Search Results (303)

Operations management personnel emphasize the maintenance of workforce empowerment on the shop floor. This is made possible by implementing effective operations and human resource management practices. However, organizations are adept at controlling the workforce empowerment domain within operational scenarios. In the current industry revolution scenario, industry personnel often face failure due to a laggard mindset in the face of industry revolutions. There are higher possibilities of failure because of standardized operations controlling the shop floor. Organizations utilize well-established human resource concepts, including McClelland’s acquired needs theory, Herzberg’s two-factor theory, and Maslow’s hierarchy of needs, in order to enhance the workforce’s performance on the shop floor. Current SME individuals require fast-paced approaches for tracking the performance and idleness of a workforce in order to control them more efficiently in both flexible and transformational stages. The present study focuses on investigating the parameters and factors that contribute to workforce empowerment in an industrial revolution scenario. The present research is used to develop a framework utilizing operations and human resource management approaches in order to identify and address the issues responsible for deteriorating workforce contributions. The framework includes HRM and operations management practices, including Herzberg’s two-factor theory, Maslow’s theory, and lean and smart approaches. The developed framework contains four phases for achieving desired outcomes on the shop floor. The developed framework is validated by implementing it in a real-life electric vehicle manufacturing organization, where the human resources and operations team were exhausted and looking to resolve employee-related issues instantly and establish a sustainable work environment. The current industry is transforming from Industry 3.0 to Industry 4.0, and seeks future-ready innovations in operations, control, and monitoring of shop floor setups. The operations management and human resource management practices teams reviewed the results over the next three months after the implementation of the developed framework. The results revealed an improvement in workforce empowerment within the existing work environment, as evidenced by reductions in the number of absentees, resignations, transfer requests, and medical issues, by 30.35%, 94.44%, 95.65%, and 93.33%, respectively. A few studies have been conducted on workforce empowerment by controlling shop floor scenarios through modifications in operations and human resource management strategies. The results of this study can be used to fulfil manufacturers’ needs within confined constraints and provide guidelines for efficiently controlling workforce performance on the shop floor. Constraints refer to barriers that have been decided, including production time, working time, asset availability, resource availability, and organizational policy. The study proposes a decision-making plan for enhancing shop floor performance by providing suitable guidelines and an action plan, taking into account both workforce and operational performance. Full article

Objective: The present study is a qualitative analysis of participant experiences and perspectives from people who received cognitive remediation therapy for adult obesity (CRT-O). Method: Post-intervention data were generated from an open-ended question requesting the participants to write, in the form of a letter to their therapist, about their experiences and reflections upon taking part in cognitive remediation therapy for adult obesity. Participants’ letters were thematically analyzed. Results: Four themes and nested subthemes emerged from participant responses, including (1) motivation and initial response to CRT-O for the adult obesity study eligibility process with the nested subthemes of initial apprehension pre-intervention and awareness and acknowledgement of one’s problematic eating behaviors; (2) perceived benefits from cognitive remediation therapy for adult obesity with the nested subthemes of the strategies and techniques that were found beneficial and the role of the cognitive remediation therapy for adult obesity therapists in facilitating positive change; (3) perceived outcomes post-intervention with the nested subthemes of changed relationship with food, self-acceptance and gaining control to effect positive lifestyle change; and (4) expectations and beliefs about the longer-term impact of cognitive remediation therapy for adult obesity with the nested subthemes of using the cognitive remediation therapy for adult obesity strategies as a lifestyle routine, apprehension about not having follow-up therapist support, and concern about potential relapse. Conclusion: Our analysis found helpful insights into the consumer perception of this novel intervention and highlighted the clinical utility of implementing cognitive remediation therapy in those living with a higher body weight. Full article

We propose a hub ridesharing method that considers path similarity to swiftly evacuate high volumes of passengers arriving at a high-speed railway hub. The technique aims to minimize total mileage and the number of service vehicles, considering the characteristics of hub passengers, such as the constraints of large luggage, departure times, and arrival times. Meanwhile, to meet passengers’ expectations, a path morphology similarity indicator combining directional and locational features is developed and used as a crucial criterion for passenger matching. A two-stage algorithm is designed as a solution. Passenger requests are clustered based on path vector similarity in the first stage using a heuristic approach. In the second stage, we employ an adaptive large-scale neighborhood search to form passenger matches and shared routes. The experiments demonstrate that this method can reduce operational costs, enhance computational efficiency, and shorten passenger wait times. Taking path similarity into account significantly decreases passenger detour distances. It improves the Jaccard coefficient (JAC) of post-ridesharing paths, fulfilling the passenger’s psychological expectation that the shared route will closely resemble the original one. Full article

Serum bactericidal assay (SBA) is a functional assay that evaluates infection- and vaccine-induced neutralizing antibodies representing the serological correlate of protection against Neisseria meningitidis. However, it is time consuming due to its readout using the enumeration of colony-forming units (CFUs), making this conventional SBA (C-SBA) difficult for large-scale use. We developed a new SBA method that takes advantage of a bioluminescence N. meningitidis serogroup B (BioLux-SBA). The assay development steps involved the human complement source validation, the setup of the optimal incubation time, and the assessment of intra-day and inter-day variability. BioLux-SBA was then compared to C-SBA using a serum collection of Norman children vaccinated in 2011 with MenBvac, an OMV meningococcal vaccine. While a conventional approach requests 48 h of work to test 24 sera per day, BioLux-SBA takes only 5 h to test 96 sera per day. The SBA titers (n = 10) correlated with R² of 0.98 (p-value < 0.0001). The deposition of terminal complement components (C5b-C9) measured by flow cytometry on the bacterial surface well correlated with BioLux SBA titers. This high-throughput method to evaluate the immunogenicity of meningococcal vaccines appears to be a reliable method for an OMV meningococcal B vaccine and requires further assessment in other laboratories and against other meningococcal vaccines. Full article

The transport sector is responsible for about 60% of emissions in the atmosphere due to the exhaust-polluting gases of internal combustion engine (ICE) vehicles. An effective solution to this issue is the electrification of the transport means, which can significantly reduce pollution, especially in urban areas. Apart from the necessary technological advancements that must improve the battery performances, the diffusion of electric vehicles (EVs) must be further supported and facilitated by new dedicated services and tools for electric vehicle users and operators aiming at improving the travel and charging experience. To this goal, this paper proposes new models based on Timed Colored Petri Nets (TCPN) to simulate and manage the charge demand of the EV fleet. At first, the proposed tool must take into account the charging requests from different EV drivers with different charging need located in different geographical areas. This is possible by knowing input data such as EV current location, battery data, charge points (CPs) availability, and compatibility. In particular, EV drivers are simulated when finding and booking the preferred charge option according to the available infrastructure in the area of interest and the CPs tariff and power rate. The proposed TCPN is designed to model the multi-user charging demand in specific geographic areas, and it is evaluated in several scenarios of a case study to measure its performance in serving multiple EV users. Full article

As aviation demand rises, fossil jet fuel consumption follows, thus increasing focus on sustainable aviation fuels to reduce aircraft greenhouse gas emissions. While advanced technologies and optimized operations play a role, alternative fuels, especially non-drop-in options like Liquefied Natural Gas (LNG) and methanol, offer promising potential for significant emission reductions if used in current aero-engines. LNG, a candidate near-term replacement of fossil jet fuel and methanol, even though a less conventional option in aviation, present advantages. Both fuels showcase the ability to generate the same thrust output by also achieving lower post-combustion temperatures, thereby enhancing component life and reducing emissions. Inversely, requesting equal post-combustion temperature as the baseline kerosene operation of the engine can produce greater thrust output, a much needed result for such fuels with low volumetric energy density, which causes greater take-off thrust demand mainly due to their larger tank requirements. This study uses advanced 0-D engine models coupled with detailed chemistry 1-D burner models and mission analysis tools to assess the aforementioned trends of LNG and methanol used to power a current geared turbofan engine. The aim of this work is to provide insights into the advantages, the limitations and the overall viability of the fuels in question as less polluting aviation fuels, addressing both environmental impact and operational feasibility in future aviation applications. According to findings of this article, when compared with Jet-A, LNG can reduce post-combustion temperature by an average of 1% or increase net-thrust by 3% while lowering $C{O}_2$, $N{O}_x$ and $CO$ emissions by 20%, 46% and 39%, respectively. Adversely, methanol is capable of lessening post-combustion temperature by 3% or enhancing thrust output by 10% while also reducing $C{O}_2$, $N{O}_x$ and $CO$ emissions by an average of 6%, 60% and 38%, respectively. Full article

Future autonomous vehicles will interact with traffic infrastructure through roadside units (RSUs) directly connected to transportation field cabinets (TFCs). These TFCs must be interconnected to share traffic information, enabling infrastructure-to-infrastructure (I2I) communications that are reliable, synchronous and capable of transmitting vehicle data to the Internet. However, I2I communications present a complex optimization challenge. This study addresses this by proposing the design, implementation, and evaluation of an automated management model for I2I service channels based on multi-agent reinforcement learning (MARL) integrated with deep reinforcement learning (DRL). The proposed models efficiently manage the routing and scheduling of data frames between internet of vehicles (IoV) infrastructure devices through time-sensitive networking (TSN) to ensure real-time synchronous I2I communications. The solution incorporates both a routing model and a scheduling model, evaluated in a simulated shared environment where agents operate within the TSN control plane. Both models are tested for different topologies and background traffic levels. The results demonstrate that the models establish the majority of paths in the scenario, adhering to near-optimal routing and scheduling policies. Recursively, for each individual request to create a service channel, the system establishes online an optimal synchronous path between entities with a limited time budget. In total, 71% of optimal routing paths are established and 97% of optimal schedules are achieved. The approach takes into account the periodic nature of the transmitted data and its robustness through TSN networks, obtaining 99 percent of compliant service requests with flow jitter levels below 100 microseconds for different topologies and different network utility percentages. The proposed solution achieves lower execution delays compared to the iterative ILP approach. Additionally, the solution facilitates the integration of 5G networks for vehicle-to-infrastructure (V2I) communications, which is identified as an area for future exploration. Full article

Ensuring the driver’s readiness to take over before a takeover request is issued by an autonomous driving system is crucial for a safe takeover. However, current takeover prediction models suffer from poor prediction accuracy and do not consider the time dependence of input features. In this regard, this study proposes a hybrid LSTM-BiLSTM-ATTENTION algorithm for driver takeover performance prediction. By building a takeover scenario and conducting experiments in the driving simulation experimental platform under the human–machine co-driving environment, the relevant state indicators in the 15 s per second before the takeover request is sent are extracted from three perspectives, namely, driver state, traffic environment, and personal attributes, as model inputs, and the level of takeover performance was labeled; the hybrid LSTM-BiLSTM-ATTENTION algorithm is used to construct a driver takeover performance prediction model and compare it with other five algorithms. The results show that the algorithm proposed in this study performs optimally, with an accuracy of 93.11%, a precision of 93.02%, a recall of 93.28%, and an F1 score of 93.12%. This study provides new ideas and methods for realizing the accurate prediction of driver takeover performance, and it can provide a decision basis for the safe design of self-driving vehicles. Full article

The Zero Trust Architecture (ZTA) security system follows the “never trust, always verify” principle. The process constantly verifies users and devices trying to access resources. This paper describes how Microsoft Azure uses ZTA to enforce strict identity verification and access rules across the cloud environment to improve security. Implementation takes time and effort. Azure’s extensive services and customizations require careful design and implementation. Azure administrators need help navigating and changing configurations due to its complex user interface (UI). Each Azure ecosystem component must meet ZTA criteria. ZTAs comprehensive policy definitions, multi-factor and passwordless authentication, and other advanced features are tested in a mid-size business scenario. The document delineates several principal findings concerning the execution of Azure’s ZTA within mid-sized enterprises. Azure ZTA significantly improves security by reducing attack surfaces via ongoing identity verification, stringent access controls, and micro-segmentation. Nonetheless, its execution is resource-demanding and intricate, necessitating considerable expertise and meticulous planning. A notable disparity exists between theoretical ZTA frameworks and their practical implementation, characterized by disjointed management interfaces and user fatigue resulting from incessant authentication requests. The case studies indicate that although Zero Trust Architecture enhances organizational security and mitigates risks, it may disrupt operations and adversely affect user experience, particularly in hybrid and fully cloud-based settings. The study underscores the necessity for customized configurations and the equilibrium between security and usability to ensure effective ZTA implementation. Full article

Recent advancements in intelligent tutoring systems (ITS) driven by artificial intelligence (AI) have attracted substantial research interest, particularly in the domain of computer programming education. Given the diversity in learners’ backgrounds, cognitive abilities, and learning paces, the development of personalized tutoring strategies to support the effective attainment of learning objectives has become a critical challenge. This paper introduces personalized fuzzy logic strategies for intelligent programming tutoring (PerFuSIT), an innovative fuzzy logic-based module designed to select the most appropriate tutoring strategy from five available options, based on individual learner characteristics. The available strategies include revisiting previous content, progressing to the next topic, providing supplementary materials, assigning additional exercises, or advising the learner to take a break. PerFuSIT’s decision-making process incorporates a range of learner-specific parameters, such as performance metrics, error types, indicators of carelessness, frequency of help requests, and the time required to complete tasks. Embedded within the traditional ITS framework, PerFuSIT introduces a sophisticated reasoning mechanism for dynamically determining the optimal instructional approach. Experimental evaluations demonstrate that PerFuSIT significantly enhances learner performance and improves the overall efficacy of interactions with the ITS. The findings highlight the potential of fuzzy logic to optimize adaptive tutoring strategies by customizing instruction to individual learners’ strengths and weaknesses, thereby providing more effective and personalized educational support in programming instruction. Full article

This study presents a Warehouse Robot Localization and Communication System prototype to optimize battery management and workflow in warehouses. Autonomous mobile robots equipped with advanced localization and wireless communication technologies coordinate to prevent downtime. When the battery level of the robot drops below a certain threshold, it communicates with the main computer to request assistance. Another robot then takes over its task, allowing the low-battery robot to reach a charging station. Using an overhead camera module and an A* algorithm for optimal pathfinding, robots navigate efficiently. A Python-based user interface enables monitoring and control. This prototype system has the potential for industrial applications with future enhancements. Full article

Background/Objectives: The present work aimed to compare the effects of the standardized dry extract from the leaves of Monteverdia ilicifolia, popularly known as “espinheira-santa”, with omeprazole in the management of dyspepsia related to gastroesophageal reflux disease (GERD). Methods: A double-blind, randomized, non-inferiority and double-dummy clinical trial was conducted. In total, 86 patients with GERD symptoms were randomized into three groups: Omeprazol (20 mg), M. ilicifolia (400 mg), or M. ilicifolia (860 mg). Capsules were provided by SUSTENTEC^®, Pato Bragato, Brazil. It was requested that the participants take three capsules before breakfast and dinner for 4 weeks. Clinical outcomes were obtained at the beginning and end of the study, with GERD symptoms (QS-GERD), the impact of heartburn symptoms on quality of life (HBQOL), and medical records. Results: Overall, 75.6% of the participants showed adherence without any differences among the experimental groups. All groups had significant reductions in both QS-GERD and HBQOL scores. Omeprazole and 400 and 860 mg of M. ilicifolia decreased the QS-GERD total scores at the endpoint compared to the baseline (Chi-square = 129.808; p < 0.0001), as well as individual item scores, such as heartburn intensity (Chi-square = 93.568, p < 0.0001) and heartburn after meals (Chi-square = 126.426, p < 0.0001). There were no differences among the experimental groups after the intervention. Conclusions: Our results suggest that capsules with a standardized dry extract from the leaves of M. ilicifolia at a dosage of 400 or 860 mg are non-inferior to omeprazole, a proton pump inhibitor. Full article

Leveraging the photometric data of the Sloan Digital Sky Survey and the Galaxy Evolution Explorer (GALEX), we construct mean/median spectral energy distributions (SEDs) for unique bright quasars in redshift bins of 0.2 and up to $z\simeq 3$, after taking the GALEX non-detection into account. Further correcting for the absorption of the intergalactic medium, these mean/median quasar SEDs constitute a surprisingly redshift-independent mean/median composite SED from the rest-frame optical down to ≃$500 \mathring{\mathrm{A}}$ for quasars with bolometric luminosity brighter than ${10}^{45.5}\mathrm{erg}{\mathrm{s}}^{-1}$. Moreover, the mean/median composite quasar SED is plausibly also independent of black hole mass and Eddington ratio, and suggests similar properties of dust and gas in the quasar host galaxies since cosmic noon. Both the mean and median composite SEDs are nicely consistent with previous mean composite quasar spectra at wavelengths beyond ≃$1000 \mathring{\mathrm{A}}$, but at shorter wavelengths, are redder, indicating, on average, less ionizing radiation than previously expected. Through comparing the model-predicted to the observed composite quasar SEDs, we favor a simply truncated disk model, rather than a standard thin disk model, for the quasar central engine, though we request more sophisticated disk models. Future deep ultraviolet facilities, such as the China Space Station Telescope and the Ultraviolet Explorer, would prompt revolutions in many aspects, including the quasar central engine, production of the broad emission lines in quasars, and cosmic reionization. Full article

Electric-powered vehicles (EVs) allow for an environmentally friendly and economic alternative to fuel-running ones. However, such an alternative is expected to impose further usage hikes and periods of instability on cities’ power systems. From their perspective, cities need to scale their infrastructure grids to allow for adequate power resources to feed such new power-hungry consumers. Indeed, for such a green alternative to proceed, our power grids need to be ready to cope with any unexpected hikes in the power consumption rates without compromising the stability of the services provided to our homes and workplaces. Operators’ steps in this path are still modest, and the coverage of EV charging stations is still insufficient as they are trying to avoid any further costs for upgrading their infrastructures. The lack of price consideration for the charging services offered at charging stations may result in EV drivers paying higher costs compared to traditional fuel vehicles to charge their EVs’ batteries, hindering the economic incentive of owning such sorts of vehicles. Hence, it may take a while for sufficient coverage to exist. Although for drivers the adoption of EVs represents a city-friendly alternative with affordable expenses, it usually comes with range anxiety and battery charging concerns. In this work, we are presenting e-Fuel, a charge-sharing model that allows for preference-based mobile EV charging services. In e-Fuel, we are proposing a stable weight-based vehicle-to-vehicle matching algorithm, through which drivers of EVs will be capable of requesting instant mobile charge-sharing service for their EVs. In addition to being mobile, such charging services are customized, as they are chosen based on the drivers’ preferences of price-per-unit, charging speed, and time of delivery. The developed e-Fuel matching algorithm has been tested in various environments and settings. Compared to the benchmark price-based matching algorithm, the resulting matching decisions of e-Fuel come with balanced matching attributes that mostly allow for 6- to 7-fold shorter service delivery times for a minimal increase in service charges that vary between 9% and 65%. Full article

South Asians living in the United States are frequently underrepresented in health research. Their lack of participation limits the generalizability of research to them and keeps them from receiving the high-quality care and innovation that some studies may offer. “Research Ready” is a five-panel, community co-created graphic-style story that encourages discussion around the purpose of research, safety/protection while participating, and why diverse participation—including South Asians—improves study results and leads to more effective interventions/treatments. This study leveraged trained young adult “cultural insiders” to invite attendees of a Midwestern South Asian Cultural Festival to read the story aloud together as the characters in English or Hindi and used a decision guide to invite discussion. Post-discussion surveys (N = 104) were analyzed using descriptive statistics. Participants spanned from 10 to 79 years, with 42% < 18 years and more females (61%). Only 18.3% indicated having prior research participation. Adults 40+ years (60%) requested the story/discussion in Hindi, compared to 2.3% of adolescents and 6.7% of younger adults. After the discussion, participants indicated their willingness to consider participation, with most being open to participating in surveys/interviews (95.2%); only 52.9% would consider studies requiring the taking of medicines. Adolescents, females, and adults with higher education were more willing to participate in medication studies. Nearly all (97.1%) said they would feel safe participating in research, and 88.5% shared that the discussion would help them better decide about future participation. In conclusion, “Research Ready” discussions shared by cultural insiders effectively encourage South Asians to consider future research participation. Full article