The study, conducted by Bomi Jin and Lonn Lanza-Kaduce of the Department of Sociology and Criminology & Law at the University of Florida, is published in the American Journal of Criminal Justice. Rather than treating schools simply as protective institutions that supervise young people, the authors reframe them as structured social environments that organize adolescents’ face-to-face interactions — including interactions with marijuana-using peers. When schools closed, that organizing structure dissolved; when they reopened, it snapped back, with measurable consequences for the transmission of substance use behavior through peer networks.

The theoretical foundation draws on Akers’ social structure and social learning framework, an extension of Edwin Sutherland’s classic differential association theory. In its simplest form, differential association holds that criminal and deviant behavior is learned through interaction with intimate personal groups, where individuals acquire not only the techniques of deviance but also the attitudes, definitions, and reinforcement patterns that sustain it. Social learning theory, developed by Ronald Akers and colleagues, added mechanisms of differential reinforcement, imitation, and definitions favorable or unfavorable to law-violating behavior. Meta-analytic reviews have consistently ranked peer associations among the strongest predictors of adolescent delinquency and substance use. What the structural branch of the theory adds is the insight that macro-level and organizational contexts — neighborhoods, communities, and schools — pattern the frequency, duration, priority, and intensity of those associations.

Jin and Lanza-Kaduce seized on a rare analytical opportunity embedded in the pandemic. Because Florida school districts resumed in-person instruction at different points during the 2020–2021 academic year, students across the state faced sharply different levels of in-person peer contact at the same moment in time. The researchers used this variation in school return timing as a proxy for the degree of face-to-face interaction adolescents had with their peers. Students in early-returning districts were reimmersed in hallways, classrooms, lunchrooms, and after-school spaces where friendships are enacted bodily — through conversation, shared routines, and what interactionist scholars call interaction rituals — while late returnees remained more dependent on digital communication, which prior research suggests carries weaker or at least different socialization signals than physical co-presence.

The empirical analysis drew on statewide survey data from Florida middle and high school students in grades 6 through 12, with an analytical sample of 7,656 adolescents. The data came from the Florida Youth Substance Abuse Survey, an instrument designed to measure risk and protective factors for adolescent substance use and problem behaviors. The outcome of interest was adolescents’ own marijuana use, and the central explanatory variable was friends’ marijuana use, alongside the school return timing that distinguished early returnees from later ones. The models also controlled for established individual and family-level predictors, including low self-control, measured through items adapted from Grasmick and colleagues’ widely used scale, and parental monitoring, with full item wording provided in the study’s online supplement.

Methodologically, the study confronted several challenges familiar to survey researchers. Missing data were substantial and, importantly, not missing completely at random: additional analyses following the approach of Meldrum and colleagues revealed significant mean differences on study variables between cases with and without missing responses, undermining the assumption of randomness. The authors therefore employed multiple imputation using chained equations, a modern technique that preserves uncertainty while filling in missing values based on observed relationships. Because marijuana use in the sample was a relatively low-prevalence behavior, which can bias maximum-likelihood estimates in logistic regression, the researchers also ran penalized Firth logistic regressions, a technique designed to stabilize estimates for rare events; the substantive conclusions were unchanged. To interpret the interaction between school return timing and friends’ use in a nonlinear probability model, the study drew on contemporary best practices for presenting and testing interaction effects in generalized linear models.

The results split cleanly into two parts. Evidence for mediation — the idea that returning to school earlier indirectly increases marijuana use by increasing friends’ marijuana use — was, in the authors’ words, limited. The structural timing of school reopening did not simply manufacture new marijuana-using friendships that then produced use. Instead, the striking finding was moderation: the association between friends’ marijuana use and adolescents’ own use was substantially stronger among early returnees than among later returnees. In other words, having marijuana-using friends predicted a student’s own marijuana use much more powerfully when those friendships were being enacted in person at school. The effect was particularly pronounced among adolescents with multiple marijuana-using friends, suggesting a dose-response pattern in which exposure to several using peers, combined with daily physical co-presence, created the strongest gravitational pull toward use.

These findings carry weight for how scientists understand the mechanics of peer influence. Social learning researchers have long distinguished between what peers think and what peers do, and laboratory and field work on ‘deviancy training’ has shown that adolescent friendships can become engines of reinforcement for rule-breaking talk and behavior. The Florida results imply that this training accelerates in the environments where it can be rehearsed and rewarded most consistently — the structured but only partially supervised social spaces of the school day and its surrounding routines. They also align with research on unstructured socializing, which finds that unsupervised time with peers is a robust situational trigger for delinquency, and with studies showing that school-adjacent settings shape patterns of adolescent alcohol and marijuana use. The virtual substitutes adolescents relied on during remote schooling — messaging, social media, gaming — appear to have sustained friendships but not to have replicated the intensity of in-person reinforcement, consistent with emerging evidence that online and offline peer influences operate differently.

The study is not without limitations, and the authors are candid about them. The data are cross-sectional, which constrains causal inference; perceived friends’ use may partly reflect projection, the well-documented tendency of adolescents to assume their friends behave as they do. The marijuana measure did not separately capture edible forms of the drug. And because the sample is statewide rather than nationally representative, generalization requires caution. Nevertheless, the pandemic-generated variation in school return timing offers a form of leverage that ordinary survey designs rarely achieve, and the consistency of the moderation result across estimation strategies strengthens confidence in the core conclusion.

The practical implications reach beyond criminology into public health and school policy. Prevention programs have traditionally targeted individual adolescents — educating them about drug risks, building refusal skills, strengthening self-control. The Florida study suggests that such individual-level interventions, while valuable, miss a structural layer: the social settings through which marijuana-using friends exert their influence. Schools, in this framing, are not merely backdrops but active organizers of peer exposure. Prevention efforts, the authors argue, may benefit from extending into the social environments where in-person interactions with marijuana-using peers occur — the rhythms of the school day, the transitions between classes, the informal gatherings before and after instruction — with particular attention to students who report multiple friends who use marijuana. As districts worldwide continue to weigh the costs and benefits of remote, hybrid, and in-person instruction, the study adds an unexpected variable to the calculus: the timing of a school’s reopening may shape not only learning loss and mental health, but the very social channels through which adolescent substance use spreads.

Subject of Research: How variation in school return timing during the COVID-19 pandemic moderated the association between deviant peer association and adolescent marijuana use.

Article Title: Structural Context, Deviant Peer Association, and Adolescent Marijuana Use: Evidence from Variation in School Return Timing during the Pandemic

Article References: Jin, B., & Lanza-Kaduce, L. (2026). Structural Context, Deviant Peer Association, and Adolescent Marijuana Use: Evidence from Variation in School Return Timing during the Pandemic. American Journal of Criminal Justice. https://doi.org/10.1007/s12103-026-09947-7

Image Credits: AI Generated

DOI: 10.1007/s12103-026-09947-7

Keywords: adolescent marijuana use, peer influence, differential association, social learning theory, school return timing, COVID-19, Florida Youth Substance Abuse Survey, criminology, substance use prevention, deviant peers, Structural, Context

The formal setting of the problem involves a positive operator-valued measure, or POVM, a collection of positive semidefinite operators that sum to the identity operator. Each POVM element corresponds to a possible guess: when a measurement is performed on a system prepared in one of the candidate states, the outcome indicates which state the experimenter concludes was present. The goal of minimum-error discrimination is to choose the POVM that maximizes the average probability of guessing correctly, given the a priori probabilities of the states. Mathematically, this optimization is an instance of semidefinite programming, a class of convex optimization problems that can, in principle, be solved to arbitrary precision. Yet semidefinite programs scale poorly as the number of states and the dimension of the underlying Hilbert space grow, which motivates the search for structural shortcuts that bypass the full optimization.

The first structural insight revisited in the work concerns single-qubit pure states. For ensembles of such states, the authors confirm that the matrix of pairwise fidelities, the magnitudes of the inner products between the states weighted by their probabilities, fully determines the optimal discrimination probability. The proof relies on the geometry of the Bloch sphere: each qubit pure state corresponds to a unit vector in three-dimensional real space, and pairwise fidelities fix all inner products between these Bloch vectors. A classical result of matrix analysis then guarantees that the whole configuration of Bloch vectors is fixed up to an orthogonal transformation. In three dimensions, every rotation can be realized by a unitary operator on the qubit Hilbert space, and reflections can be absorbed by an additional transposition trick applied to both states and measurement operators. Consequently, ensembles related by such transformations share identical optimal success probabilities, and pairwise fidelities alone suffice to pin the answer down.

Remarkably, this tidy correspondence collapses in higher dimensions. For Hilbert spaces of dimension greater than two, the generalized Bloch vectors live in spaces of dimension d squared minus one, and not every orthogonal transformation of those vectors corresponds to a physically allowed unitary on the system. Starting from a counterexample with three states in a three-dimensional Hilbert space, the authors show that the failure propagates to all higher dimensions and larger ensembles. In other words, knowing only how similar each pair of states is, in the fidelity sense, is no longer enough to determine how well they can be distinguished; phase information and richer structural detail become essential. This dimensional divide sharpens our understanding of when reduced descriptions of quantum ensembles can legitimately stand in for the full state data.

As an illustration of the single-qubit result, the paper derives a closed-form expression for the optimal discrimination probability of three equiprobable pure qubit states with equal pairwise fidelities, a condition the authors call equal fidelity distance, weaker than the conventional requirement of equidistance in the complex Gram matrix. The ensemble turns out to be geometrically uniform, meaning its states are related by a symmetry operation, a rotation of the Bloch sphere by 120 degrees about a suitable axis. For geometrically uniform states, the optimal measurement is known to be the pretty good measurement, or PGM, a canonical strategy constructed from the square root of the ensemble density matrix. Carrying out the algebra with an explicit formula for the square root of a two-by-two matrix, the authors find that the optimal success probability equals one third plus a term proportional to the square root of one minus the squared pairwise fidelity, tracing an arc of an ellipse as the fidelity parameter varies. The result is a rare example of a fully fidelity-based closed form for a multi-state discrimination problem.

The second major thread of the work concerns sparsity in the optimal measurement. Sometimes the optimal POVM assigns a zero operator to one or more states, effectively giving up on identifying them and devoting the full measurement resources to the remaining, more probable candidates. Mirror-symmetric qubit states provide a classic example: when the outer states are sufficiently probable relative to the middle one, the optimal measurement simply distinguishes the two outer states and ignores the third. Cha and Lee formalize this by defining the set of indices whose optimal POVM elements are nonzero, and they prove a scaling relation: once this set is known, the original discrimination problem reduces to a smaller problem on the surviving states, with the optimal success probability multiplied by the total prior probability of those states. This reduction is the key that unlocks tighter performance bounds.

The practical payoff comes in the form of refined upper bounds on the optimal success probability. A well-known bound due to Renes relates the optimum to the PGM success probability, and the authors show that applying this bound to the reduced ensemble of nonvanishing states often yields a strictly tighter estimate. The improvement is not universal, and the authors are careful to document this: for three mirror-symmetric qubit states there exists a small region of parameters where the refined bound is actually weaker, and numerical experiments over random pure and mixed ensembles on up to five qubits confirm that the refined bound dominates more often as the number of qubits or states grows, approaching universal superiority in the largest configurations tested. For equiprobable states the authors conjecture, supported by extensive numerics and partial analysis, that the refined bound never loses, and they verify this conjecture analytically in the simplest nontrivial setting.

Beyond the PGM-based bound, the paper extends the same sparsity-aware reduction to three other upper bounds drawn from the literature, including a fidelity-sum bound, a bound involving the trace norm of the square root of the summed squared weighted density matrices, and a trace-distance bound. For the latter two, the authors prove that the reduced versions are always at least as tight as the originals, with the trace-norm case following elegantly from the operator monotonicity of the square root function, the Löwner-Heinz inequality. For the fidelity-sum bound with equiprobable states, a direct combinatorial argument shows the reduced bound is always tighter as well. Together these results demonstrate that partial knowledge about which measurement operators vanish is a broadly useful resource for bounding discrimination performance without solving any optimization problem at all.

Perhaps most intriguingly, the authors show that such partial knowledge can often be obtained cheaply. They present a necessary condition certifying that a given state must receive a nonzero optimal operator, based on the overlap of supports of pairwise difference matrices, and a sufficient condition certifying that an operator must vanish, based on expressing one state as dominated by a convex combination of the others. In numerical experiments on ten thousand randomly generated three-state qubit problems, these two tests coincided in roughly half of the instances, uniquely identifying the full set of nonvanishing operators without ever running a semidefinite program. Since unions of certified subsets and intersections of certified supersets remain valid, the framework offers a compositional route to narrowing down the optimal measurement structure. The authors conclude that determining the vanishing pattern of the optimal POVM efficiently, whether analytically or heuristically, remains an open challenge, but one whose solution could substantially reduce the computational cost of optimal quantum measurements, with implications for quantum communication, sensing, and the readout of quantum information in any technology where nonorthogonal states must be told apart at the limits allowed by physics.

Subject of Research: Structural properties of quantum states and measurements in optimal quantum state discrimination

Article Title: Structural perspectives from quantum states and measurements in optimal state discrimination

Article References: Cha, H., & Lee, J. (2026). Structural perspectives from quantum states and measurements in optimal state discrimination. Quantum Information Processing, 25(9), Article 310. https://doi.org/10.1007/s11128-026-05335-6

Image Credits: AI Generated

DOI: 10.1007/s11128-026-05335-6

Keywords: quantum state discrimination, minimum-error discrimination, positive operator-valued measure, pretty good measurement, pairwise fidelity, semidefinite programming, Bloch vector, sparsity, upper bounds, geometrically uniform states, quantum information theory, Structural

A solar sensor is an important part of a spacecraft attitude-determination system. By measuring the direction of incoming sunlight, it provides a reference that flight computers can use to estimate the spacecraft’s orientation. That information supports functions such as pointing instruments, managing communications and directing solar panels toward illumination. In a conventional sensor, the Sun’s rays interact with a detector through an aperture, slit or shaped optical element. The resulting signal changes as the spacecraft rotates, allowing the angle of the Sun relative to the sensor to be calculated. A wide field of view is valuable because a spacecraft may emerge from an eclipse, tumble after deployment or operate while its attitude changes substantially. If the Sun lies outside the sensor’s useful angular range, the instrument may temporarily lose its reference. The pyramidal design addresses this limitation by distributing sensitive units across multiple faces. Light arriving from different directions can therefore illuminate different detector elements, producing signals that encode both azimuth, the horizontal angle, and elevation, the vertical angle. Together, these measurements define the Sun’s position in the sensor’s coordinate system.

The study’s sensor uses OPT101 sensitive elements and associated electronics to convert incident light into measurable electrical signals. The researchers tested the sensitive units and their electronics through several processes rather than treating the detector as an isolated component. This system-level approach matters because the accuracy of a solar sensor depends not only on the geometry of its housing, but also on detector response, electronic noise, signal conditioning and the algorithms used to interpret the measurements. Photodetectors do not always produce perfectly clean or linear outputs, particularly when measurements are affected by noise or changing illumination conditions. To improve the quality of the readings, the team applied a novel digital filtering algorithm. Digital filtering processes a sampled signal mathematically, suppressing unwanted fluctuations while retaining the information associated with the Sun’s direction. Better signal quality can make the transition between angular measurements more stable and reduce the risk that random variations will be mistaken for a change in spacecraft orientation. The article reports that the filtering successfully enhanced the sensor signal, although the available source does not specify a single numerical improvement in accuracy attributable only to the algorithm.

The pyramidal geometry also provides a practical optical strategy. Instead of relying on one detector and one viewing path, the arrangement allows several sensitive areas to observe different portions of the surrounding sky. As the angle of incoming sunlight changes, the relative responses of the units change as well. Comparing those responses provides the basis for estimating the Sun’s azimuth and elevation. In principle, a multi-face arrangement can maintain useful sensitivity over a larger angular range than a flat, single-face detector. It can also supply directional information without requiring a mechanically moving optical assembly, which helps simplify the design. The source article identifies wide field-of-view performance as a key advantage of the proposed configuration and states that experiments involving azimuth and elevation confirmed this behavior. The researchers also compared the device with other existing technologies and found accuracy comparable to commercial solar sensors. That comparison is important for small spacecraft, where a lower purchase and manufacturing cost is useful only if the instrument still provides sufficiently dependable orientation data for the mission’s control system.

Because the instrument is intended for space, its optical performance is only part of the engineering challenge. A sensor housing must withstand the mechanical stresses associated with launch, including vibration and shock, without allowing the detector geometry to shift. Even a small deformation could alter the relationship between the pyramid faces and the sensitive units, introducing a pointing error that software alone might not correct. The researchers therefore conducted a structural assessment of the proposed design using candidate materials and analysis of the mechanical behavior. Their results identified aluminum as the best material choice for the structure. Aluminum is widely used in spacecraft hardware because it combines relatively low density with useful strength and established manufacturing practices, although the study’s conclusion is specific to the analyzed sensor configuration. Structural analysis can reveal how a component responds to applied loads, where stresses concentrate and whether displacement remains within acceptable limits. For a solar sensor, maintaining dimensional stability is especially important because the optical geometry is directly linked to the conversion of detector signals into angular coordinates.

The reported cost reduction reflects the project’s focus on accessibility as well as performance. Commercial space-qualified sensors can impose a significant burden on missions with limited budgets, while custom development can require specialized manufacturing and testing. A design based on comparatively accessible detector technology and a simple pyramidal mechanical structure may offer an alternative for universities, emerging space programs and small-satellite teams. The authors are affiliated with the University of Abdelhamid Ibn Badis in Mostaganem, the Algerian Space Agency and the National Polytechnic School of Oran Maurice Audin. Their work places the sensor within a broader effort to develop affordable spacecraft subsystems without abandoning formal engineering assessment. The approximately 60 percent cost reduction reported in the study is not presented as a universal price guarantee for every mission; actual costs would depend on production volume, qualification requirements, integration and procurement. Nevertheless, the result suggests that careful mechanical design and signal processing may reduce the trade-off between affordability and functional capability. For missions that need several attitude sensors for redundancy, or for projects operating under strict financial limits, that difference could be significant.

The prototype’s current status also highlights the gap between a successful laboratory or test-bench demonstration and a flight-ready space instrument. The paper reports testing of the sensitive units and electronics, structural analysis, and experiments measuring azimuth and elevation. It does not report environmental qualification or in-orbit validation as completed achievements. Space hardware must generally be evaluated against the conditions expected during launch and operation, which can include vibration, shock, thermal changes, vacuum and radiation exposure. Qualification testing is intended to show that the design can survive those conditions while continuing to meet its performance requirements. Calibration is another essential step: the relationship between detector output and Sun angle must be characterized, and that relationship may need to be checked after environmental testing. The authors identify environmental qualification testing and in-orbit validation as future work. Those stages will determine whether the demonstrated wide field of view, comparable accuracy and structural performance remain available in the operational environment. Until then, the sensor should be regarded as a promising development rather than a fully qualified replacement for established flight hardware.

The broader significance of the research lies in its integration of geometry, electronics, computation and structural engineering around a specific spacecraft need. A solar sensor does not have to be large or mechanically elaborate to provide useful attitude information, but it must produce interpretable signals across the directions relevant to its mission and remain stable under launch conditions. The pyramidal concept offers a way to expand coverage while using multiple fixed sensitive units, and the digital filter addresses the quality of the measurements produced by those units. The structural assessment adds evidence that the physical assembly can be built around aluminum without compromising the intended design. Together, these elements form a practical route toward a lower-cost sensor for small spacecraft. The next tests will be decisive: qualification will challenge the structure and electronics, while orbital validation will reveal how the instrument performs amid real sunlight, spacecraft motion and the changing conditions of space. If those evaluations confirm the study’s findings, the design could give more small-satellite missions access to wide-angle solar attitude sensing at a substantially lower cost.

For attitude determination, the sensor’s azimuth and elevation measurements are most useful when combined with a spacecraft’s other available information, such as a dynamical model or additional attitude sensors. A solar direction defines a line of reference, but by itself it does not generally distinguish every possible spacecraft orientation about that line. This makes the reported angular experiments relevant to system integration: they characterize how the pyramidal detector translates sunlight into coordinates that a flight computer can use alongside other measurements. The practical value of the wide field of view therefore depends not only on angular accuracy, but also on how reliably the sensor can provide a valid Sun vector during changing spacecraft attitudes.

The study also illustrates why validation must proceed in stages. Component and electronics tests can establish whether the photodetectors and readout produce usable signals, while azimuth and elevation experiments examine the measurement principle. Structural analysis addresses a different question: whether the physical assembly preserves its geometry under modeled loading. Environmental qualification and orbital validation would connect these separate results by testing the integrated instrument under mission-relevant conditions. The authors state that supporting data are available from the corresponding author upon reasonable request, which may allow further examination of the reported methods and results as development progresses.

Subject of Research: Low-cost pyramidal solar sensing for small-spacecraft attitude determination

Article Title: Design and Structural Assessment of a Low-Cost Wide Field-of-View Pyramidal Solar Sensor for Space Applications

Article References: Nehila, A., Teffah, K., Roubache, R., Slimane, S. A., Bennaceur, M. A., Adnane, A., Cheriet, M. E.-A., & Bensabri, O. (2026). Design and Structural Assessment of a Low-Cost Wide Field-of-View Pyramidal Solar Sensor for Space Applications. International Journal of Aeronautical and Space Sciences. https://doi.org/10.1007/s42405-026-01286-5

Image Credits: AI Generated

DOI: 10.1007/s42405-026-01286-5

Keywords: solar sensors, CubeSats, small satellites, spacecraft attitude, pyramidal structures, wide field of view, digital filtering, aluminum structures, finite element analysis, Design, Structural, Assessment