ABSTRACT The conventional pyramid solar still (PSS) suffers from low thermal efficiency and limited freshwater yield due to inadequate solar energy utilization and heat losses. To address these challenges, this study introduces the Floating Absorber Pyramid Solar Still (FAPSS), a novel design that employs buoyant cork elements as floating absorbers to enhance solar energy capture. Each absorber consists of a black-coated sheet metal panel and a hydrophilic wick that ensures continuous water transport via capillary action. Three configurations with four, six, and eight absorbers (FAPSS‑4A, FAPSS‑6A, FAPSS‑8A) are evaluated, and the optimal FAPSS‑6A design is further enhanced by external mirrors to amplify incident radiation, an electric fan for vapor extraction, and a silver‑nanoparticle‑enhanced phase change material (Ag‑nano‑PCM) for thermal energy storage. The dual‑phase thermal management stabilizes the system under varying irradiance and extends operation into nighttime hours. Experimental results show that FAPSS‑6A with reflectors and fan achieves a daily freshwater yield of 10,600 mL/m2 ·day – an increase of 194% in yield over the conventional PSS – and a thermal efficiency of 62%. Incorporating Ag‑nano‑PCM further raises the yield to 11,300 mL/m2 ·day, representing a 182% improvement. Economic analysis reveals a substantial reduction in water production cost: 0.011 $/L for FAPSS with reflectors and fan, and 0.012 $/L for the configuration with PCM, compared to 0.024 $/L for the conventional PSS. The proposed FAPSS system thus offers a highly efficient, cost‑effective, and sustainable solution for decentralized solar desalination. The work demonstrates a strong commitment to advancing the United Nations SDGs (Sustainable Development Goals), particularly Clean Water and Sanitation (SDG 6).

To evaluate our "before vs. after" change in practice of stopping routine surveillance cultures in pediatric patients supported with extracorporeal membrane oxygenation (ECMO), by examining patient outcomes, reviewing antimicrobial prescription, and costs. Retrospective before vs. after study. PICU, neonatal ICU, and cardiac ICU in a quaternary children's hospital. Critically ill patients younger than 18 years supported on ECMO between October 2022 and March 2025. None. Patients supported on ECMO in the 12 months following the practice change in March 2024 were compared with the same number of ECMO patients from before the practice change (47 in each group). Removal of routine daily blood culture and every-other-day urine and respiratory culture orders in ECMO order sets was associated with a reduction in cultures obtained: from mean ( sd ) 1.8 (± 0.22) to 0.4 (± 0.19) per ECMO day ( p < 0.0001). We failed to identify an associated change in average ECMO run duration (211 vs. 181 hr; p = 0.48) or 30-day mortality (15/47 vs. 15/47). There was an associated decrease in antimicrobial prescriptions, quantified as a percentage of all ECMO days with prescription: (366/414 [88%] vs. 247/356 [69%]; mean difference, 19% [95% CI of the difference 13-25%]; p = 0.002). We estimate that using 2024 prices, there was a cost reduction of $136,000 in the 12 months following the change in practice. Our experience of introducing in March 2024 a change in using surveillance or scheduled cultures in pediatric ECMO patients in our center is that there was an associated reduction in microbiology cultures, improved antimicrobial stewardship, and cost-savings. In comparison with our experience before the change in practice, we failed to identify any associated negative effects such as increased duration of ECMO support or 30-day survival.

ABSTRACT The utilization of concrete structural materials demonstrates significant potential for cost reduction in floating offshore wind turbine platforms owing to their inherent cost-effectiveness. This study proposes a novel steel-concrete hybrid (SCH) semi-submersible platform with a three-column configuration for floating offshore wind turbines. A detailed numerical model integrating aero, hydro, servo, elastic, and mooring dynamics was created to thoroughly examine the platform's dynamic responses in intermediate water depths (65 m). A comparative analysis was performed to evaluate the hydrodynamic performance between the steel-concrete hybrid platform and a geometrically equivalent all-steel semi-submersible platform. Results reveal that the proposed SCH platform exhibits enhanced hydrodynamic characteristics. Specifically, the SCH platform demonstrates superior performance in surge, mooring line tension, and nacelle acceleration when compared to its steel counterpart.

The observation that activation sparsity emerges in MLP blocks of standardly trained Transformers offers an opportunity to drastically reduce computation costs without sacrificing performance. To theoretically explain this phenomenon, existing works have shown that activation sparsity does not result from the data properties or data fitting but from the implicit bias of the training process. However, these connections are obtained with strong assumptions (e.g., shallow networks, a small number of training steps, and special training techniques), which cannot be applied to deep models standardly trained with a large number of steps. Different from these works, we find that the flatness of loss landscapes is also closely related to the MLP activation sparsity and can serve as a weaker assumption because it naturally emerges in the standard training of deep networks without the above strong assumptions. Specifically, we find that 1) the MLP activation sparsity equals a ratio between "augmented f latness" (a weighted sum of flatness measures) and the product of the input norm and activation gradient of the MLP. We empirically find that this ratio decreases during training, leading to sparse activations. 2) We also propose the notion of derivative sparsity, which reduces to activation sparsity under ReLU, but further enables pruning in the backward propagation and is more stable than activation sparsity. With the theoretical findings, we can further encourage activation sparsity by decreasing the numerator and increasing the denominator of the ratio: 1) To improve (lower) the flatness, we add different bias vectors to input tokens of MLP blocks to strengthen stochastic gradient noises that drive the model to a flat area. 2) We restrict the lower bound of affine parameters in LayerNorm to increase the input norm of MLPs. 3) To increase the activation sparsity, we propose an activation function JSReLU to encourage the search of parameters with sparse derivatives and sparse activations. These plug-and-play modifications can effectively reduce the ratio and produce sparser activations. Experiments on ImageNet-1K and C4 demonstrate relative improvements of at least 36% on inference sparsity and at least 50% on training sparsity over vanilla Transformers, indicating further potential cost reduction in both inference and training.

Configuring psychological and infrastructural resources for poverty reduction: A hybrid PLS-SEM/fsQCA analysis of women's digital entrepreneurship.

Thematic coding helps researchers characterize intervention implementation in embedded pragmatic clinical trials (ePCTs), particularly interventions for older adults with dementia and care partners. However, manual coding is time-consuming, requiring multiple researchers. Because implementation science relies on systematic identification of determinants, barriers, and facilitators, advances in Artificial Intelligence (AI), specifically large language models (LLMs), may automate this process meaningfully by accelerating implementation evaluations within ePCTs. We developed and tested an automated algorithm using Chat GPT-4o and Chat GPT-4o-mini to achieve human-level performance coding interview transcripts. We created a Python-based system that uses LLMs to process and code semi-structured interview transcripts about implementation challenges in translating dementia interventions into healthcare systems. The system matches excerpts to an existing codebook. Multiple iterations, including expert review, were used to refine accuracy and efficiency. The LLM consistently coded more excerpts than humans. In the third iteration (V3), the LLM captured 61.7% of human-coded excerpts, with matching rates reaching as high as 72.6% for individual transcripts. Matching was higher for descriptive codes, 63.7%, than interpretive codes, 57.7%. The LLM identified 206 correct coded excerpts that human coders missed. In the fourth iteration (V4), GPT-4o outperformed GPT-4o-mini: descriptive code matching reached 89% (e.g. "Site Characteristics"), compared to 69% for GPT-4o-mini with the R1+R2 85% threshold. GPT-4o showed a weak, but positive correlation (r = 0.230) between transcript word count and matching agreement, while 4o-mini showed a moderate, but negative correlation (r = -0.452). The LLM workflow yielded a 97% reduction in time and a 99% reduction in cost per transcript. This study compared an LLM-powered workflow with human coding for thematic analysis. The LLM aligned strongly with human coders. While error rates necessitate human oversight, time and cost reduction, and ability to identify missed excerpts make it a potentially reliable supplementary tool. Although ePCTs and implementation science share complementary goals, they differ in focus, this flexible approach enhances efficiency and scalability, with acceptable accuracy. It streamlines the qualitative research workflow from outlining to the analysis of implementation processes in real-world settings and may accelerate existing implementation approaches while minimizing implementation resources.

This paper introduces a novel IoT-Enhanced Virtual Power Plant (VPP) framework that integrates edge-fog computing, blockchain-secured communication, and AI-driven market mechanisms to optimize energy management in smart grids. The proposed system addresses critical challenges in traditional VPPs including high latency (> 500ms), cybersecurity vulnerabilities (68% of grids report IoT risks), and low user engagement (< 30% adoption). Our framework achieves sub-50ms response times through hybrid edge-fog computing, resolves 94% of security vulnerabilities via blockchain consensus, and increases user participation by 116% through personalized demand response. Extensive simulations using real-world datasets from Pecan Street and prototype deployment with 120 IoT-connected distributed energy resources (DERs) demonstrate 23.1% improvement in energy efficiency, 17.3% cost reduction for end-users, and 142.3% return on investment over five years. The system integrates vehicle-to-grid (V2G) technology, achieving 25% better renewable energy utilization while maintaining grid stability. Implementation using open-source platforms (OpenEMS, GridLAB-D) ensures scalability up to 10,000 + DERs with modular architecture supporting community-driven innovation.

HighlightsThis study is one of the first empirical studies investigating AI-assisted mental health perceptions among university students in the Arab Gulf region.What are the main findings?University students demonstrated moderate awareness and cautious acceptance of AI in mental health, with trust emerging as a significant predictor of readiness to use AI-based tools.Key concerns included privacy, diagnostic accuracy, and limited emotional empathy, while students showed a clear preference for AI as a complementary rather than a re-placement tool.What are the implications of the main findings?Findings support the implementation of hybrid mental health care models in universities, where AI enhances early screening, accessibility, and continuous support along-side human counseling.Institutional adoption of AI should be guided by robust ethical frameworks, emphasizing data privacy, transparency, cultural sensitivity, and digital mental health literacy.Background: Artificial intelligence (AI) is widely used in mental health care for screening, monitoring, and intervention. Notably, most studies of AI in mental health have been performed in Western contexts, with limited evidence from the Arab Gulf region, where cultural factors such as stigma, privacy, and help-seeking norms may influence acceptance. Objective: Investigating university students’ perceptions of AI in mental health support, including awareness, trust, readiness, and preferences in a Gulf context. Methods: A cross-sectional survey was administered to 220 university students in Qatar. Data were analyzed using descriptive statistics, Chi-square tests, and one-way ANOVA to explore associations and group differences. Results: Students showed low-to-moderate levels of awareness and trust in AI-based mental health tools. The majority of participants showed that they were prepared to employ AI for stress management, but they do not prefer to replace face-to-face therapy, suggesting a preference for complementary use. A significant association was found between readiness and expectations (p < 0.00001), which means ambivalence toward AI effectiveness. No significant differences were observed across gender or academic level (p > 0.05). Key concerns included loss of human interaction, overreliance on technology, and diagnostic accuracy, while perceived benefits included cost reduction and 24/7 accessibility. Conclusions: Students exhibit cautious adoption of AI in mental health services. Acceptance is influenced by trust, privacy issues, and apparent compassion. AI is optimally situated as a supplementary instrument within ethically regulated, culturally attuned hybrid care frameworks that maintain the fundamental importance of human connection.

Abstract In today's competitive business landscape, organizations increasingly adopt automation to enhance operational efficiency, ensure financial accuracy, and reduce costs. This study investigates the comparative effectiveness of automated versus manual invoice processing within Vignesh Polymers India Pvt Ltd., a prominent manufacturer of plastic and polymer components. A structured questionnaire was administered to 201 employees drawn from a population of 420 using purposive sampling. To analyze the collected data, statistical tools including ANOVA, Pearson Correlation, Cronbach's Alpha Reliability Testing, and Binary Logistic Regression were applied via SPSS. Results indicate that automated invoicing significantly improves financial accuracy, reduces processing time, and leads to measurable cost savings relative to manual methods. Correlation analysis reveals strong relationships between automation adoption and reductions in data entry errors and human error. Reliability testing confirms the consistency of the instrument (α = 0.838). Binary logistic regression further demonstrates departmental variation in cost-saving success rates. Employee perceptions broadly support automation as a financially beneficial transformation for the organization. The study contributes empirical evidence to the literature on financial process automation within the Indian manufacturing context and offers actionable recommendations for practitioners. Keywords: invoice automation, manual processing, financial accuracy, cost reduction, accounts payable, manufacturing, India

Accurate evaluation of the caking property of non-coking coals, which generally exhibit limited metaplast-generation capacity during carbonisation compared with coking coals, is crucial for unlocking their utilisation potential in both cost reduction and efficiency improvement. Herein, an improved caking test was developed to distinguish weakly/non-caking coals under practical blending conditions. Structural characteristics of coals and macerals were quantified by Fourier Transform Infrared Spectroscopy with peak deconvolution, forming an integrated ‘petrology + structure’ framework for establishing quantitative correlations with caking indices. Results indicate that lean coal possesses superior blending potential compared to long-flame coal. Despite similarly limited metaplast-generation capability, lean coal forms a stronger carbon skeleton due to its higher degree of aromatisation and structural condensation. In contrast, long-flame coal exhibits more extensive aliphatic branching, higher thermal reactivity and a looser structural configuration, resulting in weaker cohesion and insufficient skeletal support. The generation potential of hydrocarbon ( P ) was identified as the most reliable predictor of caking behaviour owing to its consistent trends in both raw coals and their macerals. Accordingly, the theoretical P -value of blended coal ( P B . T ) was correlated with the caking index ( G R.I ) through the relationship lg G R.I = a P B . T 2 + b P B . T +c, enabling accurate prediction of the caking behaviour of blends containing non-coking constituents. This approach reduces testing costs by about 98% and eliminates pyrolysis-related emissions, offering substantial economic and environmental benefits.

The rapid industrial emergence of perovskite photovoltaics (PV) highlights their potential to complement silicon PV in meeting the growing global solar demand. As deployment scales, closed-loop recycling of perovskite PV will be beneficial to conserve critical resources and mitigate environmental risks associated with lead. However, mixed-cation, mixed-halide perovskites-typical in record-efficiency devices-undergo systematic composition drift during device fabrication. Consequently, material recovered from end-of-life modules inherits these deviations, degrading cell performance if reused without adjustment. To overcome this fundamental bottleneck in circular manufacturing, we developed a comprehensive quantification framework to audit and restore perovskite composition. By combining nuclear magnetic resonance (NMR), inductively coupled plasma-optical emission spectroscopy (ICP-OES), and ion chromatography (IC), we obtained full compositional fingerprints of the hybrid perovskite recovered from processed solar-cell stacks, allowing us to resolve their altered composition and restore the material to match the original precursor formulation. Composition restoration effectively closed the performance gap, yielding recycled perovskite cells with efficiencies comparable to pristine devices. A cost analysis demonstrates this approach can achieve a 69.1% cost reduction, while preserving supply-constrained elements like Cs and I. These results demonstrate a practical, compositionally informed pathway for the sustainable, closed-loop manufacturing of complex perovskite absorbers.

Automated demand response in residential sectors is critical for grid stability, but centralized control strategies fail to address the unique energy profiles of individual households. This limitation becomes more pronounced in districts where buildings differ in load demand, photovoltaic (PV) production and battery energy storage system (BESS) behavior, while electricity prices and grid carbon intensity vary hourly. Conventional rule-based controllers can exploit patterns, but they require tuning and do not generalize across heterogeneous buildings. Existing centralized reinforcement learning methods improve adaptivity, yet they often learn compromise policies and scale poorly as the number of buildings increases. To address these issues, this paper proposes an AttentionKAN-based multi-agent reinforcement learning controller for district-level BESS scheduling. The method uses centralized training with decentralized execution, where each building is controlled by its own actor and a centralized critic models cross-building interactions through a multi-head query-key-value attention mechanism. To improve approximation accuracy under nonlinear and constrained battery dynamics, multilayer perceptron (MLP) blocks in the actor and critic are replaced with Kolmogorov-Arnold Networks (KANs), whose spline-parameterized univariate functions capture saturation effects, tariff discontinuities and couplings among state of charge, PV availability and carbon intensity. Implemented in CityLearn and evaluated on a residential net-zero community dataset, the proposed controller is assessed using building-level and district-level indicators for cost, CO2 emissions, peak demand, ramping and load shape. The learned policy charges during solar-rich hours and discharges during evening peaks, achieving the strongest performance among benchmark controllers, including an approximately 50% cost reduction versus the reference case and emissions reduction. From a sustainability perspective, the results indicate that coordinated multi-building BESS control can support low-carbon residential electrification through emission reduction, lowering electricity expenditure and improving renewable-energy utilization and providing grid-supportive flexibility through reduced peaks and ramping.

Digital transformation is revolutionizing the hospitality industry, enabling hotels to achieve sustainable service excellence through technology-driven operations. This chapter examines how Internet of Things (IoT), automation, and artificial intelligence (AI) are integrated into hotel operations to enhance efficiency, reduce resource consumption, and improve guest experiences. The study explores practical applications such as smart energy management, predictive maintenance, automated housekeeping, and AI-driven customer service, highlighting their contribution to environmental sustainability and operational cost reduction. Through analysis of scholarly literature and real-world case studies, the chapter demonstrates that technological innovation not only optimizes hotel operations but also aligns sustainability with service quality, fostering a culture of responsible management. Challenges related to data privacy, investment costs, and workforce adaptation are discussed, along with strategies to maximize digital returns and sustainability outcomes. The chapter provides a conceptual framework linking smart technologies, operational efficiency, and sustainable performance, illustrating pathways for hotels to achieve eco-efficient service delivery in the digital era.

The article reveals the conceptual foundations of forming flexible logistics infrastructure for the needs of rapid reconstruction of Ukraine. The author proposes a transition from static capital-intensive facilities to a network of mobile railway hubs based on passive damping shielding technology, which allows gravitational unloading of gondola cars on any track section without constructing trestles. A stochastic model for economic optimization of the hub network has been developed, taking into account shortage risks, preservation of railway asset resources, and minimization of logistics costs under multiple demand scenarios. Practical calculations are provided for typical conditions of gravel and sand unloading, confirming the technical feasibility of the concept «Anywhere Logistics». The research results prove that mobile hubs provide an 87 % reduction in capital costs, reduction of deployment time to 1 hour, and increased flexibility of the logistics system with minimal impact on track infrastructure resources.A stochastic optimization model of a mobile hub network showed that a system with mobile elements provides 53.1 % lower total logistics costs compared to a traditional system with stationary terminals due to a significant reduction in the distance of «last mile» road transportation and the creation of an «Anywhere Logistics» approach.Mobile railway hubs for unloading bulk and construction cargo will quickly pay for themselves due to a significant reduction in road transportation costs. Further development of the concept «Anywhere Logistics» may be in the direction of adapting mobile railway logistics hubs to US conditions, which opens up significant opportunities for optimizing supply chains, but requires taking into account the specific legal and technological field of this country. The practical value of the method has been proven through a comparative analysis with traditional technologies based on the criteria of capital expenditure, deployment time, system flexibility and total cost of ownership.

The rapid advancement and deployment of next-generation nuclear reactors, which incorporate machine learning, artificial intelligence decision making, autonomous control, and remote operations, introduces new cybersecurity challenges not encountered by traditional light water reactors. Although these advanced reactors offer improved efficiency, scalability, and cost reduction, they also introduce vulnerabilities due to the integration of complex, interconnected digital systems. As a result, the cybersecurity landscape for advanced reactors requires a more dynamic and resilient architecture. This research proposes a framework for a vulnerability discovery and management tool for advanced reactors. The tool autonomously maps reactor networks and stores critical cybersecurity information, including the software bill of materials and vulnerability assessments using Common Vulnerabilities and Exposures. An emulated network environment, constructed using Minimega, replicates the logical topology of advanced reactor communication systems. This environment is paired with a high fidelity, full scope generic pressurized water reactor (GPWR) simulator that enables a controlled study of how cyber asset failures or compromises impact plant operations. The resulting test environment supports comprehensive penetration testing and validation of the proposed vulnerability discovery tool. The tool provides insight into security weaknesses and enables the evaluation of network architectures during both predeployment design and postdeployment operational phases. Initial testing shows that the framework automates network mapping and successfully identifies software and configuration vulnerabilities

This study analyzed the impact of blockchain technology on smart contracts within the field of privateinternational law. Through an interdisciplinary literature review, it examined the technical and legalfoundations of the technology, its advantages (immutability, transparency, cost reduction) andlimitations (anonymity, contractual rigidity, governance risks). It argued that smart contracts are selfexecutingagreements that, by operating in decentralized virtual environments, challenge classiccategories of contract law, such as freedom of contract and the interpretation of clauses. The articleconcluded that the application of blockchain can transform contractual and corporate structures,offering new possibilities for international cooperation and legal efficiency, provided that normativeintegrity and algorithmic transparency are preserved. The methodology employed was a literaturereview.

Purpose: The study explores the role of change management in ensuring business survival and growth within an increasingly dynamic and digitally driven environment. It investigates how leadership, employee adaptability, and technological integration collectively influence organisational resilience and competitiveness.Design / Methodology / Approach: A qualitative, exploratory design was adopted using secondary data drawn from peer-reviewed research (2019–2024). The study employed thematic analysis to identify key patterns linking change management with digital transformation, employee readiness, and customer engagement.Findings: Three core themes emerged:1. Employee adaptability through reskilling and leadership support improves change acceptance.2. Technological integration enhances operational efficiency and cost reduction.3. Customer-centric transformation—aided by neuromarketing and analytics—strengthens satisfaction and loyalty.These themes reveal that effective change management acts as a dynamic capability that continuously aligns people, processes, and technology.Practical Implications: Organisations should invest in leadership development, digital infrastructure, and skill enhancement to embed change readiness. For developing economies, focused capacity building and policy support are essential to overcome digital and cultural barriers.Originality / Value: The paper presents an integrated model linking Change Management → Digital Transformation → Employee Adaptation → Business Growth, offering a structured framework for sustainable organisational transformation.

ABSTRACT Leveraging the Administrative Penalty Measures of the China Banking Regulatory Commission (APMCB) as an exogenous setting, we examine how regulatory penalties on destination banks affect firms’ off-site investment. Our research indicates a notable positive impact of regulatory penalties on cross-regional capital flows. The reduction of credit costs and agency costs is the main mechanism. Our further evidence shows that credit-focused penalties characterized by relatively moderate severity and targeting individuals demonstrate a stronger effect on cross-regional capital flows. According to the heterogeneity analysis, the positive impact is amplified in subsamples characterized by private firms, higher financing constraints, and less investment experience. Our findings highlight how banking regulatory enforcement shapes capital flows and provide important implications for the design of financial policies.

Accurately integrating stiff ordinary differential equations (ODEs) and index-1 differential-algebraic equations that govern constrained multibody systems remains computationally demanding, especially for real-time applications. This article proposes an improved parsimonious physics-informed random-projection neural-network (PIRPNN) integrator that embeds explicit velocity- and position-correction into a single-hidden-layer random-feature framework and employs a vectorized assembly of system matrices for computation efficiency. Two illustrative examples are utilized to demonstrate the proposed algorithm and advantages. For the multibody dynamics benchmark problems examined, the improved PIRPNN demonstrates improved accuracy in terms of L 2 -trajectory error and energy drift, together with a notable reduction in computational cost compared with the original PIRPNN. At peculiar tolerances from 10 −6 to 10 −10 , it also outperforms the implicit MATLAB solvers ode15s and ode23t, further lowering both trajectory error and total-energy drift. The results underscore the potential of random-projection neural integrators as lightweight, constraint-preserving integration method alternative to classical integrators or more complex learning-based approaches in real-time multibody simulation.

As global supply chains increasingly prioritize environmental sustainability and operational efficiency, battery electric freight vehicles (EFVs) have emerged as a pivotal alternative to traditional diesel-powered logistics fleets. This paper addresses the integrated planning and scheduling problem for multi-modal logistics systems utilizing EFVs. An integrated model is proposed to determine the number of electric freight vehicles and optimize dispatch and charging schedules, considering deadheading, and time-of-use electricity pricing. The model is formulated as an integer linear programming (ILP) problem solvable by commercial solvers. A branch-and-price framework and a heuristic algorithm are developed to handle large-scale instances. A case study using real data from a logistics provider in China demonstrates that the EFV system achieves a 45.5% reduction in total monthly costs compared to traditional diesel freight vehicle systems, even after accounting for higher vehicle and infrastructure costs. Sensitivity analyses offer practical insights for EFV adoption in multi-modal logistics.