Proactive Techniques Research Articles

Environmental Horticulture Management Certificate: A Path to Career Opportunities and Advancement

Florida is one of the top-producing states in the environmental horticulture industry (“green industry”), which includes garden centers as well as greenhouse, nursery, and landscape operations. Although, the industry is not without its challenges. A workforce that is knowledgeable of the issues facing the green industry and of proactive techniques that can help streamline operations and facilitate building efficiency can aid in alleviating some of the industry’s challenges. Graduates from the University of Florida Environmental Horticulture Management certificate program gain a background in sustainable ornamental horticulture practices used in the greenhouse, nursery, and/or landscape sectors of the green industry.

Addressing unaccounted-for-gas (UFG): Proactive techniques for optimal management and control

This study addresses the critical issue of Unaccounted-for-Gas (UFG) in Pakistan's gas sector, a significant challenge undermining operational efficiency and sustainability. With a comprehensive approach, the research introduces a tailored UFG Control strategy focused on "Network Visibility," "Network Rehabilitation," "Theft Control," and "Research & Development." These components aim to enhance the accuracy of gas metering and distribution, reduce losses through advanced technological interventions, and mitigate theft via robust monitoring mechanisms. By analyzing the period from 2013 to 2019, findings reveal the effectiveness of the proposed strategy in significantly improving operational efficiencies across the sector. The study also identifies key indicators for each strategy component, facilitating ongoing performance evaluation and strategic adjustments. This localized approach to UFG management not only offers a pathway to reduce gas losses but also underscores the importance of integrating modern technologies with existing infrastructure to address the sector's unique challenges. The research contributes valuable insights into the adaptation of international methodologies for the Pakistani context, highlighting the potential for regional customization in addressing UFG issues. This study endeavors to furnish policymakers and industry stakeholders with practical strategies aimed at improving the management and sustainability of Pakistan's natural gas sector.

A deep learning framework for noninvasive fetal ECG signal extraction.

Introduction: The availability of proactive techniques for health monitoring is essential to reducing fetal mortality and avoiding complications in fetal wellbeing. In harsh circumstances such as pandemics, earthquakes, and low-resource settings, the incompetence of many healthcare systems worldwide in providing essential services, especially for pregnant women, is critical. Being able to continuously monitor the fetus in hospitals and homes in a direct and fast manner is very important in such conditions. Methods: Monitoring the health of the baby can potentially be accomplished through the computation of vital bio-signal measures using a clear fetal electrocardiogram (ECG) signal. The aim of this study is to develop a framework to detect and identify the R-peaks of the fetal ECG directly from a 12 channel abdominal composite signal. Thus, signals were recorded noninvasively from 70 pregnant (healthy and with health conditions) women with no records of fetal abnormalities. The proposed model employs a recurrent neural network architecture to robustly detect the fetal ECG R-peaks. Results: To test the proposed framework, we performed both subject-dependent (5-fold cross-validation) and independent (leave-one-subject-out) tests. The proposed framework achieved average accuracy values of 94.2% and 88.8%, respectively. More specifically, the leave-one-subject-out test accuracy was 86.7% during the challenging period of vernix caseosa layer formation. Furthermore, we computed the fetal heart rate from the detected R-peaks, and the demonstrated results highlight the robustness of the proposed framework. Discussion: This work has the potential to cater to the critical industry of maternal and fetal healthcare as well as advance related applications.

Proactive Fault Tolerance in Distributed Cloud Systems: A Review of Predictive and Preventive Techniques

In a cloud computing environment, various hardware and software services are provided to the users across multiple servers and data centers. These servers are communicated to each other to allow greater scalability, flexibility, and reliability. Reliability is a vital factor in cloud computing that ensures that the requested services will be delivered to the users whenever they request them. However, different hardware or software faults may occur in cloud servers or data centers that prevent the users from receiving the service. Fault tolerance is defined as the ability of the system to provide services to the users even with the presence of faults or failures. In this review, we focused on some of the emerging fault tolerance techniques researchers have proposed to tackle the fault issues in cloud computing. We divided these techniques into three main categories: proactive and reactive techniques. Proactive techniques involve protecting the system defects by proposing certain procedures to prevent reaching the defective condition. Reactive techniques refer to the ability of the cloud system to recover the defective server or framework to continue working and providing the service.

Empowering Educators: Revolutionizing Teaching with Entrepreneurial Skills

The integration of entrepreneurial competencies into academic curricula has garnered growing attention for its ability to equip students with the mindset and abilities important for fulfilment in the 21st century. This article examines the notion of entrepreneurial instruction as a mechanism for transforming conventional pedagogical approaches. By fostering creativity, critical thinking, problem- solving skills, and a proactive technique to learning, educators can empower students to navigate a hastily evolving panorama with confidence and adaptability. Drawing upon theoretical frameworks and sensible examples, this paper elucidates the transformative impact of incorporating entrepreneurial competencies into educational practices. Through a comprehensive evaluation of the advantages, demanding situations, and exceptional practices related to entrepreneurial education, this paper aims to encourage educators to embrace innovation and domesticate an entrepreneurial attitude among their students. This shift closer to entrepreneurial education represents a departure from conventional didactic approaches, emphasising experiential learning, risk-taking and cultivating an entrepreneurial mindset. By attracting students to entrepreneurial activities consisting of developing.

DDOS Threats and Preventive Measures in IOT-Based Devices

This study explores the ever-changing landscape of IoT security in depth, with a specific emphasis given to thwarting the growing menace of Distributed Denial of Service (DDoS hereafter) assaults. A thorough literature assessment explores the sizable adoption of IoT era and associated security challenges, especially when it comes to DDoS assaults and their variations. The paper highlights the human implications of DDoS assaults throughout application, network, and transport layers, emphasizing the want for modern security features. Various studies make contributions precious insights into securing IoT devices, the potential role of block chain technology, and prevention techniques for home environments. The paper proposes a multifaceted defense mechanism, akin to fortifications in a virtual town, leveraging advanced detection techniques and continuous monitoring to limit the impact of DDoS attacks. In end, it advocates for a proactive technique, suggesting future studies instructions together with fortifying utility layers, exploring cloud computing implementations, and enhancing user awareness to ensure the resilience of IoT networks inside the face of evolving cyber threats.

Improving the quality ofhospital sterilization process using failure modes andeffects analysis, fuzzy logic, andmachine learning: experience intertiary dental centre.

Activities practiced in the hospital generate several types of risks. Therefore, performing the risk assessment is one of the quality improvement keys in the healthcare sector. For this reason, healthcare managers need to design and perform efficient risk assessment processes. Failure modes and effects analysis (FMEA) is one of the most used risk assessment methods. The FMEA is a proactive technique consisting of the evaluation of failure modes associated with a studied process using three factors: occurrence, non-detection, and severity, in order to obtain the risk priority number using fuzzy logic approach and machine learning algorithms, namely the support vector machine and the k-nearest neighbours. The proposed model is applied in the case of the central sterilization unit of a tertiary national reference centre of dental treatment, where its efficiency is evaluated compared to the classical approach. These comparisons are based on expert advice and machine learning performance metrics. Our developed model proved high effectiveness throughout the results of the expert's vote (she agrees with 96% fuzzy-FMEA results against 6% with classical FMEA results). Furthermore, the machine learning metrics show a high level of accuracy in both training data (best rate is 96%) and testing data (90%). This study represents the first study that aims to perform artificial intelligence approach to risk management in the Moroccan healthcare sector. The perspective of this study is to promote the application of the artificial intelligence in Moroccan health management, especially in the field of quality and safety management.

Condition-Based Monitoring of Kiln Induced Draft Fan in A Dry Process Cement Plant for Efficient Utilization

Associated downtime and economic loss caused by replacement/maintenance of equipment necessitated pro-active technique known as Condition-Based Monitoring to prevent/reduce failures. This study evaluated the vibration of Kiln Induced Draft (KID) of cement plant before and after failure. Vibration data was acquired using accelerometer probe and data obtained were analyzed statistically by employing t-Test at 95% confidence. Results showed that vibration signals measured in mm/s for KID motor and fan bearings (non-drive and drive ends) in the horizontal plane recorded higher values than vertical and axial planes when measured before failure occurrences. After failures, horizontal plane values increased by more than 120% while those measured in the vertical and axial planes increased by less than 100%, making horizontal plane vibration measurement a more suitable parameter for predicting the machinery health condition. The t-Test conducted showed that mean differences in values of vibration signal data before and after failure are not zero but negatives (signal before failure < signal after failure). Outputs from tests of significance using two-tailed t-Test indicated that the differences in values of vibration data signals are not significant at (p≤ 0.05) when considering “equal variance not assumed”. The non-significance of these mean differences may indicate that the present operational vibration signal levelshould be maintained at lower values in order that significant difference may be observed between periods before failure and after failure for efficient prediction of failure;as the present 1.5-3.5 mm/s range is likely not a good range for efficient failure prediction.

MultiRHM: Defeating multi-staged enterprise intrusion attacks through multi-dimensional and multi-parameter host identity anonymization

Advanced and persistent multi-staged intrusion attacks are usually conducted by elite well-resourced cyber threat actors with the goal of bypassing the defense-in-depth and zoning policies of enterprise networks and accessing critical internal assets which are embedded deep in the target network (Hutchins et al., 2011; Wilkens et al., 2021). Starting from an externally reachable host, these attacks compromise a chain of network hosts until they reach their targets. Each compromised host could provide attackers access to new systems and network zones, thus enabling them to intrude deeper into the network. While conventional detection-based mechanisms are necessary to defeat such attacks, they are not enough as they can be evaded by stealthy or zero-day attack techniques.To complement these conventional approaches, in our previous works (Jafarian et al., 2015b; 2016; 2014a), we have proposed several proactive defense techniques based on the paradigms of cyber agility and cyber deception to defeat various types of cyber intrusions through disrupting their reconnaissance. While each of these techniques can individually defeat a specific type of reconnaissance, none is individually enough to defeat different types of reconnaissance on which multi-staged intrusions rely.In this paper, we first show that defeating multi-staged intrusion attacks requires a synergistic fusion of a group of coordinated strategies for (1) disrupting both external and internal reconnaissance, (2) defeating both active (e.g., port-scanning) and passive (e.g., sniffing) reconnaissance, as well as (3) disrupting collaborative reconnaissance and information reuse as attackers laterally move inside the internal network. Then, building on our previous approaches, we present a novel proactive technique, called MultiRHM, that anonymizes different identifying attributes of network hosts (IP address, MAC address, domain name responses to rDNS queries, and fingerprints) over different dimensions (time and space) to obfuscate identities of network hosts against all aforementioned types of reconnaissance. We present the necessary architectures, protocols, and algorithms for the deployment of MultiRHM in an enterprise network. Through both theoretical analysis and simulation, we show that MultiRHM is highly effective in defeating multi-staged intrusion attacks.

Determination of Geographical and Seasonal Variations of Heavy Metals in Swordfish (Xiphias gladius) and Yellowfin Tuna (Thunnus albacares)

Bioaccumulation of heavy metals in marine fish is a major concern worldwide and it leads to some economical burdens for the tropical fish exporters. However, data is yet to be unavailable in Sri Lanka in this regard. Hence this study was conducted to explore geographical variation of heavy metal accumulation in the Swordfish (Xiphias gladius) and Yellowfin tuna (Thunnus albacares) as they play a predominant role in the export fish market in Sri Lanka. Secondary heavy metal analysis data of Mercury, Led, Arsenic and Cadmium were collected from two major fish export companies in Sri Lanka during the period of 2015 to 2017. Particular points were traced with the aid of log sheets and heavy metal variations were mapped against fish species and heavy metal concentrations by using QGIS software. Heavy metal analysis data were concentrated between 64 to 70 longitudes and 0 to 8 latitudes, 76 to 80 longitudes and 0 to 8 latitudes, 88 to 92 longitudes and 14 to18 latitudes and 82 to 90 longitudes and 6 to 14 latitudes. Heavy metals such as Cd, As and Pb were not detected in the fish samples. Mercury was the major heavy metal accumulated among the tested and high concentration was detected in Swordfish. Mercury accumulation was highest in longitudes between 64 to 70 and latitudes of 0 to 8.This shows serious health issues that are challenging to humans and provides clear evidence on marine pollution. It is recommended to prevent fishing in these particular areas. Proactive measures and techniques need to be applied to fix safe limits of heavy metals in these natural aquatic ecosystems and protect the organisms in the environment.

Improving Management Systems for Oil and Gas Producing Company using Overall Equipment Effectiveness

Consideration of difficulties inherent in maintenance management system in Nigerian oil and gas industries, and to eliminate the tedium associated in the system and improve equipment performance. In this work an attempt was made to assess the equipment maintenance practice in Kwale gas recycling plant of Nigeria Agip Oil Company Limited. Overall equipment effectiveness (OEE) indicator was used to determine the performance of various types of pumps and Turbine power generators installed in the plant, Maintenance records of the pumps and gas Turbine generators provided data for the computation and analysis. The availability, performance and quality rate of the pumps and gas Turbine generators were used to assess the overall effectiveness of the equipment used in the system. Detailed analysis of the failure rate and downtime revealed that a Plan Do Check Act (PDCA) system which is a proactive (predictive) maintenance technique was not adopted in the plant. The highest OEE value of 8.9% obtained confirmed that the current maintenance strategy and policy of the company is very much below standard and unacceptable. Hence this work affirms that the use of PDCA will avert unexpected system failure through early detection of faults, improve safety operation, reliability and durability of equipment. Thereby far less (i) human effort is wasted and (ii) energy expended, both of which led to increase in availability and profitability.

Development of Deep Neural Network Model for the Prediction of Road Crashes in Real Time

Road safety remains a global concern with the number of deaths and injury recorded from road traffic accidents estimated to be 1.5 million and 50 million respectively by 2025. Despite being predictable and largely preventable, the trend of road traffic crash is on the rise in Nigeria with an annual average of 33.7 deaths per 100,000 people. Proactive technique such as real time traffic and crash prediction has the potential to reduce the likelihood of crashes and to improve post-crash response. GoogleNet Convolutional Neural Network was developed in this study to classify road conditions and predict crashes along Ondo – Akure single carriage highway in Nigeria. Traffic flow relationships were established for the empirical data collected through video technique and compared to Green shields, Greenberg and Underwood models. The results were found generally satisfactory at an average coefficient of correlation of 0.96. The developed GoogleNet Convolutional network performed quite satisfactorily at predicting the probability of different traffic conditions – congested traffic (0.98), free-flowing traffic (0.64) and traffic crash (0.94). The developed algorithm can be integrated with traffic cameras and crowd-sourced images in areas that are not within the reach of surveillance cameras and sensors to report traffic condition in real time.

Cybersecurity Vulnerabilities in Off-Site Construction

Industry 4.0 is seeking to advance traditional construction practices towards more efficient and internet of things (IoT)-based construction practices, such as offsite construction. Offsite construction (OSC) allows for the simultaneous fabrication of building modules and onsite work. Integrating IoT technologies in construction practice is projected to improve the industry’s growth. However, there is an increase in cybersecurity vulnerabilities. Cyber threats are becoming more disruptive and targeted, resulting in monetary and infrastructure losses. Furthermore, the COVID pandemic and the instability in Europe have seen over 100% increases in cyber-attacks, and most industries have weak cybersecurity protocols. The adoption of cybersecurity frameworks in the construction industry is sluggish, and the existing security frameworks fall short in addressing the needs of the industry. This paper gives a concise review of the offsite construction value chain vulnerabilities. We explore the existing cybersecurity frameworks and identify their limitations. Cybersecurity is presented as one of the most crucial components that has received little or no attention in OSC. The future of OSC is promising with the incorporation of Industry 4.0 technologies; however, its development needs to consider more proactive security approaches and management techniques that are adapted to the current hostile cyber landscape.

Proactive Risk Assessment Through Failure Mode and Effect Analysis (FMEA) for Haemodialysis Facilities: A Pilot Project.

Haemodialysis (HD) is one of the methods for renal replacement therapy in the management of advanced chronic kidney disease through an osmosis process that allows purification of blood in the dialysis machine. The complexity of the dialytic procedure often requires the presence of a multi-specialist, multi-disciplinary team. The dialysis process is an important target for clinical risk management. Failure Mode and Effect Analysis (FMEA) is a proactive technique, considered a purposeful and dynamic tool for clinical risk management. FMEA is noted in five phases that allow a preliminary assessment of a definite process through identification and classification of risk priorities. This study represents the first of a two-phase project where FMEA is applied to HD in the setting of San Feliciano Hospital. The dialysis center performs ~12,000 dialysis sessions per year. The dialysis process is divided into different stages. A total of 31 failure modes were identified in the whole dialysis stages; more than 2/3 of the failure modes were related to the only connecting of the patient to the dialysis machine. The first phase of the study clearly remarked that the most critical step of the dialytic process is represented by the connection between the patient and the machine, as expected. Indeed, in order to have the dialysis set up, an arteriovenous fistula must be surgically created prior to the procedure and it is one of the most important issues in the HD process because of the necessity of a constant revision of it. FMEA application to HD is a useful tool, easy to be implemented and it is likely to nimbly reveal the practical and potential solutions to the critical steps of the procedure.

An Effective Fault-Tolerance Technique in Web Services: An Approach Based on Hybrid Optimization Algorithm of PSO and Cuckoo Search

Software rejuvenation is an effective technique to counteract software aging in continuously-running application such as web service based systems. In client-server applications, where the server is intended to run perpetually, rejuvenation of the server process periodically during the server idle times increases the availability of that service. In these systems, web services are allocated based on the user’s requirements and server’s facilities. Since the selection of a service among candidates while maintaining the optimal quality of service is an Non-Deterministic Polynomial (NP)-hard problem, Meta-heuristics seems to be suitable. In this paper, we proposed dynamic software rejuvenation as a proactive fault-tolerance technique based on a combination of Cuckoo Search (CS) and Particle Swarm Optimization (PSO) algorithms called Computer Program Deviation Request (CPDR). Simulation results on Web Site Dream (WS-DREAM) dataset revealed that our strategy can decrease the failure rate of web services on average 38.6 percent in comparison with Genetic Algorithm (GA), Decision-Tree (DT) and Whale Optimization Algorithm (WOA) strategies.

Authority revocation scheme for MA-CP-ABE-based secure communication in IoMT ecosystem

The proliferation of the internet of medical things (IoMT) is proving to be a disruptive technology in contemporary healthcare. However, given the sensitivity of the nature of data being shared in this system security, and privacy are critical issues. Attribute-based encryption is a proactive technique for efficient one-to-many data sharing. Specifically, multi-authority-ciphertext policy-attribute-based encryption (MA-CP-ABE) is a suitable method given the multi-stakeholder ecosystem of IoMT-based healthcare. In this work, conventional MA-CP-ABE techniques have been modified to address several existing security limitations. To the best of our knowledge, the proposed work is first to address fault tolerance in MA-CP-ABE, without any redundancy at attribute authority's level and to develop a mechanism for full and partial revocation of compromised authority. A non-monotonic fully hidden access structure further enhances the security of the scheme while efficiently performing encryption/decryption operations even for a large number of attributes, making it suitable for the secure sharing of IoMT data.

Accuracy in starphotometry

Abstract. Starphotometry, the night-time counterpart of sunphotometry, has not yet achieved the commonly sought observational error level of 1 %: a spectral optical depth (OD) error level of 0.01. In order to address this issue, we investigate a large variety of systematic (absolute) uncertainty sources. The bright-star catalogue of extraterrestrial references is noted as a major source of errors with an attendant recommendation that its accuracy, particularly its spectral photometric variability, be significantly improved. The small field of view (FOV) employed in starphotometry ensures that it, unlike sun- or moonphotometry, is only weakly dependent on the intrinsic and artificial OD reduction induced by scattering into the FOV by optically thin clouds. A FOV of 45 arcsec (arcseconds) was found to be the best trade-off for minimizing such forward-scattering errors concurrently with flux loss through vignetting. The importance of monitoring the sky background and using interpolation techniques to avoid spikes and to compensate for measurement delay was underscored. A set of 20 channels was identified to mitigate contamination errors associated with stellar and terrestrial atmospheric gas absorptions, as well as aurora and airglow emissions. We also note that observations made with starphotometers similar to our High Arctic instrument should be made at high angular elevations (i.e. at air masses less than 5). We noted the significant effects of snow crystal deposition on the starphotometer optics, how pseudo OD increases associated with this type of contamination could be detected, and how proactive techniques could be employed to avoid their occurrence in the first place. If all of these recommendations are followed, one may aspire to achieve component errors that are well below 0.01: in the process, one may attain a total 0.01 OD target error.

The impact of relational demography and communication apprehension on follower perceptions of leader–follower relationships in the hospitality industry

PurposeThe paper aims to clarify the multivariate effects of follower communication apprehension (CA) and demographic dissimilarity on follower perceptions of the leader–follower relationship quality (i.e. measured by leader–member exchange (LMX) theory). This study examined the possible mediating effects of follower CA on the relationship between demographic dissimilarity and LMX.Design/methodology/approachResearch was collected from self-report ratings of one member of the dyad; several proactive techniques were utilized to reduce common method variance in the development and administration of the survey instrument. In total, 260 (N = 260) full-time hospitality industry employees participated in the study.FindingsThe results of this study indicate demographic dissimilarity has minimal effect on LMX and CA. However, the results indicated that follower CA was negatively related to follower perceptions of LMX quality. In addition, the results indicated that tenure working with the supervisor was negatively related to follower CA and positively related to follower perceptions of LMX quality.Research limitations/implicationsResearch results may lack generalizability, and causality cannot be confirmed. Future studies utilizing longitudinal designs and/or data collected from each member of the dyad may provide support for current findings.Practical implicationsThis paper includes implications for understanding how situational characteristics influence follower CA and perceptions of LMX to encourage supervisors to increase communication with new subordinates, especially during the organizational acculturation process, thus reducing turnover. Furthermore, the significant relationship between age dissimilarity and CA offers practical implications for managing and celebrating generational differences in the workplace and bridging the gap between generations by maximizing communication between supervisors and subordinates. Communication is an essential consideration in the hospitality industry linked to creativity, relationship building and enhanced service experiences. Ultimately, this paper provides guidance for leaders to build sustainable, positive relationships with subordinates through more effective communication and build more inclusive service-based organizations.Originality/valueThis paper contributes to communication and leadership management practices by addressing four major problems: (1) limited research on situational characteristics that inhibit LMX quality, (2) limited research on communication variables as antecedents to LMX, (3) minimal research on the relationship between CA and LMX and (4) non-existent research on CA as a mediating variable in the relationship between demographic dissimilarity and LMX.

A micro-service-based machinery monitoring solution towards realizing the Industry 4.0 vision in a real environment

Abstract This work presents a modular Smart Maintenance Platform (SMP), which offers a toolbox of condition monitoring tools for the day-to-day inspection of the machinery equipment in a real industrial environment. SMP complements the manual inspection performed by the maintenance engineers with advanced data-analytics solutions that utilize both reactive and proactive techniques based on state-of-the-art machine learning proposals. Appropriate data acquisition mechanisms are built to acquire the sensorial data and transform them to suitable forms for data processing. Higher-level monitoring policies are enabled, through the fusion of multiple monitoring tasks increasing the precision of the results. The generated results are persisted for the continuous enhancement of the solution through the retraining of the proactive approach or the re-configuration of the reactive ones. The generated results are visualized in a user-friendly format, to help the maintenance engineers to assess the severity of the situation and to intervene in the machinery equipment if deemed necessary.

Presenting a new fuzzy system for web service selection aimed at dynamic software rejuvenation

As an effective technique to counteract software aging, software rejuvenation is applied in continuously running applications such as web service-based systems. In such systems, web services are allocated depending on the requirements of receivers and the facilities of servers. One of the challenging issues during assignment of web services is how to select the appropriate server to minimize faults. In this paper, we proposed dynamic software rejuvenation in the form of a proactive fault-tolerance technique based on fuzzy system. While including a threshold for the rejuvenation of each web service, we carried out the training phase based on the features of the service providers as well as the receivers’ requirements. The results of simulations revealed that our strategy can mitigate the failure rate of web services by 45, 40, 23, and 12% in comparison with the non-fuzzy, regression-based, Markov-based, and ACOGELS-based web service rejuvenation strategies, respectively.