Search Results (6,271)

Given the scarcity of initiatives to charge for urban stormwater services in Brazil and the need to recognise users’ efforts in adopting technologies such as Low Impact Development (LID), a proposal was developed for a stormwater drainage fee and incentives for environmental services in a Brazilian municipality, based on flows retained by LIDs, specifically infiltration wells. To this end, simulations were carried out using the Storm Water Management Model (SWMM) for a 0.5 km² area in a Brazilian city that does not yet implement such charges. Based on the identification of the total flow retained by users within the watershed, the avoided cost to the drainage system was estimated. The charging model was defined using the avoided cost method associated with the Simplified Equivalent Residential Unit (SERU). In the baseline scenario that allocates the operation and maintenance cost, the estimated annual fee was USD 17.22 per household without LID and USD 14.64 per household with LID, and the SERU area was 371.11 m², used as a property-area reference; in an incentive scenario designed to reduce the user payback period to approximately 10 years, the fee for households without LID was set at USD 73.78. An economic incentive policy was identified, consisting of fee discounts upon adoption of LIDs, as well as support for their installation and maintenance. Thus, it was validated that combining a drainage fee with economic incentives is both feasible and motivating for both system users and managers. Full article

Although small and medium-sized enterprises (SMEs) play a key role in decarbonization, structural and organizational constraints often hinder the adoption of structured energy management practices, while existing maturity models are generally static, resource-intensive, or insufficiently tailored to SME characteristics. This paper aims to address this gap by proposing a dynamic energy management maturity model specifically designed for SMEs, integrating an adaptive assessment mechanism with an action-oriented improvement pathway. The model is structured around six maturity dimensions and four progressive maturity levels, and it employs a dynamic questionnaire that adapts to the organizational characteristics and maturity level of the assessed enterprise. The assessment outcomes are translated into a tailored action plan aimed at supporting incremental and feasible improvements. The model was applied in an exploratory pilot involving five SMEs to assess its applicability and practical usefulness. The resulting global maturity indices ranged from 0.27 to 1.97 on the 0-4 scale, distinguishing organizations at different stages of energy management development, while the dimension-level indicators highlighted different maturity profiles and improvement priorities across the participating organizations. For two organizations, model outputs were compared with prior independent qualitative assessments and showed qualitative agreement. Given the small and purposively selected sample, these findings are interpreted as preliminary evidence of feasibility and applicability rather than as empirical validation of the model. Following its development, the model was implemented in an operational web-based environment to support future larger-scale applications and validation. Full article

AI is becoming a key enabler of high-efficiency and sustainable energy conversion in power converters, electrical machines, electric drives, renewable-energy interfaces, and advanced energy-management systems. This critical review examines how machine learning, deep learning, reinforcement learning, physics-informed models, digital twins, and optimization algorithms enhance the design, control, monitoring, and operation of modern electromechanical energy-conversion systems. The paper outlines the main AI methods relevant to power electronics and electrical machines, distinguishing between data-driven, model-assisted, and hybrid approaches. It summarizes AI applications in power converter design, modulation, fault diagnosis, thermal management, wide-bandgap semiconductor operation, grid-connected renewable energy converters, and electric vehicle thermal and energy management, range characterization, and charging. A dedicated section covers electrical machines and drives, including AI-assisted electromagnetic and thermal design, condition monitoring, sensorless control, efficiency-map optimization, and predictive maintenance. To distinguish the scale of the claimed engineering outcome from the maturity of the supporting evidence, the review introduces an E1–E4 engineering outcome scale together with the AI Energy-Conversion Evidence Maturity (AI-ECEM) M1–M5 scale, a minimum reporting bundle, a net-benefit accounting framework, and a deployment roadmap. The synthesis indicates that surrogate-assisted design and diagnostic classification are comparatively mature, whereas autonomous real-time control and lifecycle deployment require stronger hardware, robustness, cybersecurity, and field evidence. Full article

AI-driven personalization has become a defining feature of digital advertising, yet whether it drives purchase intention directly or depends on other underlying mechanisms remains insufficiently understood, particularly in under-studied emerging markets. This study investigates how perceived relevance, usefulness, and privacy concerns shape perceived trust and purchase intention in response to AI-driven personalized advertising across Lebanon and Türkiye, two digitally distinct markets. Drawing on the Stimulus Organism Response model, Technology Acceptance Model, Privacy Calculus Theory, and Trust in Automation Theory, a quantitative cross-national design was employed, using a structured questionnaire administered to 802 social media users (394 Lebanon; 408 Türkiye), with data analyzed through partial least squares structural equation modeling and permutation-based multigroup analysis following partial measurement invariance. Results confirm perceived personalization has no direct effect on purchase intention, operating solely through relevance, usefulness, and privacy concerns; relevance and usefulness build trust while privacy concerns erode it, and trust, relevance, and usefulness each independently drive purchase intention. This study advances an asymmetric mediation account of AI advertising, positioning trust as the conduit through which privacy risk reaches behavior and, for managers, the lever converting personalization into purchase. Future research could examine these dynamics longitudinally or across other digitally emerging markets. Full article

This work presents a numerical investigation of a direct oil cooling system for Axial Flux Permanent Magnet (AFPM) machines. Building upon the authors’ previous study, which established the fundamental fluid dynamic mechanisms governing oil jet impingement on curved coil surfaces, the present research extends the analysis by performing a systematic parametric optimization of nozzle diameter and axial position. A validated CFD model, benchmarked against experimental data from the literature, is employed to quantify the influence of jet momentum, stagnation pressure, and flow attachment on the resulting thermal performance. Nine configurations are simulated at constant coolant mass flow rate, revealing that the nozzle diameter is the dominant parameter: smaller diameters generate higher jet velocities, stronger stagnation regions, and larger jet-induced forces, leading to significantly enhanced heat transfer coefficients and Nusselt numbers. Nozzle height plays a secondary yet relevant role, as higher positions promote a more coherent jet core and improve impingement quality. Among the nine simulated cases, the configuration with D = 3 mm and L = 14 mm achieves the lowest hotspot temperature and the most efficient energetic behavior within the simulated set, with only a modest increase in pumping power. The results confirm that direct oil impingement is highly sensitive to jet momentum and angle of attack and demonstrate that optimized nozzle design can substantially improve the thermal management of high power density AFPM machines. This extended analysis provides quantitative references for nozzle sizing and placement within the simulated operating conditions with enhanced cooling efficiency. Full article

Background/Objectives: Collared triple-tapered cementless femoral stems are designed to optimize metaphyseal fit, stability, and osseointegration following total hip arthroplasty (THA). This study evaluated 90-day revision and complication rates, short-term functional recovery, and operative characteristics of a modern collared triple-tapered stem in a same-day discharge ambulatory surgery center cohort. Methods: This retrospective cohort study reviewed 525 consecutive primary direct anterior THAs performed by 15 fellowship-trained arthroplasty surgeons at one group operating across several ASCs between September 2024 and August 2025. Ninety-day all-cause revision, stem-specific revision, periprosthetic fracture, and other early postoperative complications were evaluated. Kaplan–Meier estimates were used to account for variable follow-up within the 90 days. PROMs (HOOS-JR, FJS, VR-12) were collected preoperatively and at 12 weeks. Operative time was evaluated using within-surgeon analyses restricted to surgeons who used both an automated impaction device (AID) and manual impaction. Results: At a mean effective follow-up of 110 ± 39 days, the 90-day Kaplan–Meier revision-free estimate was 99.62% for all-cause revision and 99.81% for stem revision. Two patients (0.4%) underwent revision (one Vancouver B2 fracture; one infection treated with DAIR with stem retention). The early periprosthetic fracture rate was 0.2%. Five patients (1.0%) experienced dislocation, all of whom were managed with closed reduction. HOOS-JR improved by 28.7 points and FJS by 41.9 points at 12 weeks (both p < 0.0001), with 84.2% and 85.8% MCID achievement, respectively. Seven surgeons used both techniques, contributing 384 AID and 58 manual cases to the within-surgeon analysis. AID use was not associated with operative time after accounting for surgeon (adjusted difference, 0.18 min; 95% CI, −2.04 to 2.39; p = 0.8764). Conclusions: This collared, triple-tapered cementless stem was associated with low 90-day revision and complication rates and clinically meaningful short-term functional improvement in a same-day discharge ambulatory population. AID use was not associated with operative time after accounting for surgeon, although the study was not powered to compare uncommon complications between impaction methods. Full article

The increasing complexity of residential energy systems and the growing penetration of distributed resources require practical energy-management solutions that extend beyond conventional metering. This paper presents the design and implementation of a real-time Internet of Things (IoT)-based energy-management system for monitoring and controlling household energy consumption under different operating conditions. The proposed system adopts a dual-processor architecture, in which a primary microcontroller performs time-critical electrical measurements and low-level load switching, while a secondary processor operates as a local IoT gateway for data handling, rule-based control decisions, local visualization, and message queuing telemetry transport (MQTT)-based cloud communication through a 4G link. The contribution of this work is not associated with the individual use of dual processing, cellular communication, cloud monitoring, load shedding, or backup power, as these technologies have been previously reported in smart-metering and home energy-management systems. Instead, the study focuses on their coordinated integration within a residential-scale prototype that combines calibrated per-load monitoring, priority-based load control, outage-resilient reporting, and credit-aware load restriction. The system measures voltage, current, active and apparent power, power factor, and energy consumption for individual loads and supports centralized visualization through a cloud-based dashboard. The prototype was experimentally evaluated under three representative scenarios: overload, main power outage, and low-credit operation. In the overload scenario, automatic priority-based load shedding reduced the total load by up to 75%. During power outages, a battery-supported subsystem maintained monitoring and communication for real-time outage reporting. In the low-credit scenario, non-essential loads were disconnected when the user balance fell below a predefined threshold, while essential loads remained energized. The results demonstrate that the implemented prototype can provide integrated monitoring, local rule-based control, cloud reporting, and backup-supported operation within a unified residential energy-management platform. Full article

The formation pressure system of ultra-deep wells is complex, and managed pressure cementing (MPC) is a commonly used technical means of safety control and cementing quality improvement. During the MPC process, pump switching operations can induce substantial annular back-pressure. The attenuation of annular back-pressure within the wellbore serves as a pivotal foundation for the precise determination of back-pressure compensation values in ultra-deep wells. Building upon the one-dimensional transient flow model of the wellbore, we developed a transient transmission model for annular back-pressure and solved it using the finite difference method. The computational results were validated against experimental data, thereby elucidating the attenuation pattern of annular pressure waves in ultra-deep wells. The findings reveal that the primary controlling factors for the attenuation of pressure waves encompass well depth, the elastic modulus of the wellbore rock, and the rheological model of the drilling fluid. As well depth increases, the pressure wave exhibits a linear decrease, with discontinuities occurring at the casing and open-hole sections. The rate of pressure wave attenuation accelerates within the open-hole interval. The lower the elastic modulus of the open-hole segment, the more rapid the attenuation rate of the pressure wave becomes. The attenuation laws of annular fluids with different rheological models are ranked as follows: Power-law model > Herschel–Bulkley model > Bingham model. Under the computed well conditions, the pressure of the power-law fluid decreases to 85% of its initial back-pressure value. This research provides theoretical underpinnings for the design and execution of on-site MPC operations. Full article

Risk registers remain static governance artefacts, manually maintained and weakly coupled to operational evidence. While organisations generate continuous security telemetry from vulnerability scanners, incident reports, and audit findings, this evidence is rarely synthesised into coherent, evolving risk structures. Existing approaches address fragments of the problem: SIEM systems correlate events but do not construct risk registers; GRC platforms manage risk documentation but depend on manual entry; and LLM applications assist with summarisation but introduce non-determinism incompatible with governance requirements. This paper presents a hybrid architecture that separates stochastic LLM-based extraction from deterministic risk correlation and aggregation. The system ingests heterogeneous evidence, extracts structured claims via schema-bounded LLM processing, and correlates events into stable risk trees using anchor-based tiered matching. All correlation and projection operations are deterministic and replayable. The contribution is an architectural design pattern for integrating probabilistic inference into governance systems without compromising auditability. The walkthroughs run on a reference prototype. Replaying the stored evidence three times rebuilt the same register state, and admission scores matched the values the rules predict. An injected malformed extraction was quarantined; the register did not change. Full article

This paper presents the design, control, and validation of a solar photovoltaic (PV)-powered DC microgrid (MG) integrated with a battery energy storage system (BESS), which was studied at laboratory scale as a step towards remote electrification in resource-constrained regions. An Adaptive Neuro-Fuzzy Inference System (ANFIS)-based maximum power point tracking (MPPT) algorithm is implemented to maximise solar energy extraction under varying irradiance. An Adaptive Power Management (APM) framework is proposed to maintain DC bus stability when the BESS is unavailable to support the bus—a condition that may arise from battery degradation, sensor or communication failures, converter malfunctions, protection trips, or physical damage. In this work, BESS unavailability is represented at the system level as the withdrawal of BESS support; the individual fault mechanisms that may cause it are not separately modelled. The APM operates across three hierarchical layers—monitoring, decision, and control—and reuses only the voltage and current measurements already present in the MG, requiring no additional sensing. The system is evaluated under three operating scenarios: (i) intermittent renewable generation; (ii) varying load demand; (iii) stochastic fluctuations in both irradiance and load. During BESS unavailability, the APM activates prioritised adaptive load shedding or PV generation curtailment as appropriate, preserving critical loads and preventing DC bus overvoltage. In the scenarios studied, the APM reduces worst-case voltage sag from 35.9% to 2.4% and worst-case swell from 53.51% to 0.14%, while maintaining BESS State of Charge (SOC) within 20%–80% during normal operation. Compared with the conventional Perturb and Observe (P&O) and Incremental Conductance (INC) methods, the ANFIS-based MPPT achieves a mean point-wise tracking and conversion efficiency of 99.46%, a 1.78% improvement and a 0.86% improvement, respectively, which were corroborated by independent energy-based assessments (1.76% and 0.92%), with voltage deviations of 2.34% and oscillations of only 0.57 V peak-to-peak. Lyapunov-based analysis establishes asymptotic stability of the DC bus voltage in the BESS-regulated operating modes under stated assumptions. The proposed control strategies are validated through MATLAB/Simulink (R2025b) simulations and laboratory-scale experimental results, with the latter demonstrating coordinated PV–BESS–converter operation and bus voltage regulation. Full article

The integration of Artificial Intelligence (AI) into human resources management is driving a profound transformation in the evolution of management, and even more so in the management of human talent, which is the primary resource of any organization. This research provides an in-depth analysis of the impact of AI on core human resource management processes, covering the automation of operations that enables the exploration of dimensions such as talent acquisition, training, potential development, mental well-being, strategic workforce planning, job design, diversity, compensation, equity and inclusion, change management, culture and sustainability. The purpose of this study is to systematically synthesize the existing evidence on the impact of artificial intelligence on human management processes, identifying the scientific consensus, emerging contradictions, research gaps, and implications for sustainable organizational development. A systematic review was conducted of various sources published between 2020 and 2025 from databases such as ScienceDirect and Scopus, among others, using predefined Boolean search strategies, explicit inclusion and exclusion criteria and a structured thematic synthesis narrowing down the main studies based on search criteria. It was determined how algorithms are changing the employer-employee relationship within organizations. The findings indicate that the effectiveness of AI depends on the development of a hybrid intelligence that preserves the human factor consideration. It is concluded that AI enables the optimization of cultural change management, analytical precision, and ethical oversight—which are irreplaceable and critical human competencies in today’s digital age. This review contributes to the literature by providing a comprehensive synthesis of recent evidence, identifying unresolved research gaps, and proposing a future research agenda that will lead to the development of sustainable, responsible, and people-centered AI in human resource management. Full article

Construction supply chains (CSCs) are increasingly exposed to material shortages, demand fluctuations, logistics disruptions, and inter-organizational coordination failures. Artificial intelligence (AI) offers new opportunities to improve construction supply chain resilience (CSCR) by strengthening prediction, information processing, and collaborative decision-making. However, the mechanisms through which AI capabilities enhance CSCR remain insufficiently understood. Drawing on organizational information processing theory (OIPT) and dynamic capabilities theory (DCT), this study examines whether AI capabilities affect proactive and reactive CSCR directly or indirectly through three dimensions of supply chain integration (SCI): operational, information, and relational integration. It further compares the relative strengths of these pathways. This research adopts an explanatory sequential mixed-methods design. In the quantitative phase, 353 valid questionnaires from construction professionals in China were analyzed using partial least squares structural equation modeling (PLS-SEM). In the qualitative phase, semi-structured interviews with 15 experts, alongside three real-world cases, were utilized to interpret the quantitative findings and identify contextual boundary conditions. The results demonstrate that AI capabilities have significant positive effects on both proactive CSCR (β = 0.140, p < 0.01) and reactive CSCR (β = 0.116, p < 0.05). Furthermore, AI capabilities significantly promote operational integration (β = 0.299, p < 0.001), information integration (β = 0.361, p < 0.001), and relational integration (β = 0.227, p < 0.001), which in turn enhance both resilience dimensions. Notably, information integration is an important aspect of proactive resilience (β = 0.290, p < 0.001), while operational integration is crucial for reactive resilience (β = 0.274, p < 0.001). The qualitative findings further indicate that environmental uncertainty, technical readiness, and top management support condition the effectiveness of AI-enabled SCI. Theoretically, grounded in OIPT and DCT, this study clarifies the pathways through which AI affects CSCR and the contextual conditions shaping these effects, thereby advancing the analytical framework for AI-driven resilience. Practically, it delivers tiered implementation guidance for construction stakeholders to deploy AI tools for layered integration, thereby specifically enhancing both pre-disruption proactive risk prevention and post-shock reactive recovery capacities. Full article

With the rapid development of artificial intelligence, AI-driven algorithmic goal setting has become an important mechanism of digital platform management. In the livestream e-commerce industry, platforms increasingly use algorithmic systems to assign tasks, monitor performance, and regulate streamers’ work. Although this intensive and dynamic form of algorithmic management can improve operational efficiency, it may also be associated with potential ethical risks. Drawing on Conservation of Resources Theory, Emotional Labor Theory, and Sociotechnical Systems Theory, this study examines whether AI-driven algorithmic goal setting is associated with streamers’ unethical selling behavior through emotional dissonance and whether AI transparency moderates this relationship. Using a three-wave time-lagged survey design, data were collected from 427 livestream e-commerce streamers in China. SPSS-based hierarchical regression analysis and bootstrapping were employed to test the proposed moderated mediation model. The results showed that AI-driven algorithmic goal setting was significantly and positively associated with streamers’ unethical selling behavior and that emotional dissonance partially mediated this relationship. Furthermore, the positive relationship between AI-driven algorithmic goal setting and emotional dissonance, as well as the corresponding indirect relationship with unethical selling behavior, was weaker at higher levels of AI transparency. These findings identify emotional dissonance as an important psychological mechanism linking algorithmic performance demands with unethical selling behavior and indicate the conditional buffering role of AI transparency. This study extends algorithmic management research to the livestream e-commerce context and provides practical implications for enhancing algorithmic transparency and reducing ethical risks in platform governance. Full article

Southern corn rust, caused by Puccinia polysora Underw, is a major foliar disease that threatens corn yield in China. Detection of P. polysora during the corn growing period is critical for timely intervention and disease management. In this study, specific primers and probes were designed based on the variable region of the rDNA-ITS sequence. After conventional PCR validation, specificity screening, and sensitivity evaluation, an optimal primer–probe combination was successfully established. The developed assay achieved a detection limit of 1 pg·μL⁻¹ genomic DNA and a DNA equivalent of 57.6 urediniospores of P. polysora. The reaction conditions were further optimized, and stable amplification could be completed under a constant temperature of 38 °C for 15 min. The developed MIRA-LFIA was evaluated using 70 field maize leaf samples, with the reported OTNPCR (One-Tube-Nested PCR) method used as the gold-standard reference. MIRA-LFIA produced 55 positive and 15 negative results, whereas OTNPCR yielded 57 positive and 13 negative samples. A diagnostic evaluation revealed that the MIRA-LFIA assay achieved a sensitivity of 96.49%, specificity of 100%, positive predictive value of 100%, negative predictive value of 86.67%, and Youden’s index of 0.965., with a Kappa value of 0.911. This MIRA-LFIA system enables rapid detection and visual interpretation of southern corn rust. It features simple operation and low technical requirements, making it suitable for rapid on-site monitoring at grassroots institutions and in fields. Full article

Carbon efficiency, measuring effective output per unit of carbon emissions, is vital for managing low-carbon workshops and advancing sustainable manufacturing. However, production processes often face concurrent discrete events (e.g., equipment failures, parameter adjustments) and numerous emission factors with complex relationships, making it hard to identify dominant factors and event impact degrees, thus lacking direction for operation and maintenance decisions. This paper proposes a deductive monitoring model to analyze carbon efficiency changes under event concurrency. First, for traceability, a multi-resolution enhanced carbon efficiency information transfer network is proposed. It classifies emission factors into time-driven and event-driven accounting, and under the Parallel Discrete Event System Specification framework, adopts a multi-resolution approach with high- and low-resolution models for hierarchical aggregation from equipment to workshop, establishing a traceability path to specific factors. Second, for unclear impact degrees, a dynamic monitoring model for concurrent events is designed. A state-driven dynamic carbon efficiency accounting method automatically settles upon equipment state switching, and a rule-driven priority deduction strategy enables independent accounting of each event’s impact degrees in a determined order. A case study on a machine tool spindle production workshop validates the proposed model. Under baseline conditions, the relative accounting errors for 8 h cumulative carbon emissions and effective output are approximately 1.05% and 0.89%, respectively. In concurrent event scenarios, the model achieves deterministic trajectory reproducibility across 30 independent deduction runs and enables independent impact-degree decomposition, whereas traditional discrete event simulation exhibits trajectory ambiguity. Furthermore, testing under 42 multi-parameter perturbation combinations demonstrates traceability path integrity and accurate root-cause localization, delivering a transparent and reliable quantitative basis for low-carbon maintenance decisions. Full article