Abstract
Being a part of society, employees' behavior can't be ignored, and it must be encouraged to sustain nature for the upcoming generation. Following the resource-based view theory, this study aims to identify the factors influencing employees toward sustainable behavior. To meet the objectives, cross-sectional data were collected from employees of manufacturing companies, and structural equation modeling was used for the analysis. The study results show a positive effect of participative decision-making and employee motivation on employees' eco-friendly innovation capabilities and behavior. Additionally, this research reveals that employee motivation partially mediates the link between participative decision-making, eco-friendly innovation capabilities, and behavior. Furthermore, this research evidenced a positive moderation of green culture on the relationship between participative decision-making and eco-friendly innovation capabilities, evidencing that the relationship is stronger when the culture is high. This research contributes to the existing literature by providing a deeper understanding of the factors influencing employees' eco-friendly innovation capabilities and behavior. It highlights the significant roles of green culture as a moderator and employee motivation as a mediator, offering novel perspectives to both theory and practice.

Abstract
Background and objectives: Neurodegenerative diseases, including Alzheimer's disease (AD), mild cognitive impairment (MCI), and frontotemporal dementia (FTD), are a growing public health challenge, with dementia incidence projected to triple in the coming decades. AD is associated with memory impairment, bvFTD with behavioral dysfunction, and MCI as a transitional stage between normal cognition and dementia. While structural brain changes have been widely studied, the role of neurotransmitter pathways remains underexplored. This study aims to correlate gray matter atrophy in AD, bvFTD, and MCI with neurotransmitter pathways to identify distinctive neurochemical impairments. Methods: We included 214 participants (89 CE, 74 bvFTD, 51 MCI) from a single-center cohort. MRI from 3 T scanners was segmented via FreeSurfer. Neurotransmitter maps were sourced from JuSpace. We performed volumetric and whole-brain correlation analyses to evaluate relationships between brain regional volumes (BRVs) and neurotransmitter pathways. Group differences were assessed with Kruskal-Wallis tests followed by post-hoc analyses. Results: Volumetric analysis showed expected atrophy patterns in each group. Correlation analysis indicated distinct neurotransmitter involvement: AD showed significant atrophy correlations with dopamine D2 and GABA A receptor distribution; bvFTD had significant negative correlations with the mu-opioid receptor; MCI exhibited early serotonergic dysregulation. Conclusions: We identified distinct atrophy patterns linked to specific neurotransmitter systems, each showing unique neurochemical profiles. In AD, precuneus and inferior parietal lobules atrophy aligns with dopaminergic and GABAergic receptors, potentially impacting memory and executive functions. In bvFTD, medial orbitofrontal and temporal atrophy, is linked to mu-opioid receptor impairment, possibly contributing to behavioral symptoms. In MCI, early serotonergic dysregulation involving SERT occurs before detectable atrophy.

Abstract
The efficiency of wastewater treatment systems must be reflected in the removal of the pollutant load from the influent and the optimal energy performance of electrical equipment. Wastewater Treatment Plants (WWTPs) are part of the Intensive Energy Consumption Management System (SGCIE) and are therefore subject to mandatory energy audits. This article aims to assess the impact of an energy audit in a WWTP, according to ISO 50001:2018 and the Plan-Do-Check-Act (PDCA) methodology, to identify and quantify both persistent and transient energy inefficiencies. According to the results, the energy audit contributed to an approximate 10.8% reduction in electrical energy consumption. During the assessment, several challenges were identified that may compromise the effectiveness of audits in improving energy performance. The complexity of the treatment model, aging infrastructure and equipment, the lack of real-time data, and a limited number of indicators hinder the proper management of inefficiency phenomena, particularly transient ones.

Abstract
Adaptive variational quantum algorithms arguably offer the best prospects for quantum advantage in the Noisy Intermediate-Scale Quantum era. Since the inception of the first such algorithm, the Adaptive Derivative-Assembled Problem-Tailored Variational Quantum Eigensolver (ADAPT-VQE), many improvements have appeared in the literature. We combine the key improvements along with a novel operator pool-which we term Coupled Exchange Operator (CEO) pool-to assess the cost of running state-of-the-art ADAPT-VQE on hardware in terms of measurement counts and circuit depth. We show a dramatic reduction of these quantum computational resources compared to the early versions of the algorithm: CNOT count, CNOT depth and measurement costs are reduced by up to 88%, 96% and 99.6%, respectively, for molecules represented by 12 to 14 qubits (LiH, H6 and BeH2). We also find that our state-of-the-art CEO-ADAPT-VQE outperforms the Unitary Coupled Cluster Singles and Doubles ansatz, the most widely used static VQE ansatz, in all relevant metrics, and offers a five order of magnitude decrease in measurement costs as compared to other static ans & auml;tze with competitive CNOT counts.

Abstract
Quasi-identifiers (QIDs) are attributes in a dataset that are not directly unique identifiers of the users/entities themselves but can be used, often in conjunction with other datasets or information, to identify individuals and thus present a privacy risk in data sharing and analysis. Identifying QIDs is important in developing proper strategies for anonymization and data sanitization. This paper proposes QIDLEARNINGLIB, a Python library that offers a set of metrics and tools to measure the qualities of QIDs and identify them in data sets. It incorporates metrics from different domains-causality, privacy, data utility, and performance-to offer a holistic assessment of the properties of attributes in a given tabular dataset. Furthermore, QIDLEARNINGLIB offers visual analysis tools to present how these metrics shift over a dataset and implements an extensible framework that employs multiple optimization algorithms such as an evolutionary algorithm, simulated annealing, and greedy search using these metrics to identify a meaningful set of QIDs.

Abstract
Introduction: Neurodegenerative disorders, such as Alzheimer's disease (AD) and frontotemporal dementia (bvFTD), reflect a spectrum of cognitive impairments unified by cognitive decline. Traditional diagnostic approaches often overlook shared landscapes of these disorders. A transdiagnostic approach, cutting across conventional boundaries, may improve understanding of shared mechanisms. This study uses lesion-symptom mapping (LSM) to identify critical brain structures responsible for cognitive impairments. Methods: Patients diagnosed with Mild Cognitive Impairment (MCI), probable AD, and probable bvFTD were recruited from our memory clinic. Diagnoses were made by a multidisciplinary team using established criteria. Participants underwent detailed medical and neurological examinations, neuroimaging, cerebrospinal fluid analysis, and neuropsychological assessment. MRI scans were processed using FreeSurfer. LSM was used to assess correlations between brain structures and cognitive performance. Results: Significant correlations were found between neuropsychological test scores and reduced volume in specific brain regions. The Free and Cued Selective Reminding Test was linked to the right hippocampus and left nucleus accumbens. The Brief Visuospatial Memory Test-Revised correlated with the right hippocampus, left nucleus accumbens, and right middle temporal gyrus. Verbal fluency was linked to the left superior temporal sulcus and left middle temporal gyrus. Digit Span forward correlated with left superior frontal gyrus and left inferior parietal region, while Digit Span backward was linked to the right precuneus. Digit-Symbol Coding was associated with the left inferior parietal region. Conclusions: This study highlights common neural targets in MCI, AD, and bvFTD and their link with cognitive impairment, emphasizing the value of LSM within a transdiagnostic approach to neurodegenerative diseases.