Use Of System Research Articles

The effect of incarceration on political beliefs for vulnerable populations

AbstractObjectiveThis article examines whether incarceration influences a change in political beliefs for those incarcerated and the extent to which it affects more vulnerable groups, including women and people of color.MethodI use a series of logit models to analyze responses to the Marshall Project's 2020 prison survey.ResultsMy analysis reveals a significant change in political beliefs since being incarcerated. There is an increased effect of changing political beliefs for women and people of color incarcerated. The effect reveals that people of color are becoming, either for the first time or further aligned, with the Republican Party since being incarcerated.ConclusionThe experience of violence and abuse while incarcerated extends the tools of white supremacy in the prison system by influencing feelings of shame, hopelessness, and cultural inferiority, further aligning vulnerable groups to conservatism and whiteness. My Incapacitation theory begins to explain the change in political beliefs due to the carceral system's use of incapacitation and its long‐term effect on political behavior of incarcerated groups.

Design and Implementation of a High-Performance VLSI Architecture for Canonical Huffman Encoding

A key component of contemporary computer systems, data compression makes it possible for information to be stored and transmitted efficiently. A popular technique for lossless data compression, Huffman coding can drastically cut down on data size without sacrificing intensity. But conventional Huffman encoding techniques can have scalability and speed issues, especially when used in hardware. A high-throughput Very Large-Scale Integration (VLSI) design for a Canonical Huffman Encoder is shown in this study. To accomplish quick and effective encoding, the suggested approach makes use of parallel processing and improved algorithms. The system's use of the canonical Huffman algorithm lowers computational complexity and streamlines hardware implementation without sacrificing compression performance. The architecture incorporates real-time operation-optimized modules for frequency analysis, code generation, and symbol encoding. Results from simulations show that the suggested design achieves significantly higher throughput compared to conventional approaches, making it suitable for applications in data storage, multimedia, and communication systems. This study contributes to advancing VLSI designs for high-performance hardware Key Words: Data Compression, Huffman Coding, Lossless Compression, Very Large-Scale Integration (VLSI), Canonical Huffman Encoder, High-Throughput Encoding, Parallel Processing, Efficient Encoding, Hardware Implementation, Computational Complexity, Code Generation, Symbol Encoding, Frequency Analysis, Real-Time Operation, Throughput Optimization, Multimedia Systems, Communication Systems, Data Storage, Hardware Architecture, VLSI Design, Performance Optimization, Simulations, Compression Performance, Hardware Design, Throughput Enhancement, System-Level Optimization, Power Efficiency, Resource Management, Data Transfer Efficiency, Low-Cost Implementation.

Novel load allocation analysis for energy management and carbon emissions reduction of chiller system in hotel buildings

The operation of chiller systems for cooling energy generation in hotel buildings consumes the highest proportion of electricity. This study aims to introduce load allocation analysis to assess the potential for energy savings by a chiller system. The cooling load profile of a hotel in a subtropical climate is simulated using EnergyPlus. Eleven design options for the system include 4 to 8 chillers of the same or different capacities. Restoring chiller sequencing with high load operation can reduce the system's energy use intensity (EUI) by 12.7 %. An optimal design of four large and two small chillers with proper sequencing can reduce the EUI by up to 26.82 % or carbon emissions by 551,516 kgCO2-e. Increasing capacity steps above ten brings no significant energy savings and imposes complicated chiller staging. The area of the load allocation region can be evaluated simply from the chiller system design and used to analyze the system's energy performance. The EUI can be reduced by 0.3261 % for a 1 % drop in the area of load allocation region. The novelty of this study lies in using the load allocation region to assess energy savings of a chiller system without referring to sophisticated operating data at short timesteps. The significance of this study is to highlight the limitations of energy savings from the optimum chiller system design and address the need for incorporating more low energy technologies to enhance decarbonization.

Health system's use of technology boosts worker engagement in MH care

Health system's use of technology boosts worker engagement in MH care

Pediatric Complex Chronic Condition System Version 3

Since implementation of the International Statistical Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) in the US, thousands of new or related codes have been added to represent clinical conditions. The widely used pediatric complex chronic condition (CCC) system required a major update from version 2 (V2) to version 3 (V3) to capture the range of clinical conditions represented in the ICD-10-CM. To update the CCC V3 system, creating V3, with new, missing, or retired codes; to reconceptualize the system's use of technology codes; and to compare CCC V3 with V2. This repeated cross-sectional study examined US hospitalization data from the Pediatric Health Information System (PHIS) and the Medicaid Merative MarketScan Research Databases from January 1, 2009, to December 31, 2019, for all patients aged 0 to 18 years. Data were analyzed from March 1, 2023, to April 1, 2024. The CCCs were identified in both data sources using the CCC V2 and V3 systems. The (1) percentage of pediatric hospitalizations associated with a CCC, (2) numbers of CCC body-system categories per patient, and (3) explanatory power for hospital length of stay and in-hospital mortality were compared over time for V3 vs V2. Among 7 186 019 hospitalizations within PHIS, 54.3% patients were male, the median age was 4 years (IQR, 1-11 years), and 51.2% were aged 0 to 4 years). The CCC V2 identified 2 878 476 (40.1%) patients as having any CCC compared with 2 753 412 (38.3%) identified by V3. In addition, V2 identified 100 065 (1.4%) patients with transplant status compared with 146 683 (2.0%) by V3, and V2 identified 914 835 (12.7%) as having technology codes compared with 805 585 (11.2%) by V3. The 2 systems were similar in accounting for the number of CCC body-system categories per patient and in explaining variation in hospital length of stay and in-hospital mortality. For both V2 and V3, 10.0% of the variance in hospital length of stay and 12.0% of the variance in in-hospital mortality was explained by the presence of a CCC. Similar patterns were observed when analyzing the 2 999 420 Medicaid Merative MarketScan Research Databases' hospitalizations (52.3% of patients were male, the median age was 1 year [IQR, 0-12 years], and 62.0% were 0 to 4 years old), except that the percentages of identified CCCs were all lower: V2 identified 758 110 hospitalizations (25.3%) with any CCC compared with 718 100 (23.9%) identified by V3. These results suggest that, moving forward, V3 should be used to identify CCCs, and ongoing, frequent updates to V3, using a transparent, structured process, will enable V3 to accurately reflect the evolving spectrum of clinical conditions represented in the ICD-10-CM.

Optical, physical, mechanical, structural, and radiation shielding investigations of B2O3–TeO2-GeO2-MgO-PbO for ionizing protection and optical transmission application

This study aims to investigate a new heavy glass system's potential use in the radiation shielding and optical transmission applications. Glass former like borate is replaced in this work by heavy metal oxides like lead oxide in the following glass system: (35-x) B2O3–20TeO2-10GeO2-35MgO-xPbO (where x = 2.5, 5, 7.5, and 10 mol%). Through the melt-annealing process, four transparent glasses were synthesized. A number of physical properties were reported, namely the boron–boron separation x 10−8 m, field strength x 1018 (cm2), inter nuclear distance x 10−8 m, ion concentration (N) x 1023 (ion cm3), molar volume (cm3/mol), molecular weight (g/mol), and polaron radius x 10−9 m. Moreover, the following mechanical and structural properties of the synthesized glasses were explored: bulk modulus (B, GPa), dissociation energy (kcal/cm3), fractal bond connectivity (d), longitudinal modulus (L, GPa), hardness (H, GPa), oxygen packing density, packing density (Vt), oxygen molar volume, packing factor (Vi), shear modulus (S, GPa), Poisson radius (σ), and Young modulus (Y, GPa). To study the bulk glasses' nature, the X-ray diffraction was examined. Optical absorption along with optical transmission profile have been recorded to extract various optical properties, as well as the direct and indirect band gap. The transmittance spectra of the prepared glass samples' FTIR were investigated within the 400–2000 cm−1 range. This allowed for the examination of molecular structural groups and vibrational modes. Radiation shielding parameters have been calculated and studied using XCOM software and MCNP-5 algorithm.

"Nothing Is More Powerful than Words:" How Patient Experience Narratives Enable Improvement.

Patient experience narratives (narratives) are an increasingly important element of both measurement approaches and improvement efforts in healthcare. Prior studies show that narratives are considered by both clinicians and staff to be an appealing, meaningful, and credible form of evidence on performance. They also suggest that making concrete use of narratives within organizational settings to improve care can be complex and challenging. Our qualitative study was designed to explore how middle managers working in a health system's outpatient clinics value and use written narratives in their day-to-day work. We conducted qualitative interviews with 20 middle managers working in 8 outpatient clinics. Interviews were fully transcribed, loaded into MAX-QDA software, and coded using thematic analysis techniques. Code reports were extracted and reanalyzed for subthemes related to the objectives of this paper. Middle managers across sites described valuing narratives as a tool to: enable better patient experience assessment by augmenting data from patient experience scores; deepen understanding of and relationships with patients; provide insight about operational issues; identify areas for needed improvement and potential solutions; and facilitate strategic work. They reported using narratives for a range of activities related to their roles as supervisors, such as focusing attention on positive practices and needed improvements, promoting deeper group learning, motivating change, reinforcing sense of purpose for staff, recognizing staff strengths and training needs, and inspiring transformational thinking. Finally, interviewees reported numerous specific quality improvement projects (both short- and longer-term) that were informed by narratives-for example, by identifying an issue to be addressed or by suggesting a workable solution. Together, these interviews suggest a collective "narrative about narratives" woven by these organizational actors-a story which illustrates how narratives are highly relevant for how middle managers derive meaning from their work, put organizational values such as responsive service provision into practice, and enact their roles as supervisors. Our results add to the nascent literature a detailed description of how narratives can be used both as a tool for middle managers in their leadership and supervisory roles, and as a blueprint for improvement work within outpatient settings. They also illuminate why patient experience scores may improve when narrative data are collected and used. Finally, our results suggest that for middle managers, perhaps "nothing is more powerful than words" because narratives function as both an insight provider and a compelling tool that adds direction and meaning to workplace endeavors.

Enhancing Performance Metrics: A Google Looker Studio Approach to Key Performance Indicator (KPI) Management System for Homecorp Offshore Drafting Team

The Homecorp Offshore Drafting Key Performance Indicator (KPI) Management System is a comprehensive process that aims to improve the management and monitoring of key performance indicators within the drafting department. This system utilizes Google Looker Studio, which provides a user-friendly real-time data analysis and visualization platform. It enables managers to make well-informed decisions and enhance departmental performance. The process begins with collecting daily task data, logged manually or through automated Google Apps Scripts into a Google Sheets database. This data includes task details such as type, status, and completion time. A KPI-pointing system, developed through focus group discussions, also assigns point values to tasks based on their complexity and time requirements. Integrating Google Looker Studio with Google Sheets allows real-time synchronization and web-based monitoring of key performance indicators (KPIs). The dashboard offers detailed insights such as task counts, average KPIs, and monthly performance graphs, providing a comprehensive performance evaluation. The implementation of this system has been well-received by the team, with survey results indicating improvements in tracking performance metrics, ease of use, and enhanced collaboration. While these outcomes are positive, there is still a recognized need for further refinement, particularly in incorporating qualitative data and improving IT support. The KPI Management System has significantly enhanced performance monitoring and management for the drafting team. To keep the system up-to-date and effective, ongoing feedback and iterative improvements must be maintained. Potential future enhancements could include extending the system's use company-wide to foster a culture of continuous improvement and accountability.

Unlocking the distinctive enzymatic functions of the early plant biomass deconstructive genes in a brown rot fungus by cell-free protein expression.

Saprotrophic fungi that cause brown rot of woody biomass evolved a distinctive mechanism that relies on reactive oxygen species (ROS) to kick-start lignocellulosic polymers' deconstruction. These ROS agents are generated at incipient decay stages through a series of redox relays that shuttle electrons from fungus's central metabolism to extracellular Fenton chemistry. A list of genes has been suggested encoding the enzyme catalysts of the redox processes involved in ROS's function. However, navigating the functions of the encoded enzymes has been challenging due to the lack of a rapid method for protein synthesis. Here, we employed cell-free expression system to synthesize four redox or degradative enzymes, which were identified, by transcriptomic data, as conserved players of the ROS oxidation phase across brown rot fungal species. All four enzymes were successfully expressed and showed activities that enable confident assignment of function, namely, benzoquinone reductase (BQR), ferric reductase, α-L-arabinofuranosidase (ABF), and heme-thiolate peroxidase (HTP). Detailed analysis of their catalytic features within the context of brown rot environments allowed us to interpret their roles during ROS-driven wood decomposition. Specifically, we validated the functions of BQR as the driver redox enzyme of Fenton cycles and reconstructed its interactions with the co-occurring HTP or laccase and ABF. Taken together, this research demonstrated that the cell-free expression platform is adequate for synthesizing functional fungal enzymes and provided an alternative route for the rapid characterization of fungal proteins, escalating our understanding of the distinctive biocatalyst system for plant biomass conversion.IMPORTANCEBrown rot fungi are efficient wood decomposers in nature, and their unique degradative systems harbor untapped catalysts pursued by the biorefinery and bioremediation industries. While the use of "omics" platforms has recently uncovered the key "oxidative-hydrolytic" mechanisms that allow these fungi to attack lignocellulose, individual protein characterization is lagging behind due to the lack of a robust method for rapid synthesis of crucial fungal enzymes. This work delves into the studies of biochemical functions of brown rot enzymes using a rapid, cell-free expression platform, which allowed the successful depictions of enzymes' catalytic features, their interactions with Fenton chemistry, and their roles played during the incipient stage of brown rot when fungus sets off the reactive oxygen species for oxidative degradation. We expect this research could illuminate cell-free protein expression system's use to fulfill the increasing need for functional studies of fungal enzymes, advancing the discoveries of novel biomass-converting catalysts.

Enhancing and Securing a Real-Time Embedded Face Recognition System using Raspberry Pi

The modern world is full of data of all kinds; however, the vast amount of video and image data available provides the data set needed for facial recognition. Facial recognition is crucial in safety and surveillance systems that analyze visual data and millions of images. Using facial recognition software, a person's identity can be verified through a variety of media images. For facial recognition, there is a variety of algorithms available. The article presents an approach to a face recognition framework using Haar Cascade, a biometric technology in safety and surveillance systems. It investigates combining standard machine learning techniques for face detection and identification with Raspberry Pi face detection, a cost-effective and easy-to-use embedded system. The system detects faces from indirect and direct images, achieving high speed using the latest Raspberry Pi 4 and Python libraries. The work demonstrates a machine-learning-based design method and a complete embedded system. The face detection accuracy is 92%, and the average time is 0.35 compared to the local binary model (LBP). Many facial recognition algorithms on the web and in literature reviews are vulnerable to image attacks. These methods are very effective in identifying faces in webcams, video streams, images, and videos. This system's use of the Raspberry Pi 4 and advanced Python libraries results in fast and accurate real-time face detection. This paper extends the work first presented at the 12th Iranian/Second International Conference on Machine Vision and Image Processing (MVIP).

Artificial intelligence in clinical applications

Modern medicine has improved to the point that intelligent diagnostic tools and auxiliary medical technology, such as surgical robots and image analysis systems, are now widespread in clinical settings. In clinical practice, the performance of different surgical robots and image analysis systems is very different, which seriously limits the use of complex medical scenes. The algorithm models and robotic arms that these intelligent robots and the supporting systems rely on have being recognized by the researchers. In this study, hardware and software algorithms are introduced one at a time, with a focus on the Da Vinci medical robot arm systems, the control mechanisms that run the surgical task optimization tools, in particular Proportional-Integral-Derivative (PID) and Remote Center of Motion (RCM), and the image algorithm active contour model that significantly increased the accuracy of tumor localization. Also provided are suggestions for improving the system's use, its limits, and future research possibilities.

Associations of the Swiss national reporting system's antimicrobial use data and management practices in dairy cows on tiestall farms

Antimicrobial use (AMU) in Switzerland is above target and requires reduction, especially in dairy cattle. Measuring AMU is pivotal to identifying starting points for AMU reduction, and so are studies investigating its potential drivers in dairy farms worldwide. However, although AMU in dairy farms is high, studies estimating AMU specifically in tiestall farms are scarce. Tiestalls are a common housing system and their prevalence among dairy farms accounts to approximatively 73%, 41%, and 40% in Canada, the United States, and Switzerland, respectively. The objectives of this cross-sectional, retrospective observational study were to estimate AMU using the newly established Swiss national reporting system for AMU in livestock and to identify associated factors on Swiss tiestall dairy farms. We calculated the treatment incidence (TI) by using the European Medicines Agency methodology and their defined daily dose (DDD) and defined course dose (DCD) standards. Data on factors potentially associated with AMU were obtained through personal interviews with farm managers on 221 farms. Retrospectively, during a 1-year period, data on a total of 7,619 treatments were extracted from the national database. Associations between management factors and TI were analyzed using a generalized linear model with gamma distribution. The mean (± SD) overall TI was 5.46 DDD/cow-year (± 4.10 DDD/cow-year). Intramammary treatment during lactation accounted for highest TI (3.24 ± 3.16 DDD/cow-year), whereas dry-cow therapy accounted for lowest TI (0.44 ± 0.49 DCD/cow-year). We found that 5 of the investigated management factors were significantly associated with TI. Organic production (estimate = -2.16; 95% CI = -3.62 to -0.70) and herd size (estimate = -0.81; 95% CI = -1.23 to -0.39) were negatively associated with TI. Specific cow breeds (Brown Swiss estimate = 1.56; 95% CI = 0.45-2.68) and Holstein Friesian (estimate = 1.42; 95% CI = 0.03-2.82; reference: other breeds) and the use of hygienic powders on the lying area (estimate = 1.10; 95% CI = 0.04-2.17) were positively associated with TI. Overall, the Swiss national reporting system is a valuable tool for AMU estimation. Several herd characteristics and management factors were associated with AMU in tiestall farms. Further studies focusing on factors associated with AMU and which are amenable to intervention will help improve stewardship programs and subsequently reduce AMU in dairy cows.

An Integrated Gesture Framework of Smart Entry Based on Arduino and Random Forest Classifier

Gesture-based systems have emerged as a prominent breakthrough in the field of smart access control, effectively integrating security measures with user comfort. This study presents a novel gesture detection framework for smart entry systems that combines the computational capabilities of a Random Forest Classifier with the practicality of Arduino-based hardware. Central to methodology is the utilization of MediaPipe, an advanced computer vision library, to extract hand motion landmarks from live video streams. The selected landmarks function as a comprehensive dataset for training a Random Forest Classifier, which has been specifically chosen due to its high level of accuracy and efficiency in managing intricate classification jobs. The model exhibits outstanding competence in the categorization of gestures in real-time, attaining high levels of accuracy that are crucial for ensuring dependable entrance control. The Arduino microcontroller plays a vital role in the execution of the entry mechanism as it serves as the intermediary between the gesture detection software and the tangible entry control hardware. The incorporation of gesture recognition technology facilitates a cohesive and prompt user experience, wherein identified motions are directly converted into input commands. The system's practical use is demonstrated through a series of detailed tests, which highlight its dependability and efficiency across diverse climatic circumstances. The findings underscore the system's capacity as a flexible and safe solution for contactless access in many environments, including both private homes and highly protected establishments. Furthermore, the study makes a substantial contribution to the larger domain of human-computer interaction by showcasing the practicality of advanced gesture detection systems in many everyday contexts. The suggested framework presents a novel approach to smart entry systems and also paves the way for further investigation in the domains of smart home automation and interactive systems. In these areas, gesture-based interfaces have the potential to deliver user experiences that are both intuitive and efficient.

Common direct current (DC) bus integration of DC fast chargers, grid‐scale energy storage, and solar photovoltaic: New York City case study

AbstractThe mass deployment of distributed energy resources (DERs) to achieve clean energy objectives has become a major goal across several states in the U.S. However, the viability and reality of achieving these goals in dense urban areas, such as New York City, are challenged by several ‘Techno‐Economic’ barriers associated with available land space and the number of AC/direct current (DC) conversion stages that requires multiple electrical balance of plant (BOP) equipment for pairing/interconnecting these resources to the grid. The fundamental issue of interconnection is addressed by assessing the use of a common DC bus in a one‐of‐a‐kind configuration (to pair grid‐connected energy storage, photovoltaic, and electric vehicle chargers (EVC) systems) and reduce the number of BOP equipment needed for deployment. Building on similar work that has touched on distribution‐level DC interconnection, this paper will also address the intricacies of interconnecting third‐party and Utility DERs to a DC‐based point of common coupling. It will examine the requisite site controller configuration (control architecture) and requirements to coordinate the energy storage system's use between managing Utility and Third‐Party EVC demand while prioritising dispatch. The result shows that the DC‐coupled system is technologically feasible and hierarchical control architecture is recommended to maintain stability during various use cases proposed. This will inform a lab demonstration of this system that aims to test DC integration of the DERs with recommendations for the microgrid (MG) controllers and reduction in the BOP equipment. These learnings will then be applied to practical grid‐scale deployment of the systems at Con Edison's Cedar Street Substation. This system, if proven successful, has the potential to change the way community distributed generation and MGs are interconnected to the Utility System.

Using interactive computer play in physical therapy and occupational therapy clinical practice: an explanatory sequential mixed methods study.

This study explored the extent to which an interactive computer play system, Bootle Blast, supports motor learning in a clinical context and examined clinicians' perceptions of their therapeutic role in the system's use as an intervention tool. In this observational sequential explanatory mixed methods study, five children with cerebral palsy [mean age 9.4 years (SD, 0.5), Gross Motor Function Classification System Levels I-III] used Bootle Blast during a single video-recorded therapy session with their treating clinicians (physical therapists, occupational therapists, and therapy assistants). Children played one Bootle Blast mini game independently (without clinician involvement) before clinicians carried out therapy sessions with the game as per usual care. The type and extent of motor learning strategies (MLS) delivered by Bootle Blast and clinicians were rated from video recordings by a trained assessor using the 22-item Motor Learning Strategies Rating Instrument. Semi-structured interviews with clinicians were conducted to gain insights into MLS use and clinicians' perceived role during Bootle Blast use. Interviews were audio recorded, transcribed verbatim, and analyzed independently by two researchers using thematic analysis. Quantitative and qualitative data were merged and reported using narrative and joint display approaches. Bootle Blast provided eight MLS, with clinicians adding or enhancing another eight. Four themes reflected clinicians' perspectives: (1) Bootle Blast disguises therapy as play, (2) clinicians give Bootle Blast the human touch; (3) home use of Bootle Blast is promising; and (4) Bootle Blast is not always the right fit but some shortcomings could be addressed. Agreement was found for nine MLS and disagreement for four MLS when quantitative and qualitative findings were merged. Bootle Blast delivers several MLS as part of game play and clinicians can enhance and provide additional MLS to suit the child's needs/abilities. Further game refinements that were identified in this study may optimize its clinical use.

Design and Implementation of a Web-Based Application for Media and Laboratory Reagents Management with QR Code Feature

Planning needs, purchasing, determining the availability of practical materials, and keeping an eye on the state of the actual or real amount are the first steps in practical material management operations. This task is completed to ensure the accuracy of the bookkeeping records in order to reduce variations in inventory levels. The Department of Medical Laboratory Technology still uses a manual approach to record the inventory of practice materials. Handwriting is still used in the stock book for traditional procedures including the process of counting arriving and exiting products and calculating physical inventory items. The goal of the study is to manage media practice materials and QR Code-based laboratory reagents as efficiently as possible. This procedure consists of a set of stock-taking tasks that are frequently seen in laboratories. Inventories for laboratory equipment are managed using an inventory information system called https://silatlam.online. The system's use demonstrates the efficiency and precision with which data on laboratory equipment is recorded in order for the leadership to make decisions regarding the proper quantity of teaching and learning aids to be purchased. Users can more easily conduct stock-taking activities for media and reagent practicum materials thanks to research findings that were used to develop a website-based information system for managing those materials at https://bhp-tlm.poltekkesjakarta3.sch.id. The unique feature of this system is the direct smartphone connection of the notification, which makes it simpler for users to keep track of inventory flow.

Performance management systems in a shared service centre: an exploration of organisational injustice

PurposeThis study aims to explain how negative workplace interactions are formed by the application of a performance management system (PMS).Design/methodology/approachThe study draws from unique in-depth interviews with service workers who resigned from an accounting shared service centre (SSC), discussing the reasons behind the resignations. Following an abductive approach, organisational justice theory is used to analyse the service workers' perceptions of negative interactions and to link the negative interactions to the use of the PMS.FindingsThe findings suggest that negative workplace interactions are characterised by cost consciousness, inequality and competitiveness. These interactions are attributed to the use of a PMS in the centre and are related to perceptions of distributive, procedural and interactional injustice.Practical implicationsManagers and leaders of shared service–type organisations should not rely on PMSs as an all-encompassing solution; instead, they should acknowledge the fairness of the use of PMSs. Moreover, HR professionals should choose and train employees to apply PMSs fairly. Fairness is important in work allocation, resourcing, monitoring, giving feedback, recognising good performance, promotion and interaction between peers.Originality/valueThis study contributes to the literature by taking an overall perspective on PMSs to analyse and explain the unintended negative consequences of a PMS in a highly scripted and monitored work environment that is usually considered appropriate for such a system's use. Through the analysis, the study highlights pitfalls in the use of a PMS and the importance of interactional injustice not only between but also within organisational levels.

Efficient solid waste inspection through drone‐based aerial imagery and TinyML vision model

AbstractSolid waste management is a significant challenge in the development of smart cities. Existing approaches for solid waste monitoring are often time‐consuming and resource intensive. Therefore, this study proposes a novel approach to solid waste monitoring that utilizes drone technology. The proposed method enables the efficient identification and classification of waste objects in the garbage discovered by the drone. This system can inspect every part of a smart city from a remote location, allowing for the timely and effective management of solid waste. Thus, the proposed system can be easily integrated in the existing waste management system for smart city. The drone‐based solid waste monitoring system comprises a drone equipped with a computer vision model for resource‐constrained devices and a software application that operates the drone and analyzes the captured image or video. The system utilizes the Internet of Things (IoT) to upload the collected data to the cloud, making it easily accessible whenever necessary. The proposed drone‐based solid waste monitoring system is a promising solution for the efficient and cost‐effective management of solid waste in smart cities. The system's innovative use of drone technology and IoT provides a scalable and adaptable solution that can be customized to meet the needs of any city.

The Impact of ERP Systems on Organizational Performance

Research on the link between investments in ERP systems and organizational performance has often led to mixed results. Besides internal organizational factors, many external contextual factors come into play. This study examined the role of firm size, industry, and duration of ERP system's use in influencing the performance impact of ERP systems through moderating the relationships between antecedent variables, ERP-induced benefits, and improvement in overall organizational performance. Using a sample of 200 participant firms, and structural equation modeling (SEM) analysis, the author confirmed the significant role of business process re-engineering and organizational fit and alignment as antecedents to ERP-induced benefits in information quality, and coordination/integration. Data and information quality was in turn confirmed as a significant predictor of organizational performance. Furthermore, the roles of industry, firm size, and time elapsed were also confirmed as significant moderators to the influence of the antecedent variables on ERP benefits and organizational performance.

EFFECTIVE FRAUD DETECTION IN BLOCKCHAIN USING XGBOOST WITH RANDOM FOREST

To solve the issue of fraud and abnormalities in the Bitcoin network, we offer a model. Here, we classify transactions based on integrated and fraudulent transaction patterns using machine learning methods like XGboost and Random Forest (RF). Then, future incoming transactions are predicted using the trained dataset. To identify fraudulent transactions, the authors use blockchain technology with machine learning algorithms. The proposed model performs a security analysis of the proposed smart contract to show the system's robustness and determines the precision and AUC of the models to gauge accuracy. To guard against assaults and vulnerabilities on the suggested system, the authors additionally suggest an attacker model. Overall, as financial technology advances, the proposed approach seeks to offer a more secure method for spotting fraud in the Bitcoin network. The authors assert that their suggested system's use of machine learning and blockchain technology may successfully identify and stop fraudulent transactions.