Search Results (26,204)

Photonic computing systems require large numbers of accurate programmable voltages for photonic weight programming and device bias control. This paper presents a 128−channel digital−to−analog converter (DAC) implemented in a 250 nm BCD high−voltage CMOS process. A code−dependent per−channel auxiliary−DAC calibration scheme is proposed to compensate main−DAC conversion errors and channel−dependent offsets. In addition, a separated low−/high−voltage−domain driver and a stepwise multichannel update scheme are adopted to reduce static power and suppress update−induced disturbances. After calibration, the measured maximum absolute differential non−linearity (DNL) and integral non−linearity (INL) are 0.26 least significant bit (LSB) and 0.39 LSB, respectively, and the maximum deviation of voltage output (DVO) across 128 channels is 0.71 LSB. The DAC achieves rising/falling slew rates of 6.1/11.7 V/μs under an 8 V output swing. Under dynamic operation with 0.2 to 8.2 V sinusoidal outputs and a 10 kΩ load per channel, the total power consumption is 0.85 W. Thermo−optic phase−shifter measurements further verify programmable photonic phase tuning, demonstrating a scalable electrical control interface for thermo−optic phase−shifter−based photonic computing hardware. Full article

Aiming at the problems of high energy consumption, high carbon emissions from data centers and the difficulty of renewable energy accommodation in distribution networks driven by rapid growth in computing tasks, this paper proposes a collaborative trading method for data center–power grid–energy storage systems to improve spatiotemporal flexibility. Firstly, an integrated mechanism model including IT equipment, HVAC cooling systems, delay-tolerant batch tasks and UPS energy storage is established to quantify multi-dimensional internal flexible regulation potential. Secondly, an improved k-means algorithm is adopted for scenario reduction of wind–PV outputs, and a stochastic-robust collaborative trading optimization model considering carbon emission cost is constructed. Multiple practical constraints are incorporated, including power balance, power flow limits, nodal voltage bounds, task service latency and state of charge limits of energy storage. An improved particle swarm optimization with premature-convergence indicator is developed to solve this nonlinear, non-convex, mixed-variable problem. Simulations are carried out on a modified IEEE 33-node test system over a 24 h scheduling horizon. Numerical results demonstrate that compared with the conventional demand-response strategy, the proposed method reduces total operational cost by 10.7%, curtails wind–PV abandoned power, and achieves 28.6% peak-shaving ratio for data center load. Monte Carlo repeated experiments indicate that the improved Particle Swarm Optimization (PSO) reaches a 95% feasible solution rate with an average computation time of 26.8 s for day-ahead dispatch, which satisfies practical engineering requirements. Full article

Regional crop coefficients are widely used to estimate agricultural consumptive use, but their ability to represent water use across commercial fields is rarely tested. This study evaluated regional corn crop coefficients in Colorado’s South Platte River Basin against six years (2020–2025) of satellite-derived evapotranspiration from 12,430 field-years processed with the AgroET model. The benchmark is itself AgroET-derived, so the analysis assesses consistency with a field-scale satellite benchmark rather than independently validated accuracy. If applied to the full commercial population, a previously developed regional curve representing near-potential corn water use would exceed observed aggregate consumptive use by 14.0% (10.9% by volume, about 134,000 acre-feet relative to the benchmark); equivalently, commercial fields realized about 88% of near-potential use on a depth basis and 90% by volume. Refitting the same formulation to the quality-screened commercial population reduced the difference to 4.0%; this ten-point separation varied only from 9.1 to 11.1 points under ±10% benchmark rescaling. Stratifying by processing region or season changed bias by about 1 point for the tested partition. A trajectory derived from the published Colorado Agricultural Meteorological Network (CoAgMET) algorithm, never calibrated to these retrievals, was near zero in aggregate depth (−1.0%; −3.6% by volume), reflecting partial cancellation between densely and sparsely observed field-years. Field-scale errors remained substantial under every coefficient; in well-observed seasons, 90% of within-season pools met ±10% at five to eight pooled fields. Multi-year satellite records can thus be used to evaluate whether regional coefficients remain representative of commercial water use. Full article

Agricultural methane, from livestock, manure, and rice cultivation, is the largest human source of the second most important greenhouse gas. Because methane is potent but short-lived, reducing it is one of the fastest available levers on near-term warming. The obstacle, however, is widely understood to be institutional and fiscal rather than technological: these emissions are diffuse and difficult to observe, and governments can reach them only through extension services, monitoring, and regulation, all of which must be paid for. We therefore ask whether a state’s fiscal capacity is associated with lower agricultural methane, using a panel of 27 countries observed from 2014 to 2022 and comparing four governance attributes: trust in government, public employment and representation, control of corruption, and public finance. Only public finance is consistently associated with lower per capita agricultural methane, and the association holds within countries over time as well as across them, so that periods of stronger public finances coincide with lower emissions. No other governance attribute shows such a relationship. Several features of the data are consistent with a fiscal-capacity explanation rather than simple affluence: public finance is essentially uncorrelated with national income in our sample and, unlike control of corruption, bears no relation to consumption-driven emissions such as carbon dioxide and waste. The pattern is also specific to methane rather than to environmental performance in general, and it survives an extensive battery of robustness and reverse-causality checks. The results suggest that money for methane abatement works only through the state’s capacity to spend it well, a consideration that matters for the design of methane finance under the Global Methane Pledge, where funds flow to countries whose fiscal and administrative capacity to deliver agricultural change varies widely. Full article

This study evaluates the energy and operational carbon performance of phase change material (PCM) integrated into one external wall of a reference office building across 19 Turkish cities representing six TS 825 climate zones. A full-factorial parametric analysis was conducted using DesignBuilder by varying five nominal melting temperatures (21–29 °C), three PCM thicknesses (5, 10, and 20 mm), and four façade orientations. The lowest-energy tested PCM configuration reduced annual total site energy consumption in all investigated cities, with savings ranging from 0.38% in Kayseri to 2.86% in Samsun and average savings of 1.89%. The corresponding CO₂ reductions ranged from 0.26% to 1.84%, with an average reduction of 1.25%. Among the investigated thicknesses, a 20 mm PCM layer produced the lowest annual total site energy consumption in all 19 cities. Melting temperatures of 21 °C and 23 °C generally provided the greatest energy savings, while the best-performing façade orientation varied with climate. A machine-learning surrogate model was also developed for rapid PCM screening by comparing Ridge regression, Random Forest, and Gradient Boosting models. Random five-fold cross-validation produced high predictive accuracy. Leave-one-city-out validation showed limited accuracy for absolute annual energy prediction in cities excluded from model training but stronger performance for energy- and CO₂-saving percentages. The model is therefore suitable for preliminary screening and ranking PCM configurations, while detailed simulations remain necessary for final design validation. Full article

Multiple myeloma (MM) remains largely incurable because of its complex biology and frequent disease relapse. Metabolic reprogramming is increasingly recognized as a critical contributor to MM progression. NAD (P)-dependent steroid dehydrogenase-like protein (NSDHL) has been highlighted as an unfavorable metabolism-related prognostic gene in plasma cell myeloma, but its functional significance and metabolic role in MM remain unclear. Publicly available MM transcriptomic datasets and the MMRF-CoMMpass cohort were analyzed to evaluate NSDHL expression and its clinical relevance. Cell proliferation was assessed using CCK-8 assays, while glucose consumption, lactate production, and extracellular acidification rate were measured to evaluate glycolytic activity. RNA sequencing was performed following NSDHL knockdown. NSDHL was upregulated in MM and associated with advanced disease stage and poor overall survival. NSDHL knockdown suppressed MM cell proliferation and glycolytic activity. Mechanistically, NSDHL reduced c-Myc phosphorylation at T58 while promoting phosphorylation at S62, thereby inhibiting proteasome-dependent degradation of c-Myc and stabilizing the protein. Stabilized c-Myc subsequently enhanced the transcriptional expression of Pyruvate kinase M2 (PKM2), leading to increased glycolysis. Our data reveal a novel NSDHL/c-Myc/PKM2 regulatory axis that drives glycolytic reprogramming and MM cell proliferation, highlighting NSDHL as a potential prognostic biomarker and therapeutic target in MM. Full article

This paper treats the “Two Centralizations” policy for residential land supply in 22 key cities introduced by China’s Ministry of Natural Resources in 2021 as a quasi-natural experiment. Based on data of A-share non-financial listed companies from 2013 to 2023, we adopt the Difference-in-Differences (DID) method to systematically evaluate the impact of the centralized land supply policy on the carbon emissions of listed companies and its underlying mechanisms. The findings indicate that the centralized land supply policy significantly reduced the carbon emission levels of listed companies in pilot cities. Measured by the logarithm of total carbon emissions, the policy reduced the average logarithm of total carbon emissions for treatment-group firms by approximately 0.152, corresponding to an approximately 14.1% reduction in the level of carbon emissions (1 − exp(−0.152) ≈ 0.141). This effect is larger in cities with high land fiscal dependence, in high-energy-consumption industries, and among state-owned enterprises. Mechanism analysis reveals that land fiscal back-pressure, industrial land structure optimization, and green technology innovation constitute three complementary transmission channels. Heterogeneity analysis further reveals the boundary conditions for the release of policy effects: the coupling effect of land fiscal dependence and property rights nature is the most critical, and the carbon emission reduction effect of state-owned enterprises in cities with high land fiscal dependence is significantly higher than the average. This paper extends the research on factors influencing corporate carbon emissions from the perspective of land supply institutions and provides empirical evidence for the coordinated design of land policy and environmental protection. Full article

To address redundant and tortuous paths generated for tracked mowers in unstructured orchard environments and the limited adaptability of fixed-gain controllers to nonuniform-curvature paths, this study proposes a joint optimization method combining an improved rapidly exploring random tree (IRRT) planner with fuzzy PD (F-PD) tracking control. The planner uses an obstacle-density-based adaptive step size to balance search efficiency and obstacle-avoidance safety, an improved artificial potential field to bias random samples toward the goal, and cubic B-spline smoothing to generate continuous paths. Based on a differential-steering kinematic model, the F-PD controller uses heading error and its rate of change as inputs and adjusts proportional and derivative gains online through fuzzy inference. Across the three simulated environments, the average reductions in path length, node count, and computation time achieved by IRRT relative to conventional RRT were 10.6%, 11.7%, and 66.7%, respectively. The maximum lateral error of F-PD was 0.43 m, versus 1.42 m for PID. Field tests showed that the IRRT–F-PD combination reduced cumulative operation time and cumulative relative fuel consumption by 30.5% and 26.6%, respectively, compared with RRT–PID. The proposed method improves planning efficiency and curved-path tracking for autonomous orchard mowing. Full article

In greenhouses with evaporative cooling systems, the amount of water used for cooling can exceed plant water consumption, so caution is advised in water-scarce regions. Since the semi-arid region where the study was conducted receives low rainfall (an average of 380.4 mm per year), the amount of water used for plant and evaporative cooling makes water use management a critical issue. For this purpose, a study was conducted between May and July 2023 in Kırşehir, Türkiye, using two physically identical, side-by-side, polyethylene-covered high tunnels (5 m × 3 m × 2 m) with external shading nets; one operating with natural ventilation (NV) and the other with direct evaporative cooling (DEC). The study determined the effects of these applications on the indoor climate, the morphological and quality characteristics of tomatoes, plant water consumption, and water-use efficiency. During the study period, the highest cooling effect measured in the DEC application was 9.6 °C, the relative humidity effect was 29.3%, and the cooling efficiency was 67.6%. In the NV application, the highest cooling effect was 5.0 °C, and the relative humidity effect was 13.2%. As a result of the findings, the DEC application made a positive contribution to the morphological (stem diameter, plant height, and number of leaves) and quality parameters (width, length, weight, pH, titratable acidity, and total soluble solids) of tomatoes compared to the NV application. Daily plant water consumption per unit area was 114.7 L m⁻² in the NV application, 88.1 L m⁻² in the DEC application, and 117.5 L m⁻² for cooling. The amount of water used for irrigation in the NV application was approximately 23.2% higher than in the DEC application. In the study, total yield (TY) was 2355.5 g m⁻², and marketable yield (MY) was 2240.8 g m⁻² under NV application. In the DEC application, TY was 5721.5 g m⁻² and MY was 5529.8 g m⁻². Accordingly, TY decreased by 58.8% and MY decreased by 59.5% in the NV application compared to the DEC application. Total water-use efficiency (TWUE) was 20.5 g L⁻¹ in the NV application, while marketable yield water-use efficiency (MWUE) was 19.5 g L⁻¹. In the DEC application, TWUE was 42.2 g L⁻¹ while MWUE was 39.6 g L⁻¹. Accordingly, compared to the DEC application, TWUE decreased by 51.4%, and MWUE decreased by 50.8% in the NV application. Furthermore, considering the water used for cooling in the DEC application (irrigation + cooling), CTWUE was 27.8 g L⁻¹, and CMWUE was 26.9 g L⁻¹. Therefore, the water used for DEC reduced CTWUE by 34.1% and CMWUE by 32%. The results indicate that while evaporative application has positive contributions to cultivation, it also reduces water-use efficiency; therefore, its use should be considered in regions with limited water availability. Collecting rainwater and using it in irrigation and as cooling water after filtration will be important for sustainable greenhouse farming in these regions. Full article

The increasing density and complexity of airport surface operations have intensified the risk of crossing conflicts between ground service vehicles and taxiing aircraft. Such interactions are characterized by strong spatiotemporal coupling, asymmetric right-of-way relationships, and stringent safety requirements, making conventional human-driven conflict avoidance highly dependent on drivers’ perception and judgment. To address this problem, this study proposes a spatiotemporal prediction-driven model predictive control (MPC) framework for autonomous ground vehicles on airport surfaces. First, the spatial interaction between the aircraft safety boundary and the vehicle service road is modeled to define the vehicle–aircraft conflict zone. Aircraft motion information is then used to predict the temporal occupancy of the conflict zone, based on which a dynamic time-window constraint is constructed to characterize the time-varying safe passage conditions for autonomous vehicles. The predicted spatiotemporal constraints are embedded into a rolling MPC framework that continuously optimizes vehicle motion while jointly considering safety, traffic efficiency, and energy consumption. Simulation results show that, compared with human-driven vehicles, the proposed method reduces average energy consumption from 1200.16 kJ to 866.67 kJ and shortens average arrival time from 43.63 s to 41.27 s. In addition, the method demonstrates effective disturbance compensation under aircraft-state uncertainty and adaptability to sequential multi-aircraft crossing scenarios. Full article

Background: Tibetan butter tea sits at a tension between the health concerns raised by its high salt and fat content and the potential cognitive benefits of its tea-derived constituents, yet whether this traditional beverage is associated with cognition in high-altitude Tibetan adolescents has not been directly examined. Methods: We conducted a school-based cross-sectional study of Tibetan students in grades 7–12 in Lhasa, Xizang, China, between June 2024 and June 2025. The analytic sample included 7967 students, with ages ranging from 10.18 to 24.82 years (mean ± SD: 16.06 ± 1.80). Butter tea consumption frequency was self-reported. Cognitive performance was assessed with Raven’s Standard Progressive Matrices; low cognitive performance was defined as a score below the grade-specific 25th percentile. Logistic regression with cluster-robust standard errors estimated odds ratios (ORs) and 95% confidence intervals (CIs), adjusting for demographic, family, socioeconomic, sleep, physical activity, and lifestyle factors, both overall and in sex-specific models. Results: Among 7967 Tibetan students, 3534 were male, and 4433 were female. Low cognitive performance was most prevalent among students reporting daily (29.56%) or never (26.34%) consumption and less prevalent among those reporting occasional (22.18%) or frequent (20.39%) consumption. Compared with never consumption, occasional consumption (aOR = 0.79; 95% CI, 0.70–0.88; p < 0.001) and frequent consumption (aOR = 0.71; 95% CI, 0.55–0.90; p = 0.004) were associated with lower odds of low cognitive performance. Daily consumption was not statistically significant. In sex-stratified analyses, frequent consumption was associated with lower odds among males (aOR = 0.68; 95% CI, 0.49–0.93; p = 0.018). Among females, both occasional (aOR = 0.76; 95% CI, 0.64–0.90; p = 0.001) and frequent (aOR = 0.74; 95% CI, 0.58–0.93; p = 0.011) consumption were associated with lower odds. Conclusions: Occasional and frequent consumption of Tibetan butter tea was associated with reduced odds of low cognitive performance among Tibetan adolescents at high altitude, with modest sex differences. These findings suggest that Tibetan butter tea should be evaluated as a culturally embedded whole-dietary exposure and may inform culturally sensitive school-based nutrition education and public health guidance in high-altitude Tibetan settings; however, causal inference cannot be established from this cross-sectional study, and longitudinal validation is needed. Full article

Detecting tool wear in CNC milling is a central challenge for data analytics and sensor integration in Industry 4.0, as gradual degradation increases the risk of tool breakage and downtime and often leads manufacturers to replace tools early and inefficiently. This contribution reviews existing tool wear detection approaches, including machine control data and additional sensors, and addresses the resulting need for effective data reduction and interpretation. An experimental setup on a CNC milling machine collected OPC-UA data and vibration signals, processed via a KNIME-based pipeline. Results show that, in the investigated proof-of-concept experiments, simple aggregated indicators (e.g., power consumption) allow a clear distinction between new-tool and end-of-life (EoL) states under stable process conditions. However, when cutting parameters vary, the evaluated conventional machine-learning classifiers do not achieve satisfactory discrimination. This limitation is associated with parameter-induced signal overlap, the limited training dataset, and the deliberately simple time-domain features used in this study. The study is therefore intended as an exploratory proof-of-concept rather than a comprehensive validation of continuous tool wear progression. Finally, the contribution highlights the potential of generative AI for data preprocessing, showing that large language models can efficiently clean, structure, and interpret raw manufacturing data, reducing engineering effort and improving accessibility of data analytics. The findings provide a feasibility baseline for future studies addressing intermediate wear states, richer feature extraction, and broader industrial validation. Full article

Against the backdrop of the global energy crisis, hybrid vehicles offer greater energy saving potential. The equivalent consumption minimization strategy is an effective way to achieve energy savings for hybrid electric vehicles. This paper presents a fuzzy adaptive control method based on the equivalent consumption minimization strategy. To obtain a better SOC trajectory and lower equivalent fuel consumption, an adaptive adjustment algorithm for the equivalent fuel factor was designed, which incorporates a penalty function and a fuzzy adaptive method that accounts for SOC tracking errors. To verify the effectiveness of the proposed algorithm, this paper established a model of an HEV (hybrid electric vehicle) in Simulink and compared the proposed algorithm with the rule-based energy management strategy and the PI-based adaptive ECMS energy management strategy under different operating conditions. Compared with the rule-based and PI-based strategies, the proposed method improves the fuel economy by 1.01–4.64%, and the terminal SOC deviation is reduced from 0.0568–0.0619 to 0.0002–0.0010 under the UDDS and NYCC conditions. Full article

To address charging-resource contention and task-allocation conflicts in multi-UAV operations supported by a single mobile charging vehicle (MCV), this paper develops GEMS-DQN (Global-Enhanced Multi-Agent Scheduling Deep Q-Network), a centralized discrete-action scheduling framework for coordinated task and charging decisions. The framework uses a joint individual–global state representation to characterize UAV energy, task urgency, spatial information, global task progress, and charging-resource utilization. A normalized system-level reward with a dynamic conflict penalty provides explicit feedback for task-assignment and charging-resource conflicts. Per-UAV Q-values are used for feasibility masking and top-k action ranking, while beam search constructs a bounded joint-action candidate set for Monte Carlo Tree Search (MCTS) under stochastic MCV motion. Experiments are conducted over 30 independent training runs. At 800 training iterations, GEMS-DQN achieves a total score of $883.7\pm 22.4$, a task completion rate of $92.1\pm 3.4\%$, an average energy consumption of $10.3\pm 0.5\%$, and a conflict rate of $0.091\pm 0.018$. Compared with MAPPO, the strongest modern MARL baseline evaluated, GEMS-DQN improves total score by approximately $5.6\%$ and task completion by 7.9 percentage points, while reducing average energy consumption by 0.4 percentage points and conflict rate by 0.050. Ablation, reward-sensitivity, and scalability analyses further demonstrate the complementary effects of global information, conflict-aware learning, and bounded look-ahead search, while revealing the expected computation–performance trade-off of the centralized framework. Full article