Key Set Research Articles

Assessing Implementation of Social Screening Within US Health Care Settings: A Systematic Scoping Review.

Though a growing crop of health care reforms aims to encourage health care-based social screening, no literature has synthesized existing social screening implementation research to inform screening practice and policymaking. Systematic scoping review of peer-reviewed literature on social screening implementation published 1/1/2011-2/17/2022. We applied a 2-concept search (health care-based screening; social risk factors) to PubMed and Embase. Studies had to explore the implementation of health care-based multi-domain social screening and describe 1+ outcome related to the reach, adoption, implementation, and/or maintenance of screening. Two reviewers extracted data related to key study elements, including sample, setting, and implementation outcomes. Forty-two articles met inclusion criteria. Reach (n = 7): We found differences in screening rates by patient race/ethnicity; findings varied across studies. Patients who preferred Spanish had lower screening rates than English-preferring patients. Adoption (n = 13): Workforce education and dedicated quality improvement projects increased screening adoption. Implementation (n = 32): Time was the most cited barrier to screening; administration time differed by tool/workforce/modality. Use of standardized screening tools/workflows improved screening integration. Use of community health workers and/or technology improved risk disclosure and facilitated screening in resource-limited settings. Maintenance (n = 1): Only 1 study reported on maintenance; results showed a drop in screening over 21 months. Critical evidence gaps in social screening implementation persist. These include gaps in knowledge about effective strategies for integrating social screening into clinical workflows and ways to maximize screening equity. Future research should leverage the rapidly increasing number of screening initiatives to elevate and scale best practices.

From niche-innovation to mainstream markets: Drivers and challenges of industry adoption of agrivoltaics in the U.S.

Agrivoltaic systems harmonize agriculture and solar energy to mitigate land use competition, strengthen agricultural viability and resilience, and enhance solar development practice. Innovations in agrivoltaics has gained traction globally yet exist in niche-application and early adoption stages in the U.S. While initial research has emphasized technical and economic performance, critical questions remain about stakeholder adoption, social acceptance, and the role of policymakers. To better define the socio-political landscape for agrivoltaic development, we leverage qualitative interviews with U.S. solar professionals. We articulate key sets of drivers and challenges of industry adoption and their interactions. The findings suggest that overcoming the challenges impeding U.S. solar industry adoption of agrivoltaics will require robust market mechanisms that stimulate price improvements as well as coordinated, cross-sector learning processes, research, and regulation. We maintain that socio-political adaptations, coupled with techno-economic advances in price and performance, may constitute the key improvements of the development landscape for agrivoltaics in the U.S. Multi-stakeholder considerations and the co-evolution of technology, practice, and regulation are discussed in the context of developing the enabling framework conditions to progress agrivoltaics from niche-innovation to mainstream markets.

Comprehensive Performance Study of Hashing Functions

Most literature on hashing functions speaks in terms of hashing functions being either ‘good’ or ‘bad’. In this paper, we demonstrate how a hashing function that gives good results for one key set, performs badly for another. We also demonstrate that, for a single key set, we can find hashing functions that hash the keys with varying performances ranging from perfect to worst distributions. We present a study on the effect of changing the prime number ‘$p$’ on the performance of a hashing function from $H_1$ Class of Universal Hashing Functions. This paper then explores a way to characterize hashing functions by studying their performance over all subsets of a chosen Universe. We compare the performance of some popular hashing functions based on the average search performance and the number of perfect and worst-case distributions over different key sets chosen from a Universe. The experimental results show that the division-remainder method provides the best distribution for most key sets of the Universe when compared to other hashing functions including functions from $H_1$ Class of Universal Hashing Functions.

A Meta-model for Key Performance Indicators in Higher Education

We propose a software solution for representing diverse sets of key performance indicators in higher education. Our solution addresses both the heterogeneity and the common structure of key performance indicators. To tackle the issue of heterogeneity, we employ metamodeling and propose a meta-model that is expressive and generic enough to represent any set of key performance indicators in higher education. The proposed meta-model is more abstract than any specific key performance indicators set, and the sets are considered as models, which are instances of the proposed metamodel. We address the heterogeneity in calculating the key performance indicators' values by representing them with mathematical formulas and utilizing an expression language that allows for their dynamic evaluation. We verified the solution by representing typical key performance indicator sets and developing a software application prototype that enables the creation, monitoring, and further development of key performance indicator sets. The verification confirms the wide applicability of our proposed solution.

Impacts of improved agricultural technology adoption on welfare in Africa: A meta-analysis

A large body of researches have widely examined the impact of adopting improved agricultural practices and technologies on general welfare of smallholder farmers. The results of deep literature review show that varies agricultural technologies have significant impacts on different welfare measures identified in the primary studies. However, the estimated effects of technology adoption differ among studies. The current study presents a meta-analysis of empirical estimates using a sample of 52 studies that investigated the impact of improved agricultural technologies in Africa on three key sets of outcome variables: output or expenditure, food security, and poverty. The study also conducted tests for publication bias to see if researchers tend to report results in similar or different ways for the same outcome variable. The findings the study shed light on the ways of identifying potential factors explaining the differences in the effects of estimated technology adoption. Results of the meta-regression analysis revealed that differences in the reported impact of technologies is explained by factors like data type, model specification, sample size, region of the study, and journal type. It was also observed that no publication bias in the studies reviewed for the effect size measures of output (expenditure) and poverty models, but in the food security model there is some evidence of publication bias. One of the core implications of the current study is that, based on the sensitivity of effect sizes to study attributes (i.e. data type, econometric methods, sample size, region of the study, and journal type), interested researchers and academicians need to pay attention to these attributes to provide more reliable estimates for policy interventions. We believe this study provides information useful to interested decision-makers in designing policy intervention measures that could encourage the adoption of improved agricultural practices and technologies in the African context. Finally, the study also highlighted future research directions.

Token Mixing: Parameter-Efficient Transfer Learning from Image-Language to Video-Language

Applying large scale pre-trained image-language model to video-language tasks has recently become a trend, which brings two challenges. One is how to effectively transfer knowledge from static images to dynamic videos, and the other is how to deal with the prohibitive cost of fully fine-tuning due to growing model size. Existing works that attempt to realize parameter-efficient image-language to video-language transfer learning can be categorized into two types: 1) appending a sequence of temporal transformer blocks after the 2D Vision Transformer (ViT), and 2) inserting a temporal block into the ViT architecture. While these two types of methods only require fine-tuning the newly added components, there are still many parameters to update, and they are only validated on a single video-language task. In this work, based on our analysis of the core ideas of different temporal modeling components in existing approaches, we propose a token mixing strategy to enable cross-frame interactions, which enables transferring from the pre-trained image-language model to video-language tasks through selecting and mixing a key set and a value set from the input video samples. As token mixing does not require the addition of any components or modules, we can directly partially fine-tune the pre-trained image-language model to achieve parameter-efficiency. We carry out extensive experiments to compare our proposed token mixing method with other parameter-efficient transfer learning methods. Our token mixing method outperforms other methods on both understanding tasks and generation tasks. Besides, our method achieves new records on multiple video-language tasks. The code is available at https://github.com/yuqi657/video_language_model.

A generalization of Bernstein–Vazirani algorithm with multiple secret keys and a probabilistic oracle

A probabilistic version of the Bernstein-Vazirani problem (which is a generalization of the original Bernstein-Vazirani problem) and a quantum algorithm to solve it are proposed. The problem involves finding one or more secret keys from a set of multiple secret keys (encoded in binary form) using a quantum oracle. From a set of multiple unknown keys, the proposed quantum algorithm is capable of (a) obtaining any key (with certainty) using a single query to the probabilistic oracle and (b) finding all keys with a high probability (approaching 1 in the limiting case). In contrast, a classical algorithm will be unable to find even a single bit of a secret key with certainty (in the general case). Owing to the probabilistic nature of the oracle, a classical algorithm can only be useful in obtaining limiting probability distributions of $ 0 $ and $ 1 $ for each bit-position of secret keys (based on multiple oracle calls) and this information can further be used to infer some estimates on the distribution of secret keys based on combinatorial considerations. For comparison, it is worth noting that a classical algorithm can be used to exactly solve the original Bernstein-Vazirani problem (involving a deterministic oracle and a single hidden key containing $n$ bits) with a query complexity of $\mathcal{O}(n)$. An interesting class of problems similar to the probabilistic version of the Bernstein-Vazirani problem can be construed, where quantum algorithms can provide efficient solutions with certainty or with a high degree of confidence and classical algorithms would fail to do so.

Unbounded Predicate Inner Product Functional Encryption from Pairings

Predicate inner product functional encryption (P-IPFE) is essentially attribute-based IPFE (AB-IPFE) which additionally hides attributes associated to ciphertexts. In a P-IPFE, a message {textbf {x}} is encrypted under an attribute {textbf {w}} and a secret key is generated for a pair ({textbf {y}}, {textbf {v}}) such that recovery of langle {{textbf {x}}}, {{textbf {y}}}rangle requires the vectors {textbf {w}}, {textbf {v}} to satisfy a linear relation. We call a P-IPFE unbounded if it can encrypt unbounded length attributes and message vectors. bullet zero predicate IPFE. We construct the first unbounded zero predicate IPFE (UZP-IPFE) which recovers langle {{textbf {x}}}, {{textbf {y}}}rangle if langle {{textbf {w}}}, {{textbf {v}}}rangle =0. This construction is inspired by the unbounded IPFE of Tomida and Takashima (ASIACRYPT 2018) and the unbounded zero inner product encryption of Okamoto and Takashima (ASIACRYPT 2012). The UZP-IPFE stands secure against general attackers capable of decrypting the challenge ciphertext. Concretely, it provides full attribute-hiding security in the indistinguishability-based semi-adaptive model under the standard symmetric external Diffie–Hellman assumption. bullet non-zero predicate IPFE. We present the first unbounded non-zero predicate IPFE (UNP-IPFE) that successfully recovers langle {{textbf {x}}}, {{textbf {y}}}rangle if langle {{textbf {w}}}, {{textbf {v}}}rangle ne 0. We generically transform an unbounded quadratic FE (UQFE) scheme to weak attribute-hiding UNP-IPFE in both public and secret key setting. Interestingly, our secret key simulation secure UNP-IPFE has succinct secret keys and is constructed from a novel succinct UQFE that we build in the random oracle model. We leave the problem of constructing a succinct public key UNP-IPFE or UQFE in the standard model as an important open problem.

Guantánamo's Legacy

The military detention facility at the Guantánamo Bay naval base is the most enduring manifestation of the US “war on terror.” It is also materially and symbolically central to US torture, war crimes, and other egregious violations of law in the post-9/11 era. Since the first detainees arrived in 2002, Guantánamo has been the subject of controversy and debate, as well as a key setting for legal challenges to government policies. This article traces the legacy of the prison and the military commissions across four administrations. It demonstrates that the lack of a common understanding or shared narrative about what Guantánamo means or has meant is a product of entrenched partisanship that characterizes contemporary US politics more broadly. Guantánamo's confounding legacy reflects the lack of a national consensus about the role of laws and courts as guarantors of even the most basic rights.

ENHANCED AUTHORIZATION FOR SECURE MANAGEMENT OF SENSITIVE DATA IN HYBRID APPLICATIONS

Sensitive data is often managed by cloud-based applications, which can be vulnerable to attackers who seek unauthorized access to this data. Traditional approaches to authorization may not be sufficient to protect sensitive data from such attacks. In this article, we propose an enhanced authorization approach that uses a combination of symmetric and asymmetric cryptography to secure sensitive data. Specifically, we propose generating a unique encryption key per file and a set of public and private keys per user, which are used to encrypt and decrypt the data. We demonstrate the feasibility of our approach with examples in Node.js, showing how to generate public and private keys, encrypt and decrypt files, and store encrypted data on a drive. Our approach provides an effective solution to the problem of managing sensitive data in hybrid applications, while preserving user and developer convenience.

Failure Propagation Prediction of Complex Electromechanical Systems Based on Interdependence

Interdependence is an inherent feature of the cyber-physical system. Small damage to one component in the system may affect several other components, leading to a series of failures, thus collapsing the entire system. Therefore, the system failure is often caused by the failure of one or more components. In order to solve this problem, this paper focuses on a failure propagation probability prediction method for complex electromechanical systems, considering component states and dependencies between components. Firstly, the key component set in the system is determined based on the reliability measure. Considering the three coupling mechanisms of mechanical, electrical, and information, a topology network model of the system is constructed. Secondly, based on the topology network model and fault data, the calculation method of influence degree between components is proposed. Three state parameters are used to express the risk point state of each component in the system through mathematical representation, and the correlation coefficient between the risk point state parameters is calculated and measured based on the uncertainty evaluation. Then, the influence matrix between the system risk points is constructed, and the fault sequence is predicted by using the prediction function of an Artificial Neural Network (ANN) to obtain the fault propagation probability. Finally, the method is applied to the rail train braking system, which verifies that the proposed method is feasible and effective.

Integrated LC/MS-based lipidomics and transcriptomics analyses revealed lipid composition heterogeneity between pectoralis intramuscular fat and abdominal fat and its regulatory mechanism in chicken

Intramuscular fat (IMF) content is conducive to multiple meat quality properties, while abdominal fat (AF) is treated as waste product in chicken industry. However, the heterogeneity and distinct regulatory mechanisms of lipid composition between the IMF and AF are still unclear. In this study, we carried out non-targeted lipidomics analyses of pectoralis IMF and AF, and detected a total of 423 differential lipid molecules (DLMs) between chicken IMF and AF, including 307 up-regulated and 116 down-regulated DLMs in pectoral IMF. These DLMs exhibited the definite alteration of lipid composition. The up-reglated DLMs in IMF were mainly glycerophospholipids (GPs), including the bulk of phosphatidylcholines (PC, PC (P) and PC (O)), phosphatidylethanolamines (PE, PE (P) and PE (O)), phosphatidylglycerols (PG) and phosphatidylinositol (PI), while the up-reglated DLMs in AF were mainly glycerolipids (GLs), including most of triacylglycerols (TG) and diacylglycerols (DG). We further identified 28 main DLMs contributing to the heterogeneous deposition of IMF and AF, including 11 TGs common to IMF and AF, 12 PCs/PC (P)s specific to IMF and 5 DGs specific to AF. Further integration of transcriptome with the main DLMs by weighted gene co-expression network analysis (WGCNA), we found five key gene sets that included 386 unique genes promoting IMF deposition in pectoralis, 213 unique genes promoting AF deposition, 6 unique genes detrimental to AF deposition, 7 common genes that promote IMF deposition in pectoralis while adversely affect AF deposition, and 28 genes that only promoted IMF deposition in pectoralis but had no effect on AF deposition. In addition, we also observed the expression characteristics of key genes in vivo and in vitro, and found that transmembrane protein family gene TMEM164 might be mainly involved in the positive regulation of intramuscular fat deposition in pectoralis and zinc finger protein family gene ZNF488 had a potential unique positive regulatory function on abdominal fat deposition. These findings provide new perspectives for understanding IMF and AF heterodeposition and will serve as a valuable information resource for improving meat quality via breeding selection in chicken.

When fake news infects political networks: case study of the Tallano gold myth in the Philippines

Mis-/disinformation has increasingly become a global threat to democratic societies, creating distrust in institutions, fomenting deep societal divisions, and disrupting democratic elections. To complement earlier studies on the Philippines, this paper develops a case study approach to analyze one specific false narrative that went “viral” prior to and during the Philippine Presidential elections in 2022. Specifically, this paper examines the Tallano gold myth, using an empirical analysis of an extensive social media dataset of almost 24,000 social media posts compiled using CrowdTangle. Three key sets of messages appear central to the myth: (1) Marcos gold is critical for economic revival; (2) Marcos’ wealth is legitimate; and (3) the opposition wants to steal the gold. This paper finds evidence that the Tallano gold myth spread across partisan groups prior to the 2022 Presidential elections, proving difficult to overcome with mere fact-checking efforts. A final section concludes by briefly reviewing various country responses to fake news, and outlining possible policy responses with an eye to their possible timing in the virality pattern. Lessons from this case study emphasize the need to catch fake news in time to stop them from reaching virality and generating large adverse impacts on society.

Evaluating deep generative models on cognitive tasks: a case study

We present a detailed case study evaluating selective cognitive abilities (decision making and spatial reasoning) of two recently released generative transformer models, ChatGPT and DALL-E 2. Input prompts were constructed following neutral a priori guidelines, rather than adversarial intent. Post hoc qualitative analysis of the outputs shows that DALL-E 2 is able to generate at least one correct image for each spatial reasoning prompt, but most images generated are incorrect, even though the model seems to have a clear understanding of the objects mentioned in the prompt. Similarly, in evaluating ChatGPT on the rationality axioms developed under the classical Von Neumann-Morgenstern utility theorem, we find that, although it demonstrates some level of rational decision-making, many of its decisions violate at least one of the axioms even under reasonable constructions of preferences, bets, and decision-making prompts. ChatGPT’s outputs on such problems generally tended to be unpredictable: even as it made irrational decisions (or employed an incorrect reasoning process) for some simpler decision-making problems, it was able to draw correct conclusions for more complex bet structures. We briefly comment on the nuances and challenges involved in scaling up such a ‘cognitive’ evaluation or conducting it with a closed set of answer keys (‘ground truth’), given that these models are inherently generative and open-ended in responding to prompts.

Optimal Threshold-Based High Impedance Arc Fault Detection Approach for Renewable Penetrated Distribution System

Detection of high impedance arcing faults (HIAFs) in an active distribution network is challenging due to limited fault current contribution from power electronic interfaced distributed energy resources. It may also happen that during the switching operation of any system, component like a transformer, capacitor bank, nonlinear load, and lines, the protection function may fail to maintain security. The secure operation of any detection algorithm depends on the proper selection of key parameters and threshold settings. To enhance the HIAF detection process, the residual current is preprocessed using OTET. The cumulative sum of the fault detection index from the energy coefficients of OTET is used to take a suitable decision. Optimal parameters of the basis function in OTET are obtained using particle swarm optimization algorithm with a wide variety of fault and no-fault datasets. Further, the optimal value of a drift parameter is designed using OTET investigations and utilized in the fault detection index calculation. The selection mechanism incorporated in the method increases both dependability and security. It can be used for grid-connected/islanded modes of the system, different switching events, and grounding connections. The method is also reliable for noise and harmonic contaminated signal and unbalanced system.

Bit‐level evaluation of piccolo block cipher by satisfiability problem solver

AbstractIn the field of symmetric key cryptography, the security against distinguishing attacks is one of the crucial security requirements. With advancements in computing capabilities and cryptanalysis techniques in recent years, more efficient methods have been proposed for exploring distinguishers using Mixed‐Integer Linear Programing (MILP) or satisfiability problem (SAT), thereby updating the security bounds of various ciphers. Piccolo is a lightweight block cipher proposed at CHES in 2011, with support 80‐bit and 128‐bit keys. Designers have undergone a rough security evaluation against differential, impossible differential, and related‐key differential attacks, based on nibble‐wise estimations due to the limitation of computational resource. Here, the authors perform bit‐level evaluations on Piccolo block cipher against differential, integral and impossible differential attacks by leveraging SAT‐based approaches. For the first time, the authors succeed in identifying optimal differential distinguisher on 6 rounds in the single key setting, and on 10/12 rounds in the related‐key setting for 80‐bit and 128‐bit keys, respectively. For integral attacks, the authors find integral distinguisher up to 7 rounds. Although the number of attacked rounds is the same as that of the previous attack, the authors find the 56th ordered integral distinguisher, which enable reducing the data complexity for attacks from 263 to 256. As a result, the authors find the 7‐round impossible differentials which is the same number of rounds as the previous nibble‐wise evaluation.

A scalable school-based intervention to increase early adolescents' motor competence and health-related fitness.

Schools are key settings for the promotion of students' physical activity, fitness, and motor competence. The purpose of our study was to investigate the efficacy of a 5-month-long intervention program that aimed to increase students' motor competence and health-related fitness during school days. We conducted a quasi-experimental study with 325 Finnish Grade 5 (Mage = 11.26, SD = 0.33) students from five schools. Two schools were allocated to the intervention group and three schools to the control group. The intervention consisted of three components: (a) weekly 20 min session during regular PE lessons, (b) weekly 20 min session during recess, and (c) daily 5-minute-long classroom activity breaks. All activities were designed to systematically develop different elements of motor competence and fitness. The following assessments were conducted at baseline and 5-months: cardiorespiratory fitness levels were measured by 20-meter shuttle run test, muscular fitness by curl-up and push-up tests, and motor competence by 5-leaps and throwing-catching combination tests. We analyzed the data using a multi-group latent change score modeling. Results showed that students in the intervention group developed significantly better in 20-meter shuttle run test (β = 0.269, p = 0.000, 95% CI [0.141, 0.397]; +5.0 laps), push-up (β = 0.442, p = 0.000, 95% CI [0.267, 0.617]; +6.5 repetitions), curl-up (β = 0.353, p = 0.001, 95% CI [0.154, 0.552]; +7.8 repetitions), and throwing-catching combination tests (β = 0.195, p = 0.019, 95% CI [0.033, 0.356]; +1.1 repetitions) than students in the control group. The intervention program appeared to be feasible and effective in increasing students' cardiorespiratory fitness, muscular fitness, and object control skills. This indicates that guided school-based physical activity programs can be influential in promoting physical fitness and motor competence among early adolescent students.

Sevoflurane Exposure in Neonates Perturbs the Expression Patterns of Specific Genes That May Underly the Observed Learning and Memory Deficits.

Exposure to commonly used anesthetics leads to neurotoxic effects in animal models-ranging from cell death to learning and memory deficits. These neurotoxic effects invoke a variety of molecular pathways, exerting either immediate or long-term effects at the cellular and behavioural levels. However, little is known about the gene expression changes following early neonatal exposure to these anesthetic agents. We report here on the effects of sevoflurane, a commonly used inhalational anesthetic, on learning and memory and identify a key set of genes that may likely be involved in the observed behavioural deficits. Specifically, we demonstrate that sevoflurane exposure in postnatal day 7 (P7) rat pups results in subtle, but distinct, memory deficits in the adult animals that have not been reported previously. Interestingly, when given intraperitoneally, pre-treatment with dexmedetomidine (DEX) could only prevent sevoflurane-induced anxiety in open field testing. To identify genes that may have been altered in the neonatal rats after sevoflurane and DEX exposure, specifically those impacting cellular viability, learning, and memory, we conducted an extensive Nanostring study examining over 770 genes. We found differential changes in the gene expression levels after exposure to both agents. A number of the perturbed genes found in this study have previously been implicated in synaptic transmission, plasticity, neurogenesis, apoptosis, myelination, and learning and memory. Our data thus demonstrate that subtle, albeit long-term, changes observed in an adult animal's learning and memory after neonatal anesthetic exposure may likely involve perturbation of specific gene expression patterns.

Contact pattern expansion regulation model for spiral bevel gear transmission

Focusing on digital assessment and optimization for time-varying contact pattern, an innovative contact pattern expansion (CPE) regulation model is established for spiral bevel gear transmission. By building a parametric optimization design bridge between loaded contact pattern and geometric boundary, the proposed method can get an accurate and effective forecasting, control and decision-marking of geometric topography and contact performances. Firstly, in order to reflect the influence of misalignment on CPE, an improved TCA solution method is developed for loaded contact pattern determination. Considering influence of tooth surface geometric boundary, accurate parametric CPE definition and assessment are proposed. Then, CPE regulation model is established by providing tooth flank deflection method. Where, by optimizing the regulation amounts of each assessment items, it can get an accurate and effective CPE regulation. In order to establish the parametric relations between regulation targets and deflection surface, an innovative TCA-oriented prescribed surface expression is developed. Levenberg-Marquardt method is used to solve the established model for the machining setting parameters. Finally, the proposed model is exercised with an example face-milled spiral bevel gear set from an aerospace engine application to demonstrate the impact of the key machine settings on the result CPE regulation.

Informing Diet and Physical Activity Interventions with Family Involvement in an Urban Setting: Views of Children and Adults in Lagos, Nigeria

There is limited evidence on diet and physical activity (PA) interventions to prevent childhood over- and under-nutrition in Nigeria, particularly those with parental involvement. The aim of this qualitative cross-sectional study was to explore parents’, children’s, and other stakeholders’ views on what might enable or hinder participation in diet and PA interventions with parental involvement, and potential intervention components likely to be feasible and acceptable. The study took place in Lagos State, Nigeria. Sixteen boys and girls aged 8–11 years in four participating schools were involved in the study. In addition, women (n = 19) and men (n = 13) aged 18–60 years took part. These included parents, teachers, school heads, community leaders, health professionals, and health or education civil servants, recruited via the participating schools, community and professional links. Data were collected in schools, homes, and places of work via one-to-one interviews and focus groups, audio-recorded with participants’ consent. Topics included knowledge about a balanced diet and active living; frequency of exercise and barriers and facilitators of participation; and ideas for fostering good diet and physical activity habits. Socio-demographic data were collected orally at the start of each discussion. Thematic analysis of verbatim transcripts of the recordings was conducted. Three overarching themes were identified: (1) Active community collaboration; (2) strategies for involving families; and (3) schools as key settings for interventions. The main barriers to participation in potential interventions included poor access to clean water, safe outdoor play space, and menstrual hygiene products. Suggested solutions included increased security, infrastructure improvements using recycled materials, school gardening, and health campaigns funded by local dignitaries. Health literacy education for parents, tailored to local dialects, was considered an important potential intervention component that would engage families. The feasibility and acceptability of grassroots suggestions for intervention components and parental involvement could usefully be explored in future pilot studies.