Search Results (3,089)

In grid-deficient environments, residential energy systems face severe carbon emission penalties due to mandatory reliance on diesel standby generators during supply interruptions. In Iraq, summer peak loads routinely exceed grid capacity, triggering prolonged generator operation and dramatically increasing household carbon footprints. This study presents a deep Q-network (DQN) reinforcement learning framework for intelligent battery energy storage system (BESS) scheduling, targeting carbon emissions reduction through strategic peak shaving. The DQN agent learns optimal battery dispatch strategies by internalizing diurnal patterns in load and solar generation through temporal state features, enabling anticipatory control without requiring explicit external forecasting models. The system is trained on one-year operational data from a representative Iraqi residential installation and evaluated over the critical summer period (122 days, 35.5% grid unavailability). The results demonstrate a 54.8% ${\mathrm{CO}}_2$ reduction (306.5 kg versus 677.4 kg baseline), a 25.5% reduction in generator runtime, and a 23.7% reduction in operating costs for the studied configuration. The learned policy approaches 89.6% of perfect-foresight MILP performance while executing 35,000 times faster. A reward function sensitivity analysis across five weighting schemes confirms that the 20:1 carbon-to-cost priority ratio optimally balances environmental and economic objectives. Ablation studies quantify the mechanism contributions: anticipatory pre-charging accounts for 58% of the total improvement, discharge optimization for 44%, and real-time PV coordination for 22%. These findings establish DQN-based BESS optimization as a practically deployable decarbonization approach for residential systems in grid-constrained developing regions. Full article

Achieving sustainability in prefabricated construction necessitates a balance between operational efficiency and stringent environmental constraints. However, cascading rework chains triggered by assembly defects frequently disrupt this equilibrium. Existing literature predominantly addresses this dynamic through reactive rescheduling, thereby largely overlooking the potential of proactive topological interception. To bridge this gap, this study proposes a proactive bi-level scheduling framework that mathematically integrates strategic quality inspection planning with operational low-carbon project execution. Specifically, a Generalized Total Cost (GTC) model is formulated to internalize multi-objective trade-offs—including time, cost, and carbon emissions—into a unified financial metric through market-based shadow prices. This framework is operationalized through a novel bi-level Hybrid Evolutionary Algorithm (H-TS-CDBO). By combining the global exploration capabilities of Chaotic Dung Beetle Optimization with the local refinement mechanisms of Tabu Search, the proposed solver is specifically engineered to navigate the topological ruggedness induced by proactive inspection interventions. Empirical benchmarking validates the computational robustness of the solver, while an illustrative case study substantiates a critical managerial paradigm shift from “passive remediation” to “active prevention”: compared to traditional methods, a marginal preventive investment of 5.4% functions as an effective containment mechanism, yielding a 40.8% net reduction in the GTC. Furthermore, a sensitivity analysis regarding varying static carbon tax rates simulates algorithmic adaptation under diverse regulatory intensity thresholds, delineating an actionable pathway for project managers to achieve lean, low-carbon synergy amidst evolving regulatory pressures. Full article

The rapid growth of distributed solar energy, such as rooftop photovoltaics (PVs), has revolutionized conventional power systems into more distributed networks, enabling end-users to engage in and trade within the energy market. Maximizing the benefits of rooftop PV panels for residential end-users, including increased renewable energy use and reduced reliance on the utility grid, remains an essential challenge in conventional centralized markets. Moreover, reducing energy consumption may lead to increased peak demand, decreased self-consumption, reduced system flexibility, and reduced grid stability. Therefore, this study presents a transactive energy market framework that integrates home energy management systems (HEMSs) with multi-objective optimization and an aggregator-based, distributed peer-to-peer (P2P) trading strategy to increase rooftop PV utilization and reduce grid dependency within an intra-residential community. The HEMS is structured to integrate rooftop PV production, battery energy storage systems, and smart appliances to offer flexibility through demand response programs in balancing supply and demand by scheduling appliances during periods of rooftop PV production and lower grid prices. Multi-objective (i.e., minimizing energy consumption cost and peak load) optimization problems are solved using the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) by achieving a Pareto-optimal solution. To validate the reliability and optimality of the NSGA-II results, the same problem formulation is solved using a mixed-integer linear programming approach. Moreover, a Strategic Double Auction with Dynamic Pricing (SDA-DP) strategy is proposed to support P2P trading among consumers and prosumers and thereafter compared with a rule-based zero-intelligence strategy with market-matching rules to analyze the trading performance of the proposed SDA-DP. The results of this comparative analysis (for 10 households, year-long simulation with 15 min time resolution) demonstrate that compared to the baseline case, integrating NSGA-II optimization with SDA-DP trading significantly enhances rooftop PV utilization by 35.11%, reduces grid dependency by 34.04%, and reduces electricity consumption costs by 30.53%, with savings of €1.93 to €6.67 for a single day after participating in the proposed P2P market. Full article

Objectives: Cardiac point-of-care ultrasound (cPOCUS) enables rapid bedside cardiac assessment and may facilitate early identification of potential cardiac sources of embolism in patients with acute ischemic stroke (AIS). This study aimed to evaluate whether neurologist-performed cPOCUS is associated with reduced hospital length of stay (LOS) in patients admitted to a Stroke Unit (SU). Methods: We conducted a retrospective observational study including consecutive patients with AIS admitted between 2020 and 2021 who required cardiac ultrasound for etiological evaluation. Patients underwent cPOCUS and/or transthoracic echocardiography (TTE) and were classified into two groups: those evaluated with cPOCUS (with or without TTE) and those evaluated exclusively with TTE (control group). The availability of cPOCUS depended on predefined weekly schedules rather than individual clinical decision-making, partially mitigating selection bias. The primary outcome was LOS. Multivariable linear regression analysis was performed to adjust for potential confounders. Results: Among 808 patients with AIS, 332 underwent cardiac ultrasonography during hospitalization: 219 in the cPOCUS group and 113 in the control group. Overall, 60.4% were male, the mean age was 68.4 years (SD 13.3), and the median National Institutes of Health Stroke Scale score at admission was 5 (IQR 9), with no significant differences between groups. Median LOS was shorter in the cPOCUS group than in the control group [7 days (IQR 4) vs. 8 days (IQR 5); p = 0.015]. After adjustment for confounders, cPOCUS evaluation remained independently associated with shorter LOS (β −1.49, standard error 0.73, 95% CI −2.93 to −0.05; p = 0.04). Conclusions: Neurologist-performed cPOCUS is independently associated with reduced LOS in patients with AIS admitted to an SU. These findings suggest that cPOCUS may facilitate more efficient in-hospital workflows and support its potential integration into routine stroke care pathways. Full article

The large-scale integration of electric vehicles (EVs) introduces significant operational challenges for power systems, particularly when grid-favourable operating periods coincide with high marginal carbon emissions. This paper proposes a carbon-aware rolling-horizon energy management framework for EV fleets coordinated through virtual power plants (VPPs), explicitly addressing such carbon–grid conflict conditions. The proposed framework prioritises grid-friendly scheduling through power and ramp constraints while enforcing energy-service equivalence and a policy-level carbon budget consistent with carbon peak and carbon neutrality objectives. Carbon awareness is incorporated as a secondary steering term within the rolling-horizon optimisation, enabling temporal shifting of EV charging toward low-carbon periods without compromising grid stability. A Pareto-based trade-off analysis is conducted to characterise the relationship between grid stress mitigation and carbon reduction, and a knee point is identified to select a balanced operating regime. Simulation results using real EV charging demand combined with a conflict-driven carbon intensity signal demonstrate that grid-oriented scheduling alone can increase emissions under carbon–grid mismatch. In the evaluated conflict scenario, the proposed carbon-aware rolling-horizon strategy achieves a 17.35% reduction in total CO₂ emissions relative to RH-NoCarbon scheduling while maintaining peak–valley load variation below 11.03 kW compared with 43.65 kW under uncontrolled charging. These results confirm that explicit carbon-aware coordination can significantly mitigate emissions without compromising grid operational stability. All control strategies are evaluated in a simulation environment using real EV charging demand data as exogenous inputs, ensuring realistic demand representation while enabling controlled assessment of operational performance. These findings highlight the necessity of embedding carbon considerations directly into operational EV scheduling and establish VPP-based rolling-horizon coordination as a practical mechanism for low-carbon power system operation. Full article

Date palms, a vital Moroccan crop that typically flowers once a year, displayed a rare double flowering phenomenon in the summer of 2023. This study investigated the occurrence of this phenomenon across three small oases in the Zagora region of southeastern Morocco. Field surveys revealed that 60% of spring-blooming palms also produced a second bloom in July, affecting trees of all ages, sexes, and varieties. This secondary flowering cycle featured a compressed development period, leading to limited fruit enlargement and a failure of most fruit to reach maturity. Analysis suggests that climatic fluctuations, specifically a delayed temperature rise during the normal spring cycle and relatively cooler shifts in July 2023, likely disrupted the palms’ reproductive schedule, triggering the anomaly. Despite the failed second harvest, the phenomenon did not have a negative impact on the palms’ productivity for the subsequent year, confirming that double flowering is a complex, climate-influenced event that requires further research to understand its full implications for local date production. Full article

High-performance speed control of Permanent Magnet Synchronous Motor (PMSM) drives in Electric Vehicle (EV) applications faces significant challenges due to inherent nonlinearities, parameter variations, and signal non-idealities such as sensor noise and measurement latency. To address these issues, this paper proposes a robust PI-based Fractional-Order PSO-Fuzzy Weight Controller (PI-FOPSOFWC). The proposed strategy integrates a fractional-order PI (FOPI) core to ensure iso-damping robustness, a fuzzy inference mechanism for online gain scheduling against nonlinear load dynamics, and a novel Fractional-Order Particle Swarm Optimization (FOPSO) algorithm for optimal parameter tuning. A key contribution of this study is the validation of the control strategy within a high-fidelity co-simulation framework coupling MATLAB/Simulink with CarSim 2023, which incorporates realistic vehicle dynamics and time-varying road loads unavailable in conventional simplified simulations. Co-simulation results demonstrate that the proposed controller effectively eliminates overshoot in step responses and maintains stability under significant parameter mismatches ($2.0\times$ inertia). Furthermore, under the EPA urban driving cycle, the proposed method reduces the speed tracking Root Mean Square Error (RMSE) by 75.0% compared to the standard PI controller. Computational complexity analysis further confirms the feasibility of the proposed algorithm for real-time implementation in commercial EV traction drives. Full article

Robot-assisted therapy (RT) is increasingly implemented in rehabilitation, yet evidence on its effectiveness in improving upper extremity function after cervical spinal cord injury (cSCI) remains limited. Therefore, this randomised crossover study aims to investigate the effects of unilateral RT compared to conventional unilateral occupational therapy (OT) on upper extremity function in individuals with cSCI. 40 participants with traumatic or non-traumatic cSCI (16–81 days post-injury, neurological level of injury: C1–T1) will be randomised (1:1), stratified by their predicted recovery profile, to receive 6 weeks of RT (ArmeoSpring) and 6 weeks of OT in random order, each 3 × 30 min/week in addition to the clinical routine therapy. Assessments are conducted before (t₀), between (t₁) and after both intervention blocks (t₂ and t₃). The primary outcome is the Quantitative Grasping Subtest of the Graded Redefined Assessment of Strength, Sensibility, and Prehension (GRASSP-QtG); primary analysis uses a linear mixed model to estimate the treatment effect based on change scores. Recruitment is currently ongoing. This randomised crossover study allows the collection of a comprehensive dataset to generate knowledge about treatment effectiveness, enabling future individuals with cSCI to benefit from improved and individualised therapy schedules. Full article

Large organizations face a critical systems integration challenge when executing multiple concurrent security modernization programs. This paper examines the U.S. Department of Defense’s simultaneous implementation of three transformational initiatives—post-quantum cryptography (PQC) migration, Zero Trust Architecture (ZTA) deployment, and AI security assurance—each operating under separate governance structures, timelines, and compliance frameworks. Through systematic evidence synthesis of 59 sources (47 policy/standards documents and 12 performance benchmarks), we identify cross-program dependencies that create integration failures when programs operate in isolation. We propose a shared modernization substrate—a four-layer infrastructure architecture (Cryptographic Services, Identity Management, Analytics Pipeline, Policy Orchestration) that enables coordinated execution while preserving program independence. The framework addresses the fundamental systems challenge of achieving interoperability across programs with misaligned schedules and competing resource demands. We introduce a five-level Triad Convergence Maturity Model (TCMM) with operationalized indicators enabling repeatable organizational assessment. Illustrative application to three DoD modernization contexts demonstrates the framework’s ability to differentiate maturity levels. Performance analysis synthesizes published benchmark data: enterprise PQC latency overhead is modest (measured), while tactical environment estimates of 158–383% overhead are derived from benchmark extrapolation under packet-loss assumptions (modeled). Scenario modeling suggests that coordinated incident response through the substrate architecture could substantially reduce risk exposure windows compared to siloed approaches (modeled). The framework transforms fragmented program execution into synchronized systems modernization, offering practical guidance for chief information officers, program managers, and enterprise architects managing concurrent technology transitions. Full article

Background/Objectives: Pediatric neurosurgical procedures often involve significant blood loss and rapid hemodynamic shifts, necessitating accurate hemoglobin (Hb) monitoring. While continuous non-invasive Hb (SpHb) monitoring offers real-time trending, its accuracy in high-risk pediatric populations remains debated. We aimed to evaluate the diagnostic accuracy and clinical utility of SpHb compared to invasive arterial blood gas (ABG) analysis in pediatric patients undergoing cranial and spinal surgeries. Methods: This prospective, observational study enrolled 60 pediatric patients (aged 0–16 years) scheduled for high-risk neurosurgery. SpHb was measured continuously and compared with intermittent ABG-Hb values. Statistical analysis included Bland–Altman agreement, Pearson’s correlation, and Error Grid Analysis. Subgroup analyses assessed the impact of the Perfusion Index (PI), hypotension, and metabolic acidosis on device performance. Results: Data from 57 patients (median age: 12 months, interquartile range: 6–42 months; 70.2% aged <24 months) were analyzed. SpHb demonstrated a moderate correlation with ABG-Hb (r = 0.567, p < 0.001) but exhibited systematic overestimation with a mean bias of +1.60 ± 1.54 g/dL. Crucially, SpHb showed 0% sensitivity for detecting critical anemia (Hb < 8.0 g/dL). Device performance was significantly compromised by physiological extremes: severe metabolic acidosis significantly increased bias to +2.27 g/dL (p = 0.038), and intraoperative hypotension significantly widened the limits of agreement (SD of bias: 1.79 g/dL vs. 1.45 g/dL in normotension). Furthermore, hemodynamic analysis revealed a loss of autoregulation during hypotension, where the pressure-perfusion coupling strengthened (r = 0.44) compared to the normotensive state (r = 0.15). Conclusions: SpHb monitoring provides fair Hb trending but is limited by systematic overestimation and poor sensitivity for critical anemia. Accuracy worsens during severe acidosis and hemodynamic instability. Therefore, SpHb should function as a complementary “early warning” trend monitor rather than a sole transfusion trigger, with invasive validation remaining essential for intraoperative decision-making. Full article

In recent years, the food processing industry in the Gulf Cooperation Council (GCC) has faced increasing pressures to improve operational efficiency while improving its environmental performance. This research examines whether Lean Six Sigma (LSS) methodologies can be used as tools to incorporate sustainability into current operational processes at a date processing facility in Saudi Arabia. In addition to illustrating the ways in which production was improved, this research developed and preliminarily validated a Sustainability Integration Index (SII) framework to measure the contributions of improvement projects to sustainable practices in terms of their impact on the environment, society, and economy. Furthermore, this research examined the role of organizational culture as a moderator of the effectiveness of integrated LSS–sustainability approaches using a Cultural Readiness Assessment Model (CRAM). This research addressed production bottlenecks and aligned production with selected United Nation Sustainable Development Goals (SDGs) using the Define–Measure–Analyze–Improve–Control (DMAIC) methodology. Production bottlenecked in packaging operations resulted in schedule overruns and excessive overtime; therefore, the intervention focused on improving the production process in these areas. There were three distinct improvement streams: demand-based resource leveling, advanced production planning to allow for pull-based flow, and targeted maintenance to raise Overall Equipment Effectiveness (OEE) from 48.2% to 74.6%. Results indicated a 23% increase in daily processing capacity, a 38 min decrease in the average length of time of production closures, and estimated annual cost savings of 940,000 SAR (approximately USD 250,000). The SII framework showed a 21.2% improvement in sustainability scores, with a total composite score improvement from 0.66 to 0.80. Social sustainability had the greatest relative increase (+24.2%). Exploratory correlation analysis found that improvements in cultural maturity and cross-functional collaboration are possible predictors of successful sustainability integration; however, the limitations of the single case study limit the ability to draw causal inferences. The results provide both empirical evidence and possible measurement tools to an under-explored area: the use of LSS in Middle Eastern food processing industries with specific sustainability goals. Validation of the frameworks across different industries will be necessary to establish generalizability. Full article

Accurate and early detection of bone cancer is critical for improving patient outcomes, yet conventional radiographic interpretation remains limited by subjectivity and variability. Conventional AI models often struggle with complex multi-modal noise distributions, non-convex and topologically entangled latent manifolds, extreme class imbalance in rare oncological conditions, and heterogeneous data fusion constraints. To address these challenges, we present a Quantum-Inspired Classical Convolutional Neural Network (QC-CNN) inspired by quantum analogies for automated bone cancer detection in radiographic images. The proposed architecture integrates classical convolutional layers for hierarchical feature extraction with a classical variational layer motivated by high-dimensional Hilbert space analogies for enhanced pattern discrimination. A curated and annotated dataset of bone X-ray images was utilized, partitioned into training, validation, and independent test cohorts. The QC-CNN was optimized using stochastic gradient descent (SGD) with adaptive learning rate scheduling, and regularization strategies were applied to mitigate overfitting. Quantitative evaluation demonstrated superior diagnostic performance, achieving high accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). Results highlight the ability of classical CNN with quantum-inspired design to capture non-linear correlations and subtle radiographic biomarkers that classical CNNs may overlook. This study establishes QC-CNN as a promising framework for quantum-analogy motivated medical image analysis, providing evidence of its utility in oncology and underscoring its potential for translation into clinical decision-support systems for early bone cancer diagnosis. All computations in the present study are performed using classical algorithms, with quantum-inspired concepts serving as a conceptual framework for model design and motivating future extensions. Full article

In the building sector, time and cost overruns are still ongoing difficulties; hence, good project management depends critically on accurate evaluation of project performance. Usually, project success is measured in several performance criteria: cost, schedule, quality, safety, and others as well. This research suggests the construction of a thorough Project Performance Index (PPI) methodically combining these important performance criteria. One finds the relative weight of every element by means of a frequency-based analysis of their occurrence in the current literature. The final index presents a complete method for assessing and contrasting the performance of building projects, giving researchers and practitioners trying to improve project results a helpful instrument. Full article

Background: Patients with single-ventricle physiology represent a high-risk group for heart transplantation. Due to complex anatomical and physiological challenges, including multiple prior sternotomies, pulmonary artery abnormalities, and systemic consequences of altered circulation, they represent both a surgical and a clinical challenge. We aimed to analyze perioperative challenges, as well as early and long-term complications, in this specific group of patients. Methods: We performed a retrospective data analysis of a high-volume heart transplant center, focusing on patients with single-ventricle physiology who were scheduled for heart transplantation due to end-stage heart failure. We retrospectively analyzed the period from the beginning of the transplant program in November 1985 to the end of November 2024. Results: Among 1553 transplanted patients (adults and children), 29 were transplanted due to congenital heart disease (congenital valvular disease not included). In this group, nine patients were transplanted due to end-stage heart failure in the course of single-ventricle physiology. Age at transplantation ranged from 7 to 31 years (median, 17 years), and body weight ranged from 15 to 69 kg (median, 47.9 kg). All nine patients referred for heart transplantation presented with single-ventricle physiology. Their underlying congenital heart defects were heterogeneous and included hypoplastic left heart syndrome (HLHS), double-outlet left ventricle (DOLV), transposition of the great arteries (TGA) with associated ventricular septal defects (VSDs), atrial septal defects (ASDs), valvular abnormalities such as tricuspid and or pulmonary valve atresia or stenosis, systemic or atrioventricular valve regurgitation, and vascular abnormalities, including right-sided aortic arch, aortic coarctation, and pulmonary artery hypoplasia, stenosis, or occlusion, as well as associated pulmonary vascular abnormalities such as left pulmonary artery stenosis and MAPCAs. All patients had previously undergone staged palliative procedures, including Norwood, Hemi-Fontan, Fontan, bidirectional Glenn, modified Blalock–Taussig shunts, Bjork–Fontan, or pulmonary artery banding, often with repeated interventions such as balloon angioplasty, stent placement, or MAPCA closure. Extracardiac comorbidities were common and included coagulopathies, protein-losing enteropathy, hepatic dysfunction, and chronic venous insufficiency. Preoperative functional status was markedly impaired in all patients (NYHA III-IV, INTERMACS 3-4), with severely reduced exercise capacity and thrombotic events in several individuals. Perioperative transplant surgical strategies included femoral cannulation in four cases and standard aortic and caval cannulation in five cases. Pulmonary artery reconstruction was required in all patients. Extended donor pulmonary arteries were applied in eight cases, while a bifurcated Dacron prosthesis was utilized in one patient. Perioperative mortality was 33%, with three deaths attributed to bleeding and hemodynamic instability, while overall mortality was 44% including one late death unrelated to transplantation. Protein-losing enteropathy, although persistent in the immediate postoperative period, resolved in all surviving patients, underscoring the transformative impact of transplantation. Conclusions: These findings emphasize the importance of individualized surgical planning, extended donor pulmonary artery harvesting, and careful preoperative coordination. Heart transplantation remains a viable and life-extending option for selected single-ventricle patients, despite the significant technical and clinical challenges involved. Full article

A spatial awareness is a fundamental aspect of dance, reflecting deep philosophical ideas and aesthetic values across different cultures. While existing studies often focus on theatrical or biomechanical analyses, few explore how material cultural artifacts, such as pottery and porcelain figurines, reveal spatial differences in dance. This study addresses this gap by comparing Chinese pottery figurines from the Neolithic to Tang dynasties with Western Meissen porcelain dancers from the 18th century onward, applying a three-dimensional framework of “Movement Scheduling Space—kinetic space—expressive space.” Drawing on Confucian principles of “Harmony between Heaven and Humanity” and Christian notions of transcendence, the research examines how cultural traditions shape the spatial expression in dance. The findings show that Chinese dance emphasizes inward, upper-body movements extending from two-dimensional to one-dimensional space, reflecting a centripetal, earthly orientation. In contrast, Western dance expands from two-dimensional to three-dimensional space, emphasizing outward, lower-body movements symbolizing transcendental aspirations. Additionally, Chinese dance focuses on subtle hand gestures, while Western dance highlights expressive foot movements. By integrating artifact-based analysis with cultural and philosophical interpretation, this study offers a fresh approach to comparative dance philosophy, providing valuable insights for the reinterpretation of traditional aesthetics in modern choreography. Full article