Let A be an n & times; n real Toeplitz matrix satisfying A + A(inverted perpendicular) = 2J(n), where J(n) is the all-ones matrix. If Ar(i, j) denotes the r & times; r contiguous submatrix of A consisting of rows i, i + 1, ... , i + r-1 and columns j, j + 1, ... , j + r-1, then for every n >= 2 one has det A(n)-1(1, 2) + det A(n)-1(2,1) = 2 det A(n)-1(1, 1). This confirms a conjecture of Charles R. Johnson (2003). The proof combines a rank-one determinant expansion with Dodgson's condensation formula, and then invokes a polynomial-identity argument in the Toeplitz parameters: after obtaining an equality of squares in the integral domain & Zopf;[b(1), ... , b(n-1)], we factor it to deduce an identity up to sign and determine the sign by a suitable specialization. We also give an extension of the Bayat-Teimoori arithmetic-geometric mean identity: for every real accretive matrix A, one has the sharp inequality root det A(n-1)(1, 1) det A(n-1)(2, 2) >= |det A(n-1)(1, 2) + det A(n-1)(2, 1)/2, with equality whenever the symmetric part has at most rank one, i.e. A + A(inverted perpendicular) = alpha ww(inverted perpendicular) for some alpha is an element of R and w is an element of R-n\ {0}, recovering the Bayat-Teimoori equality as a special case. (c) 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Spiking Neural Networks (SNNs) offer energy-efficient inference through event-driven computation, but their hardware implementation remains challenging because neuron-state updates require costly arithmetic and are sensitive to approximation errors. This paper presents ReSCom, a reconfigurable FPGA accelerator that combines stochastic multiplication with exact fixed-point addition, subtraction, and state accumulation. A unified neuron core supports Integrate-and-Fire (IF), Leaky Integrate-and-Fire (LIF), and Synaptic models, enabling runtime adaptation without duplicating hardware. ReSCom is evaluated on MNIST image classification and the Spiking Heidelberg Digits (SHD) auditory benchmark, which provides a more demanding temporally structured workload with 700 input channels. At a 16-bit stochastic precision, the LIF configuration achieves 95.52% accuracy on MNIST and 71.09% on SHD, with operational energies of 0.1868 mJ and 0.1674 mJ per sample, respectively. A bit-stream study from 2 to 1024 bits shows that moderate stream lengths substantially improve the accuracy of stateful neuron models, whereas very long streams increase energy with limited additional benefit. These results demonstrate that ReSCom enables configurable accuracy–energy trade-offs across both visual and auditory SNN workloads.
Dynamic electricity pricing is widely advocated for demand-side management, yet its distributional consequences under crisis conditions remain poorly understood. We examine the equity implications of hourly spot pricing during the European energy crisis using metered consumption from 1136 Norwegian households (October 2020–March 2022) linked to socio-demographic surveys. Southern Norwegian wholesale prices nearly tripled (+192%), and mean household electricity costs rose from 484 to 1785 NOK/month. Energy poverty emerged from a zero baseline: no household exceeded the conventional 10% income-burden threshold before the crisis, versus 1.2% during it, and 6.8% exceeded a 6% burden. Impacts were steeply regressive: the mean burden of the lowest-income group reached 7.4%, against 1.9% for the highest — a fourfold gap. SHAP analysis shows that housing characteristics dominate income and price as predictors of consumption, indicating that vulnerability is structural, rooted in the building stock, rather than purely economic. We term this the vulnerability-responsiveness paradox: the households facing the greatest welfare exposure have the least structural capacity to adjust. Counterfactual simulations show that a 1.0 NOK/kWh price cap cuts poverty to 0.3%, and that pairing the cap with targeted retrofits of high-consumption detached homes performs best across cost, poverty, and inequality simultaneously. Crisis pricing policy should combine short-term price caps with structural building-efficiency programmes.
Background: Iranian herbal medicines have significant export potential to the Russian market, yet face structural barriers including information asymmetry, lack of digital trust, and regulatory complexities.Aims: This study evaluates the role of electronic platforms in overcoming these challenges.Methodology: Using a mixed-methods approach, data were collected from 36 stakeholders via a structured questionnaire and from 14 experts through in-depth interviews.Findings: Statistical analysis using SPSS revealed that while Russian market demand is high (mean= 4.1/5), traditional export processes remain inefficient due to legal barriers (mean= 2.9/5) and weak after‑sales services (mean= 2.44/5). The correlation between platform adoption and export success is strong and positive (r= 0.72, P< 0.001), while legal barriers have a significant negative relationship with success (r= -0.45). Digital trust (r= 0.65) and after‑sales support (r= 0.61) are also key predictors. Based on expert estimates, digital platforms can reduce buyer discovery time by ~85%, intermediation costs by ~60%, and customs clearance time by ~70%. Also, findings indicate that in the Iran‑Russia context, alignment of digital standards and document transparency are primary prerequisites for building trust. Contrary to expectations, simple and well‑integrated platforms are more effective than complex technologies like blockchain at the current stage.Conclusion: The study concludes that transitioning from traditional methods to an integrated digital ecosystem is not merely an option but a strategic necessity for sustainable export development.
Young novice drivers remain overrepresented in serious-injury crashes worldwide, yet most evidence guiding safety policy is derived from single jurisdictions, limiting understanding of whether injury-severity determinants and related policy insights are transferable across different regulatory and operating contexts. This study examines the contextual stability of young novice driver injury-severity relationships by comparing crashes involving drivers aged 18-25 across three cities with contrasting licensing systems, enforcement regimes and speed environments: London (UK), Philadelphia (USA), and Melbourne (Australia).Using police-reported crash data from 2022 to 2023, city-specific random parameters logit models with heterogeneity in means are estimated to capture unobserved variation in injury-severity outcomes. Pairwise likelihood-ratio tests are then used to assess the cross-jurisdiction stability of the city-specific model specifications. The tests reject model-level transferability across all city pairs, indicating substantial cross-context instability in the estimated injury-severity relationships. At the variable level, only a small number of determinants show full empirical consistency across all three cities. Female drivers are consistently associated with a lower probability of severe injury, whereas weekend crashes are consistently associated with a higher probability. By contrast, adverse weather and first-point-of-impact variables are significant across jurisdictions but exhibit differences in effect direction, while lighting conditions, speed environments, vehicle age, and traffic-control characteristics show additional city-specific variation.These findings suggest that some injury-severity relationships are more consistent across contexts, whereas others vary substantially across cities and therefore warrant local validation. Overall, the findings caution against assuming that injury-severity evidence and the policy responses informed by that evidence can be transferred directly across jurisdictions without local validation.
