Search Results (2,288)

Population aging has markedly increased the burden of cancer in older adults, in whom frailty, sarcopenia, and reduced physiological reserve limit tolerance to treatment and worsen clinical outcomes. Aging is accompanied by progressive functional decline and by biological processes such as cellular senescence, characterized by irreversible cell cycle arrest, chronic low-grade inflammation, and impaired immune surveillance. The accumulation of senescent cells and the persistence of a senescence-associated secretory phenotype contribute to tissue dysfunction and generate a microenvironment that favors tumor initiation and progression. Physical exercise has been associated with attenuation of inflammation, improvements in metabolic and immune function, and with lower levels of senescence-related biomarkers. Although aerobic exercise has been extensively studied in this setting, resistance training holds relevance for older adults due to its capacity to counteract sarcopenia, preserve muscle strength and power, and sustain functional independence. Structured and periodized approaches to resistance exercise may further enhance these benefits by delivering targeted stimuli aligned with age-related physiological deficits. Block strength training (BST), a periodized model that concentrates training adaptations into sequential phases of maximal strength, power, and muscular endurance, has demonstrated consistent improvements in functional performance and reductions in frailty risk in community-dwelling older adults. BST improves physical function. It may also influence biological processes related to aging and cancer; however, mechanistic evidence specific to BST remains to be established. We hypothesized that the exercise in block as a targeted, a structured and physiologically grounded resistance training intervention highlights the potential of BST to promote functional aging and healthy. In the case of cancer biology, and the environment near to tumour, the relationship between aging mechanisms in older adults and controlled exercise effects are currently in advance, but mechanistic trials are still lacking. Finally, we propose a novel training method, structured and personalized, that could impact different clinical outcomes in older patients with cancer. Full article

This paper presents a compact output-stage current-limiting architecture intended for reliable overcurrent protection in CMOS analog and mixed-signal circuits. In modern integrated systems, the output stages of blocks such as operational amplifiers, drivers, buffers, and reference circuits may be exposed to overload conditions, low-impedance loads, or short circuits that can lead to excessive power dissipation and device degradation. The proposed architecture employs scaled replicas of the output transistors together with local negative feedback to sense the delivered load current and independently limit both sinking and sourcing currents. The circuit is demonstrated by integration into a two-stage folded-cascode operational amplifier with a class-AB output stage and evaluated through circuit-level simulations in 130 nm CMOS technology. The results confirm a well-defined current limit across the supply and temperature corners that are relevant to high-reliability applications, spanning 2 V and 5 V supplies and a temperature range from −55 °C to 175 °C. The proposed current-limiting scheme constrains both pull-down and pull-up currents to approximately 9–12 mA across the investigated operating domain. Monte Carlo analysis further shows bounded dispersion and symmetric single-mode distributions, indicating predictable operation under device mismatch. These results demonstrate that the proposed architecture provides a compact and scalable solution for deterministic current limiting in reliability-critical CMOS systems. Full article

Industrial electrical houses are engineered systems that transform and control electrical power to supply industrial loads. Preparing technical proposals for these rooms requires consistent engineering choices across multiple artifacts while drawing from heterogeneous client documents, historical projects, and supplier catalogs. This paper reports an industrial prototype that integrates generative AI, system modeling, and mathematical decision methods to support that workflow. We represent requested outputs as ordered sequences of functions and link those functions to candidate equipment blocks through functional and physical graphs that enable traceable retrieval and reuse. Using this representation, we compute a minimal internal-cost baseline by solving a mixed-integer assignment model with sizing constraints, and we rank technically feasible alternatives using fuzzy DEMATEL to derive criterion weights and TOPSIS to obtain an overall ordering under multiple criteria. The workflow is illustrated with an example and the prototype tool used in a company operating in Chile, Peru, Ecuador, and Bolivia, where document ingestion and equipment-list extraction are integrated with human validation. The results illustrate how structured representations, optimization, and multi-criteria ranking can support auditable configurations for engineering review and commercial selection. Full article

The deployment of Convolutional Neural Networks (CNNs) on entry-level Edge FPGAs is severely constrained by the scarcity of Digital Signal Processing (DSP) blocks, a phenomenon termed the “DSP Wall”. To circumvent this bottleneck, this paper presents AEMAC, a Software–Hardware Co-Designed accelerator architecture that decouples arithmetic computation from DSP availability. The proposed methodology synergizes a software-level Dynamic Integer Scaling strategy with a hardware-level Adaptive Error-Compensated Multiply-Accumulate unit. By mapping floating-point activations to an optimal integer domain and employing a DSP-free, LUT-based tri-mode datapath, the architecture achieves extreme resource efficiency. To mitigate the precision loss inherent in logic-based truncation, a statistical bias compensation mechanism is integrated into the accumulator chain. Experimental validation on a Xilinx Zynq-7020 FPGA demonstrates a strictly zero-DSP implementation with minimal logic utilization (100 LUTs). Post-implementation timing simulations confirm a dynamic power of 0.490 W for a 64-core cluster under worst-case random workloads, yielding a verified energy efficiency of 26.1 GOPS/W. Micro-level analysis confirms a 16.7% reduction in arithmetic Mean Absolute Error (MAE) compared to naive truncation. Furthermore, macro-level evaluation on the CIFAR-10 dataset reveals that the co-design strategy recovers system accuracy to 64.74%, outperforming the uncompensated baseline by 0.55% and achieving statistical comparability to floating-point baselines. To ensure absolute internal consistency, all hardware metrics are strictly validated via SAIF-based post-implementation simulations. Based on a conservative full-chip projection that incorporates a routing derating model, these internally consistent results establish AEMAC as a highly scalable and reliable solution for breaking the DSP wall in resource-constrained edge intelligence. Full article

This paper presents a quantum system designed to generate random, phase-manipulated emissions. A key feature of the proposed system is its ability to create a controllable electromagnetic center. To achieve this, the architecture utilizes two synchronized sources positioned at distinct spatial locations. A method is introduced where Quantum-generated keys are used to form a random sequence in real time to control digital phase manipulators. A block diagram of a quantum system for generating random phase-manipulated emissions with a controllable electromagnetic center has been developed that enables control of the main operating frequency, the length of the additionally generated random sequences controlling the modulations, the frequencies and phases of the emissions, the period and start of phase manipulations, as well as the power of the signals emitted by each of the channels. This way ensures uniformity or a controllable difference in the signals emitted by the two sources of the system upon their arrival at a predetermined point in space. A laboratory prototype of the quantum system has been developed, and tests have been conducted to confirm the feasibility of the proposed method and block diagram. The proposed research refers to a case of phase manipulation of transmitted signals with a preset clock frequency. The theoretical and technical solutions presented in the material can also be used to create systems with randomly frequency-manipulated signals, as well as systems in which the manipulation periods change randomly, determined by random quantum keys generated in real time. Full article

The transition toward capacity-based network tariffs shifts the primary role of battery energy storage systems (BESS) from traditional energy arbitrage to active peak shaving. This paper presents a mixed-integer linear programming (MILP) optimization model for the co-optimization of both BESS size and operation scheduling for multiple prosumers operating individually and within an energy community (EC). Battery aging is accounted for in the optimization model through the state of health (SOH). The framework is evaluated by a comprehensive techno-economic analysis of BESS integration under Slovenia’s multi-block tariff structure. The results demonstrate that while individual distributed BESS integration is highly profitable, centralized EC BESS financially underperforms. Because centralized BESS cannot directly reduce individual contracted power limits, its profitability relies on energy arbitrage, making the initial investment and double grid fees the primary barriers. Conversely, integrating distributed storage with peer-to-peer (P2P) trading minimizes the required BESS capacity while maintaining profitability. The evaluation also reveals that ECs do not automatically act as socio-economic equalizers, indicated by a stable Gini coefficient. However, a break-even analysis reveals the necessary reduction in capital costs to overcome these hurdles, confirming the strong future viability of centralized EC BESS. Full article

Stereo matching constitutes a critical technology in applications such as autonomous driving and robot navigation. Conventional algorithms, however, often encounter limitations in real-time performance and resource efficiency when deployed on embedded platforms. This paper presents a real-time stereo matching system implemented on a Field-Programmable Gate Array (FPGA), which is built around a lightweight, hardware-optimized dual-path Semi-Global Matching (SGM) algorithm. The proposed method simplifies the traditional eight-path cost aggregation into horizontal and vertical dual-path aggregation, substantially reducing hardware resource consumption while preserving matching accuracy. The system employs a pipelined architecture that integrates image capture, DDR3 caching, and HDMI display output. Experimental results demonstrate that under the configuration of a 5 × 5 matching window and a disparity range of 64, the system generates stable disparity maps at 60 frames per second, with total power consumption below 2.2 W and FPGA core logic utilization under 30%. Compared to the conventional eight-path SGM, the dual-path strategy incurs only a marginal 6% increase in average bad pixel rate on standard stereo datasets while reducing Block RAM (BRAM) usage by approximately 30%. This achieves an effective practical balance between accuracy, computational efficiency, and power consumption. Full article

As a widely used computing substrate, the side-channel security of FPGAs has attracted considerable attention, yet a systematic understanding of how FPGA device types contribute to exploitable leakage remains limited. This work presents a device-centric evaluation that maps an S-box-like function onto common FPGA primitives, including look-up table (LUT), flip-flop (FF), block RAM (BRAM), and distributed RAM (LUTRAM), and assesses Correlation Power Analysis (CPA) outcomes under the Hamming Weight (HW) and Hamming Distance (HD) power models. The results show pronounced leakage differences across device types: FF- and BRAM-based implementations exhibit substantially stronger leakage than LUT- and LUTRAM-based ones, and they frequently achieve $\mathrm{GE}=0$ in our configurations, while the HD model is generally more effective than the HW model in the performed CPA evaluations. Notably, FF-, BRAM-, and LUTRAM-based implementations can already be breakable starting from one instance under the HD model in our device-level tests, indicating that exploitable leakage may manifest in real FPGA applications. These device-level observations are further validated on a practical cipher by analyzing two SM4 encryption modules that differ only in the S-box implementation style; the BRAM-based design shows significantly stronger leakage than the LUT-based design, achieving $\mathrm{GE}=2.58$ versus $\mathrm{GE}=78.3$ at 10,000 traces. This work highlights the critical role of device selection and implementation style in FPGA side-channel security, and it provides practical insights for designing secure FPGA applications against power side-channel analysis. Full article

Unmanned Aerial Vehicles (UAVs) were originally designed for military and surveillance applications but are now significant in smart agriculture, wireless communication, and product delivery. In contrast to an Internet Service Provider (ISP), which typically relies on fixed base stations, which can fail in the event of a disaster, UAVs offer more stable alternatives. Because IoT devices, sensors, and ground users have limited processing power and battery life, there is a need for energy-efficient solutions. Meanwhile, users still expect high data rates. UAV-based wireless networks can meet these needs, even in harsh or disaster-hit areas. Current research focuses on improving energy efficiency and data transmission by optimizing UAV flight paths and scheduling. In this work, we tackle these issues by formulating a mixed-integer non-convex optimization problem that jointly considers device scheduling and UAV trajectory. We further decompose it into the following two parts: energy-efficient scheduling among ground users ($P_2$) and the trajectory optimization of UAVs ($P_3$). To address these issues, we develop a linear programming relaxation approach, a Quadratically Constrained Quadratic Programming (QCQP)-based Successive Convex Approximation (SCA) scheme, and the Block Coordinate Descent (BCD) algorithm. Experimental results demonstrate that our approach outperforms the state of the art in both power consumption and transmission rate. Full article

The optimal dispatch of integrated energy systems (IESs) is strongly affected by uncertainties on both the supply and demand sides. To model wind power uncertainty and embed it into dispatch decision-making, this paper develops a distributed stochastic scheduling method driven by Diffusion-TS-based scenario generation. First, a conditional Diffusion-TS model is developed to generate high-fidelity wind power scenarios from day-ahead forecasts, and a temperature parameter is introduced to balance scenario diversity and fidelity. Second, a distributed stochastic scheduling framework with chance constraints is established, in which the probabilistic constraints are reformulated into a mixed-integer linear programming problem to address source-load fluctuations while preserving subsystem privacy. Third, the block coordinate descent method is used to decompose the system into cooling, heating, and electricity subproblems for iterative solution. Case study results show that the average CRPS of the generated scenarios is 162.16 MW, which is 34% lower than that of the deterministic forecast benchmark. The total cost of distributed deterministic dispatch is 2.8% higher than that of centralized deterministic dispatch, while the total cost of distributed stochastic dispatch is 53.1% higher than that of distributed deterministic dispatch, reflecting the additional economic cost of uncertainty-aware scheduling. Compared with the traditional LHS-Kmeans method, the scenarios generated by Diffusion-TS are closer to the actual wind power output. Although the resulting dispatch cost is higher, the obtained scheduling results are more consistent with realistic wind power conditions. Overall, the proposed method provides a practical technical route for the secure and economical operation of IESs under uncertainty. Full article

Under high-power disturbances such as HVDC blocking, stability strategies such as generator tripping are employed to ensure the frequency stability of the sending-end power grid. For renewable energy units, rapid emergency power reduction instead of direct tripping can quickly reduce active power and suppress frequency spikes, while maintaining grid connection to provide dynamic reactive power support, avoiding voltage collapse, and smoothly restoring power after a fault, thus improving the transient stability and resilience of a high-proportion renewable energy grid. However, the control performance of rapid emergency power reduction for wind turbines is limited by the converter’s overcurrent capacity and the unit-side load limit. Sudden large-scale active power reduction can easily cause motor speed fluctuations and mechanical stress accumulation, and may trigger current limiting and protection actions when the inverter current is saturated, or the DC bus voltage exceeds the limit, thus strictly limiting the range and duration of the adjustable power. To address the engineering requirements for rapid active power reduction in wind turbines, this paper proposes a control scheme based on low-voltage ride-through (LVRT) energy dissipation circuit reuse, and simultaneously conducts a special study on LVRT reuse conditions. When the unit receives a command to rapidly reduce active power, the scheme uses a percentage current duty cycle control strategy to drive the energy-consuming circuit to quickly dissipate excess energy. Simultaneously, it controls the pitch angle to increase at the maximum adjustment rate, thus completely eliminating excess power. This scheme leverages the existing LVRT hardware of the wind turbine to expand its functionality without requiring additional equipment. Furthermore, research on LVRT reuse conditions provides crucial support for the reliable operation of the scheme, demonstrating both outstanding economic efficiency and engineering practicality. Full article

Hyperspectral imaging provides rich spatial–spectral information but generates huge data volumes, posing significant challenges for storage, transmission, and real-time processing in remote sensing applications. In this study, we propose SpecResNet, a 3D autoencoder-based model for hyperspectral image compression. This framework introduces hybrid residual blocks for preserving representational power and a spectral calibration (SC) block to enhance spectral fidelity. It also uses Squeeze-and-Excitation (SE) blocks for adaptive feature recalibration. Our model obtains different compression operating points by varying model capacity, with bitrate emerging implicitly from the learned latent representations. Experiments on several benchmark datasets show that SpecResNet surpasses the performance of existing frameworks on most datasets in terms of PSNR, MS-SSIM, and SAM, demonstrating its strong potential. Our results suggest that SpecResNet offers a promising trade-off for efficient hyperspectral image compression, with potential for further refinement in complex scenes. Full article

Soaking bovine tooth blocks in demineralization solution is a widely used method to simulate caries-like demineralization for further experimental studies. The objective of this study was to evaluate the degree and depth of mineral loss in bovine enamel and dentin blocks under various controlled conditions and to investigate the relationships between these factors and mineral loss, providing guidance for researchers to achieve targeted demineralization outcomes. A total of 54 enamel blocks and 54 dentin blocks were divided into 18 groups according to the exposed area and solution volume and then immersed in demineralization solution. Micro-CT scans were performed before immersion, as well as after 1, 2, 3, 7, and 10 days of immersion. The results were analyzed using data analysis software and subsequently summarized into graphical representations. The analysis revealed that soaking time and solution volume showed positive correlations with mineral loss, whereas the exposed area was negatively correlated with mineral loss. Mean mineral loss increased significantly with immersion time in all groups (e.g., from 6314 to 25,670 vol%·μm in the dentin 3 × 3 mm², 50 mL group, p < 0.05). After 7 days, specimens immersed in larger solution volumes showed significantly greater mineral loss than those immersed in smaller volumes (p < 0.05). In addition, larger exposed areas resulted in greater mineral loss after 3 days of immersion. Mean mineral loss followed a power function relationship with time when the solution volume was sufficiently high relative to the exposed surface area. In contrast, when the solution volume was limited, a logarithmic relationship between time and mineral loss was observed. Given its superior stability, the mean mineral loss appears to be a more reliable indicator for assessing tooth demineralization. Based on our results, more controlled and reproducible demineralization conditions can be achieved, which may contribute to improving the reliability of in vitro caries models and facilitating the evaluation of preventive and therapeutic strategies. Full article

In this work, we propose a localization method based on an exogenous radar with multi-base station and the synchronization signal block (SSB) in 5G downlink signals. We combine physical cell identities (PCIs)-based identification with the extensive cancellation algorithm (ECA) to reconstruct and cancel the present strongest SSB signal, thereby obtaining reference signal receiving power (RSRP) values of them in descending order of strength. Then, we designed a two-stage localization method. Firstly, we determined the target’s coarse location based on the directional characteristics of different SSB beams. Subsequently, we compared the RSRP values extracted from the actually received signals against those pre-obtained when the target is at various reference points. The reference point corresponding to the closest match was selected as the estimated target position. We conducted simulations under various signal-to-noise ratio (SNR) levels, reference point densities, and signal jitter conditions. The simulation results demonstrate that the method outperforms techniques such as Fang’s method for time difference of arrival (Fang-TDOA) and observed time difference of arrival (OTDOA). Full article

Background/Objectives: Self-rated health (SRH) is a widely used summary indicator of health and a predictor of subsequent morbidity and mortality. Among adults with severe disabilities, SRH may reflect not only chronic conditions and functional limitations but also psychological factors, particularly depressive mood. This study examined the incremental contribution of depressive mood beyond physical and functional factors to SRH among adults with severe disabilities. Methods: We analyzed data from a survey of adults with severe disabilities in Seoul, South Korea (N = 1519). SRH (higher scores indicating better health) was modeled using block-wise hierarchical linear regression with robust standard errors. Models sequentially adjusted for (1) sociodemographic factors (including living arrangement); (2) disability characteristics (disability type and multiple disability status); (3) physical and functional health factors (illness status, instrumental activities of daily living (IADL), and unmet medical need); and (4) depressive mood. Results: In the fully adjusted model (R² = 0.241), illness status (b = −0.330, p < 0.001), functional capacity (IADL; b = 0.116, p < 0.001), and depressive mood (b = −0.105, p < 0.001) were independently associated with SRH. Adding disability characteristics significantly improved model fit (ΔR² = 0.074; Wald block F(3, 1510) = 42.56, p < 0.001). Further adding illness status, IADL, and unmet medical need improved model fit (ΔR² = 0.051; Wald block F(3, 1507) = 30.85, p < 0.001), and depressive mood provided additional explanatory power (ΔR² = 0.011; Wald block F(1, 1506) = 16.86, p < 0.001). Living alone and unmet medical need were not significantly associated with SRH after adjustment. Conclusions: Depressive mood was independently associated with SRH among adults with severe disabilities, even after accounting for physical health and functional limitations. These findings suggest that attention to depressive mood may be relevant to disability-related assessment and service planning, alongside chronic disease management and functional support. The observed association reflects a short-term affective state rather than clinical depression and should be interpreted within the context of subjective health appraisal. Full article