搜索结果：Nonprofit and voluntary sector quarterly

During the COVID-19 pandemic, governments faced the challenge of managing population behavior to prevent their healthcare systems from collapsing. Sweden adopted a strategy centered on voluntary sanitary recommendations while Belgium resorted to mandatory measures. Their consequences on pandemic progression and associated economic impacts remain insufficiently understood. This study leverages the divergent policies of Belgium and Sweden during the COVID-19 pandemic to relax the unrealistic -- but persistently used -- assumption that social contacts are not influenced by an epidemic's dynamics. We develop an epidemiological-economic co-simulation model where pandemic-induced behavioral changes are a superposition of voluntary actions driven by fear, prosocial behavior or social pressure, and compulsory compliance with government directives. Our findings emphasize the importance of early responses, which reduce the stringency of measures necessary to safeguard healthcare systems and minimize ensuing economic damage. Voluntary behavioral changes lead to a pattern of recurring epidemics, which should be regarded as the natural long-term course of pandemics. Governments should carefully

Public and nonprofit organizations often hesitate to adopt AI tools because most models are opaque even though standard approaches typically analyze aggregate patterns rather than offering actionable, case-level guidance. This study tests a practitioner-in-the-loop workflow that pairs transparent decision-tree models with large language models (LLMs) to improve predictive accuracy, interpretability, and the generation of practical insights. Using data from an ongoing college-success program, we build interpretable decision trees to surface key predictors. We then provide each tree's structure to an LLM, enabling it to reproduce case-level predictions grounded in the transparent models. Practitioners participate throughout feature engineering, model design, explanation review, and usability assessment, ensuring that field expertise informs the analysis at every stage. Results show that integrating transparent models, LLMs, and practitioner input yields accurate, trustworthy, and actionable case-level evaluations, offering a viable pathway for responsible AI adoption in the public and nonprofit sectors.

This paper addresses the methodology for the quarterly estimation of Compensation of Employees paid by the General Government (GG) sector, in accordance with the European System of Accounts (ESA 2010). Due to the limited high-frequency data availability and the need to guarantee the consistency with annual constraints, quarterly estimation relies on indirect temporal disaggregation techniques. These methods use specific infra-annual indicators as proxies for the variables being estimated. The specific case of the quarterly estimation of Compensation of employees presents several additional challenges. Firstly, the information provided by the sources, based on cash or legal-accrual data, is elaborated to define indicators which respect the accrual ESA 2010 principle as the annual estimates, based on more compliant data sources such as final budgets of public entities. Secondly, at a quarterly level the extraordinary events - such as the recording of delayed collective bargaining agreements which result in arrears - have a strong impact on quarterly indicators, whereas their effect is mitigated at annual level. To attribute these flows to the period when the work is performed, multi-

G(2) is the smallest exceptional group and it is the simplest and viable gauge group to minimally extend the strong interaction sector: G(2) includes the group SU(3) of Quantum Chromodynamics (QCD) as a maximal subgroup and it is equipped with six additional gluons that can acquire mass via a Higgs mechanism driven by a new Higgs particle and constitute dark matter. In this article I want to describe how the exceptional G(2) group can be a physical gauge group, capable of extending the Standard Model (SM) of particles and including a versatile dark sector, which is compatible with experimental observations. In fact, due to its peculiar mathematical features, the group G(2) manifests some complex features, not properly considered in literature, which guarantee its correct use in physics, as its {3}+anti{3} decompositions w.r.t. SU(3) can acquire a complex structure. The resulting framework can be a solid Beyond Standard Model (BSM) solution for the dark matter (DM) problem, in the form of massive complex scalar glueballs, and it includes the proper color representations for quarks and leptons. Several quantum field theory features are discussed, like the G(2) coupling constant runni

Mobility is severely impacted in patients with Parkinson's disease (PD), especially when they experience involuntary stopping from the freezing of gait (FOG). Understanding the neurophysiological difference between "voluntary stopping" and "involuntary stopping" caused by FOG is vital for the detection and potential intervention of FOG in the daily lives of patients. This study characterised the electroencephalographic (EEG) signature associated with FOG in contrast to voluntary stopping. The protocol consisted of a timed up-and-go (TUG) task and an additional TUG task with a voluntary stopping component, where participants reacted to verbal "stop" and "walk" instructions by voluntarily stopping or walking. Event-related spectral perturbation (ERSP) analysis was used to study the dynamics of the EEG spectra induced by different walking phases, which included normal walking, voluntary stopping and episodes of involuntary stopping (FOG), as well as the transition windows between normal walking and voluntary stopping or FOG. These results demonstrate for the first time that the EEG signal during the transition from walking to voluntary stopping is distinguishable from that of the tran

Enhancing sustainable development performance requires an assessment of the relative roles of economic sectors in this process. However, comparative empirical evidence regarding the sectoral structure of sustainable development is limited, particularly for Turkiye. Therefore, this study examines the long-run relationship between sectoral structure and sustainable development in Turkiye by focusing on agriculture, industry, construction, and services. The empirical analysis uses annual data for the period 2000-2022 and proceeds in three steps. First, the stationarity properties of the variables are examined using ADF, PP, and Zivot-Andrews unit root tests. The Johansen cointegration test is then applied to determine whether a long-run equilibrium relationship exists among the variables. Finally, long-run coefficients are estimated using the DOLS estimator, while the FMOLS estimator is used as a robustness check. The findings show that all sectoral shares are positively associated with the sustainable development index in the long run. Based on the DOLS results, the services sector has the highest coefficient at 0.882, followed by the agriculture, industry, and construction sectors w

This paper develops a multi-period optimization framework to design a voluntary renewable program (VRP) for an electric utility company, aiming to maximize total renewable energy deployments. In the business model of VRP, the utility must ensure it generates renewable energy up to the total amount of contract during each market episode (i.e., a year), while all the revenue collected from the VRP must either be used to invest in procuring renewable capacities or to maintain the current renewable fleet and infrastructure. We thus formulate the problem as an optimal pricing problem coupled with revenue allocation and renewable deployment decisions. We model the demand function of voluntary renewable contracts as an exponential decay function based on survey data. We analytically derive the optimal pricing policy of the VRP as a function of the current grid carbon intensity. We prove that a myopic policy is conditionally optimal, which maximizes renewable capacity in each period, attains the long-run optimum due to the utility's revenue-neutral constraint. We show different binding conditions and marginal values of decision variables correspond to different phases of the energy transit

Transdisciplinary research, the co-creation of scientific knowledge by multiple stakeholders, is considered essential for addressing major societal problems. Research policy makers and academic leaders frequently call for closer collaboration between academia and societal stakeholders to address the grand challenges of our time. This bibliometric study evaluates progress in collaboration between academia and three societal stakeholders: industry, government, and nonprofit organisations. It analyses the level of co-publishing between academia and these societal stakeholders over the period 2013-2022. We found that research collaboration between academia and all stakeholder types studied grew in absolute terms. However, academia-industry collaboration declined 16% relative to overall academic output while academia-government and academia-nonprofit collaboration grew at roughly the same pace as academic output. Country and field of research breakdowns revealed wide variance. In light of previous work, we consider potential explanations for the gap between policymakers' aspirations and the real global trends. This study is a useful demonstration of large scale, quantitative bibliometri

The integration of AI into journalism challenges participatory design (PD), particularly with respect to stakeholder influence, workplace perceptions, and organizational dynamics. Traditional PD assumes that users can shape technologies, yet AI systems resist influence due to opaque data, fixed architectures, and inaccessible objectives. Through interviews with 10 journalists, we identify the perception gap, showing that trust in AI depends on perceived agency within workplace participatory workflows. Informed by these findings, we introduce the Gradual Voluntary Participation (GVP) framework in journalism and its five core principles, reconceptualizing participation as a gradual and voluntary process that can be operationalized at the newsroom level, beyond fixed workshops or one-time preference-elicitation campaigns. Addressing epistemic burdens, participatory ceilings, and performative consultations, GVP treats gradualism and voluntariness as design dimensions that shape perception, legitimacy, and ownership. Moving beyond unidimensional ladder metaphors and adopting a bidimensional matrix structure, the framework maps stakeholders across depth and scope, offering a new model fo

Recently, a shared-autonomous scheme has been introduced into prosthetic hand control field, where the user provides high-level intent by moving the hand towards the target, and the artificial intelligence system autonomously executes low-level control (e.g., grasp and release the object). This system reduces user workload but risks unintended grasp or release actions without explicit user control. In particular, release actions remain challenging, as vision-based autonomous systems typically assume that proximity to a supporting surface signals the user's intent to let go, making mid-air release tasks difficult and error-prone. This study presents an inertial measurement unit (IMU)-based gesture-triggered interface enabling voluntary initiation or override of grasp and release actions to the autonomous system. A real-time motion detection algorithm recognizes three deliberate upper-limb gestures: shoulder shrug, elbow flap, and wrist shake, across three control paradigms: autonomous, hybrid, and manual. In a controlled study with 14 able-bodied participants and one individual with an upper-limb difference, the elbow flap emerged as the most preferred gesture (66% preference) and a

The inception of AI-based fraud detection systems has presented the banking sector across the globe the opportunity to enhance fraud prevention mechanisms. However, the extent of adoption in Nigeria has been slow, fragmented, and inconsistent due to high cost of implementation and lack of technical expertise. This study seeks to investigate extent of adoption and determinants of AI-driven fraud detection systems in Nigerian banks. This study adopted a cross-sectional survey research design. Data were extracted from primary sources through structured questionnaire based on 5-point Likert scale. The population of the study consist of 24 licensed banks in Nigeria. A purposive sampling technique was used to select 5 biggest banks based on market capitalization and customer base. The Ordered Logistic Regression (OLR) model was used to estimate the data. The results showed that top management support, IT infrastructure, regulatory compliance, staff competency and perceived effectiveness accelerate the uptake of AI-driven fraud detection systems adoption. However, high implementation cost discourages it. Therefore, the study recommended that banks should invest in modern and scalable IT s

We discuss a dark family of lepton-like particles with their own "private" gauge bosons under a local SU'(2)xU'(1) symmetry. The product of dark and visible gauge groups SU'(2)xU'(1)xSU_w(2)xU_Y(1) is broken dynamically to the diagonal (vector-like) subgroup SU(2)xU(1) through the coupling of two scalar fields M_i to the Higgs field and the dark lepton-like particles. After substituting vacuum expectation values for the fields M_i, the Higgs doublet couples in the standard way to the left-handed SU'(2) doublet and right-handed singlets of the dark gauge group, but not to the extra gauge bosons. This defines a new Higgs portal, where the "dark leptons" can contribute to the dark matter and interact with Standard Model matter through Higgs exchange. It also defines a dark matter model with internal interactions. At low energies, the Standard Model Higgs boson aligns the two electroweak-type symmetry groups in the visible and dark sectors and generates the masses in both sectors. We also identify charge assignments in the dark sector which allow for the formation of dark atoms as bound states of dark lepton-like particles. The simplest single-component dark matter version of the model

Tangent categories provide an axiomatic framework for understanding various tangent bundles and differential operations that occur in differential geometry, algebraic geometry, abstract homotopy theory, and computer science. Previous work has shown that one can formulate and prove a wide variety of definitions and results from differential geometry in an arbitrary tangent category, including generalizations of vector fields and their Lie bracket, vector bundles, and connections. In this paper we investigate differential and sector forms in tangent categories. We show that sector forms in any tangent category have a rich structure: they form a symmetric cosimplicial object. This appears to be a new result in differential geometry, even for smooth manifolds. In the category of smooth manifolds, the resulting complex of sector forms has a subcomplex isomorphic to the de Rham complex of differential forms, which may be identified with alternating sector forms. Further, the symmetric cosimplicial structure on sector forms arises naturally through a new equational presentation of symmetric cosimplicial objects, which we develop herein.

Cooperation underlies many natural and artificial systems. While voluntary participation can sustain cooperation without informational assumptions, real interactions are rarely anonymous, leaving the joint effects of participation and reputation insufficiently understood. We propose a reputation-based voluntary Prisoner's Dilemma in which agents incur a monitoring cost to inspect opponents and decide whether to exit an interaction for a fixed incentive to avoid exploitation or to default to cooperation or defection. We show that reputation-conditioned exit generates multiple coexistence pathways that sustain cooperation across population structures. In well-mixed populations, cooperation persists through stable mixed coexistence, whereas in structured populations, exit-incentive-dependent regimes emerge, including local cyclic dominance and persistent oscillations. Together, these results extend voluntary participation frameworks and underscore the role of exit-incentive design in cooperative multi-agent systems.

Even when a tool is explicitly described as unfair and harmful to others, ostensibly safety-aligned LLM agents still voluntarily engage in secret collusion whenever doing so confers a strategic advantage. To investigate this phenomenon, we introduce an empirical framework built on two strategic multi-agent environments: Liar's Bar, a competitive deception scenario, and Cleanup, a mixed-motive resource-management scenario, in which agents are offered secret collusion tools that provide significant advantages while clearly disadvantaging the other agents. Across 12 models (at the 7B, 70B, and proprietary scales) and 6 prompt variants, we find that most agents consistently accept these tools and develop collusive strategies, while explicitly acknowledging the unfairness of the tools before accepting. We further show that neither the unfairness labels nor baseline alignment alone reliably deters collusion: only explicit ethical framing reduces adoption and, even then, smaller models remain susceptible. More broadly, our work presents the first systematic investigation of voluntary collusion adoption in LLM-based multi-agent systems, and suggests that preventing such behaviour requires

This study examines what drives organizational adoption and use of social media through a model built around four key factors - strategy, capacity, governance, and environment. Using Twitter, Facebook, and other data on 100 large US nonprofit organizations, the model is employed to examine the determinants of three key facets of social media utilization: 1) adoption, 2) frequency of use, and 3) dialogue. We find that organizational strategies, capacities, governance features, and external pressures all play a part in these social media adoption and utilization outcomes. Through its integrated, multi-disciplinary theoretical perspective, this study thus helps foster understanding of which types of organizations are able and willing to adopt and juggle multiple social media accounts, to use those accounts to communicate more frequently with their external publics, and to build relationships with those publics through the sending of dialogic messages.

We present a data-driven pipeline developed in collaboration with the Power Packs Project, a nonprofit addressing food insecurity in local communities. The system integrates data extraction from PDFs, large language models for ingredient standardization, and binary integer programming to generate a 15-week recipe schedule that minimizes projected wholesale costs while meeting nutritional constraints. All 157 recipes were mapped to a nutritional database and assigned estimated and predicted costs using historical invoice data and category-specific inflation adjustments. The model effectively handles real-world price volatility and is structured for easy updates as new recipes or cost data become available. Optimization results show that constraint-based selection yields nutritionally balanced and cost-efficient plans under uncertainty. To facilitate real-time decision-making, we deployed a searchable web platform that integrates analytical models into daily operations by enabling staff to explore recipes by ingredient, category, or through an optimized meal plan.

Evaluation has always been a key challenge in the development of artificial intelligence (AI) based software, due to the technical complexity of the software artifact and, often, its embedding in complex sociotechnical processes. Recent advances in machine learning (ML) enabled by deep neural networks has exacerbated the challenge of evaluating such software due to the opaque nature of these ML-based artifacts. A key related issue is the (in)ability of such systems to generate useful explanations of their outputs, and we argue that the explanation and evaluation problems are closely linked. The paper models the elements of a ML-based AI system in the context of public sector decision (PSD) applications involving both artificial and human intelligence, and maps these elements against issues in both evaluation and explanation, showing how the two are related. We consider a number of common PSD application patterns in the light of our model, and identify a set of key issues connected to explanation and evaluation in each case. Finally, we propose multiple strategies to promote wider adoption of AI/ML technologies in PSD, where each is distinguished by a focus on different elements of

In the wake of epidemics, quarantine measures are typically recommended by health authorities or governments to help control the spread of the disease. Compared with mandatory quarantine, voluntary quarantine offers individuals the liberty to decide whether to isolate themselves in case of infection exposure, driven by their personal assessment of the trade-off between economic loss and health risks as well as their own sense of social responsibility and concern for public health. To better understand self-motivated health behavior choices under these factors, here we incorporate voluntary quarantine into an endemic disease model -- the susceptible-infected-susceptible (SIS) model -- and perform comprehensive agent-based simulations to characterize the resulting behavior-disease interactions in structured populations. We quantify the conditions under which voluntary quarantine will be an effective intervention measure to mitigate disease burden. Furthermore, we demonstrate how individual decision-making factors, including the level of temptation to refrain from quarantine and the degree of social compassion, impact compliance levels of voluntary quarantines and the consequent colle