus trunk volatile is the key to the behavior choice of fifth-instar larvae. The switch in olfactory attraction to different compounds between fifth- and third-instar larvae indicated an olfactory plasticity between third- and fifth-instar larvae.And the particular blend mediated the highly specialized communication interactions specificity between fifth-instar larvae and eucalyptus trunk volatile indicated the specialized host adaptation to fifth-instar larvae. This enhances understanding of how the primitive lepidopteran E. signifier, as a native pest, adapts to introduced eucalyptus. Moreover, this study provides knowledge for the screening and development of target volatiles for trapping and managing E. signifer larvae. © 2022 Society of Chemical Industry.

The Impact of COVID-19 on Transportation of Adults With Visual Impairments.

Introduction: Access to efficient and affordable transportation options has long been a challenge for many individuals with vision loss. In spring 2020, the COVID-19 pandemic caused a quick shift in the availability and safety of transportation. Methods: Using the constant comparison method, open-ended responses from 1,162 participants in the Flatten Inaccessibility study were coded. Responses were from participants who had concerns about transportation. Results: Ten themes and corresponding subthemes emerged from the data. Themes were interdependent in that the extent of concerns differed based on respondents' support networks, transportation availability, and financial circumstances. Discussion: The COVID-19 pandemic brought to the forefront both systemic and COVID-19 transportation challenges about which those with visual impairments experienced or had concerns or both. Implications for Practitioners: It is imperative that professionals support those with visual impairments to develop alternative plans for when their typical transportation options are disrupted.

Characteristic of fixed abrasive polishing for KDP in an anhydrous environment.

High grade optical components are rapidly being employed in a wide range of applications due to their great performance. Among these, KDP (potassium dihydrogen phosphate) is the preferred choice for frequency doubling components because to its large non-linear optical technique and high laser damage threshold. In this research, KDP is treated employing fixed polishing in an anhydrous environment. Copper oxide is utilized as abrasive, and toner and phenolic resin are combined to form pellets for KDP polishing. Calculation of the temperature field on the surface of the workpiece based on the relationship of the movement. The ideal polishing parameters are also determined from the findings of the temperature field and the optimum process parameters are 5kPa pressure, 150rpm and 8mm eccentricity. A new material removal model is presented and based on this, a unique processing technique to increase the material removal depth is provided. Interestingly, with this form of polishing, the surface roughness of the workpiece remains about Ra 20nm after a quick shift and does not alter with re-pressing.

Online Teaching during the COVID-19 Pandemic: A Case Study of Albania

The spread of COVID-19 has caused the interruption of education in all levels of schools, forcing them to seek urgent solutions to reconfigure traditional education programs for distance learning. The emergency restrictions related to social distancing, gathering, etc., have generated a disruption, resulting in a massive quick shift to distance and online teaching. The disruption of teaching has been highly critical and complex, especially for developing countries due to the lack of technological infrastructure, low Internet access, low level of preparation of teachers and parents to use technology, and lack of learning motivation. This paper aimed at identifying the main teaching practices, challenges, and contextual characteristics that impacted the adoption and use of online teaching solutions, during the COVID-19 outbreak, in a developing country, Albania. The paper reached this goal by providing the results of a survey involving 100 educators from different levels of public education institutions in Albania (high schools and universities). The results revealed that despite a set of challenges faced, the online teaching experience during the pandemic time brought improvements in computer teaching skills and communication with students by using several applications for interactivity.

Robust mean shift filter for mixed Gaussian and impulsive noise reduction in color digital images

Noise reduction is one of the most important topics of digital image processing and despite the fact that it has been studied for a long time it remains the subject of active research. In the following work, we present an extension of the Mean Shift technique, which is efficiently reducing the Gaussian noise, so that it is able to cope with the impulsive disturbances. Furthermore, the elaborated technique can be applied to enhance the images corrupted by a mixture of strong Gaussian and impulsive noise, severely decreasing the quality of color digital images. By means of our approach, which is based on a novel similarity measure between a pixel and a patch located in the center of the processing block, even heavily disturbed images can be effectively restored, which enables the success of further stages of the image processing pipeline. We evaluate the efficiency of the proposed method using a publicly available database of test color images and compare the restored images applying a set of standard quality metrics with the results delivered by state-of-the-art denoising methods. Additionally, we compare our method with the Medoid and Quick Shift techniques, accelerating the original Mean Shift algorithm, in terms of objective quality criteria and computational complexity. The results of the performed experiments indicate that the proposed technique is superior to the widely used denoising techniques and can be used as a robust extension of the Mean Shift procedure. In the paper, a particular emphasis is placed on the ability of the presented algorithm to preserve and enhance image edges. The performed experiments evaluated with the use of the Pratt’s index, quantitatively confirm the superiority of the proposed design over the Mean Shift and standard denoising methods. The preservation of edges and even their sharpening is a very important feature of our algorithm whereas the final goal is segmentation, playing a crucial role in various computer vision tasks. The proposed algorithm is intended for the mixed noise reduction in color images, but it can be also applied in multispectral imaging and clustering of multidimensional data. To enable the comparison of our method with the standard denoising techniques and to help applying it in other image processing fields, we made its code freely available.

Fintech market in Iran: an analysis of Fintech ecosystem and business models

ABSTRACT The global economic is restructured by the wave of deepened technological revolutions brought by the worldwide Fintech storm. To overhaul the rapid growth and expansion of the Fintech industry requires solid data about its market structure. This conceptual study attempts to provide the first inclusive analysis of the Iranian Fintech market as a major player in Middle East. Financial services in Iran are experiencing a quick shift and the adoption of electronic payments is growing rapidly. We identified 275 active Fintech companies in Iran. Our findings show that transaction volume of Fintechs in the payment segment is the largest Fintech solution in Iran amounted to approximately 166.8 billion dollars in 2020. Almost 0.9 million Iranians are using bank-independent personal financial management for analysis of their personal finances. Moreover, crowdfunding solution as a new landscape for financing and innovation is a noble experience in Iranian Fintech market that is growing slowly. The Fintech industry in Iran has great potential and strong infrastructure; however, comparing with the major hubs in the region, such as Abu Dhabi and Dubai, the Iranian Fintech industry requires more immersion.

A Generic Online Nonparametric Monitoring and Sampling Strategy for High-Dimensional Heterogeneous Processes

With the rapid advancement of in-process measurements and sensor technology driven by zero-defect manufacturing applications, high-dimensional heterogeneous processes that continuously collect distinct physical characteristics frequently appear in modern industries. Such large-volume high-dimensional data place a heavy demand on data collection, transmission, and analysis in practice. Thus, practitioners often need to decide which informative data streams to observe given the resource constraints at each data acquisition time, which poses significant challenges for multivariate statistical process control and quality improvement. In this article, we propose a generic online nonparametric monitoring and sampling scheme to quickly detect mean shifts occurring in heterogeneous processes when only partial observations are available at each acquisition time. Our innovative idea is to seamlessly integrate the Thompson sampling (TS) algorithm with a quantile-based nonparametric cumulative sum (CUSUM) procedure to construct local statistics of all data streams based on the partially observed data. Furthermore, we develop a global monitoring scheme of using the sum of top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${r}$ </tex-math></inline-formula> local statistics, which can quickly detect a wide range of possible mean shifts. Tailored to monitoring the heterogeneous data streams, the proposed method balances between exploration that searches unobserved data streams for possible mean shifts and exploitation that focuses on highly suspicious data streams for quick shift detection. Both simulations and a case study are comprehensively conducted to evaluate the performance and demonstrate the superiority of the proposed method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the critical challenge of online process monitoring by considering the cost-effectiveness and resource constraints in practice (e.g., limited number of sensors, limited transmission bandwidth or energy constraint, and limited processing time). Unlike the existing methodologies which rely on the restrictive assumptions (e.g., normally distributed, exchangeable data streams) or require historical full observations of all data streams to be available offline for training, this paper proposes a novel monitoring and sampling strategy that allows the practitioners to cost-effectively monitor high-dimensional heterogeneous data streams that contain distinct physical characteristics and follow different distributions. To implement the proposed methodology, it is necessary: (i) to identify sample quantiles for each data stream based on historical in-control data offline; (ii) to determine which data streams to observe at each acquisition time based on the resource constraints; and (iii) to automatically screen out the suspicious data streams to form the global monitoring statistic. Experimental results through simulations and a case study have shown that the proposed method has much better performance than the existing methods in reducing detection delay and effectively dealing with heterogeneous data streams.