Search Results (2,377)

Eye movement classification, particularly the identification of fixations and saccades, plays a vital role in advancing our understanding of neurological functions and cognitive processing. Conventional modalities of data, such as RGB webcams, often face limitations such as motion blur, latency and susceptibility to noise. Neuromorphic Vision Sensors, also known as event cameras (ECs), capture pixel-level changes asynchronously and at a high temporal resolution, making them well suited for detecting the swift transitions inherent to eye movements. However, the resulting data are sparse, which makes them less well suited for use with conventional algorithms. Spiking Neural Networks (SNNs) are gaining attention due to their discrete spatio-temporal spike mechanism ideally suited for sparse data. These networks offer a biologically inspired computational paradigm capable of modeling the temporal dynamics captured by event cameras. This study validates the use of Spiking Neural Networks (SNNs) with event cameras for efficient eye movement classification. We manually annotated the EV-Eye dataset, the largest publicly available event-based eye-tracking benchmark, into sequences of saccades and fixations, and we propose a convolutional SNN architecture operating directly on spike streams. Our model achieves an accuracy of 94% and a precision of 0.92 across annotated data from 10 users. As the first work to apply SNNs to eye movement classification using event data, we benchmark our approach against spiking baselines such as SpikingVGG and SpikingDenseNet, and additionally provide a detailed computational complexity comparison between SNN and ANN counterparts. Our results highlight the efficiency and robustness of SNNs for event-based vision tasks, with over one order of magnitude improvement in computational efficiency, with implications for fast and low-power neurocognitive diagnostic systems. Full article

Digital services companies help in the digitalization and transformation of the industry in driving Malaysia by advancing the economy of the country. However, digital services companies often face financial risks in terms of liquidity, solvency, efficiency, profitability, and operational risks. These risks increase the chances of failure and financial volatility, which put the companies at a serious disadvantage. This paper proposes an integrated Entropy-Weight TOPSIS model to analyze the financial risks of the listed digital services companies within Malaysia. The entropy method helps to prevent subjective weights by reflecting on information obtained from the financial reports of the companies. This study also provides an analysis to show possible improvements for the companies. The interest coverage ratio (ICR), which measures the capability to settle interest on debt, shows the highest weight followed by the basic indicator approach (BIA) and return on asset (ROA) based on the entropy weighting method. In addition, CLOUDPT, ITMAX, and MSNIAGA are ranked as the top three digital services companies that give the highest relative closeness to the ideal solution. The results help the risk managers to identify the criteria that caused the greatest financial risk in digital services companies to formulate targeted strategies to improve the companies’ financial health. Full article

To improve solar energy utilization efficiency, address control precision issues in solar panel tracking systems, and strengthen the sustainable supply capacity of clean renewable energy, this study proposes an innovative variable universe fuzzy adaptive PID control algorithm for high-precision solar tracking systems. Based on this algorithm, a fusion scheme combining a high-precision four-quadrant detector and GPS positioning is employed to achieve real-time and precise positioning of the tracking system. The azimuth and elevation angle deviations between the real-time solar rays and the system’s actual position are calculated and used as input signals for the tracking control system. These deviations are dynamically corrected by the variable universe fuzzy adaptive PID controller, which drives a stepper motor to achieve high-precision solar tracking. The results demonstrate that, under ideal operating conditions, the proposed algorithm reduces the steady-state error by 3.5–4.9°, shortens the settling time by 4.4–5.8 s, decreases the rise time by 0.6 s, lowers the overshoot by 18–19%, and reduces the disturbance recovery time by 1.3 s. These improvements significantly enhance tracking accuracy and dynamic response efficiency. Under complex operating conditions, the algorithm reduces the steady-state error by 3.2–5.9°, shortens the settling time by 5.4–6.2 s, decreases the rise time by 0.7 s, lowers the overshoot by 17.5–19%, and reduces the disturbance recovery time by 1.5 s, thereby ensuring stable and efficient solar tracking and maintaining continuous energy capture. By quantitatively optimizing multiple performance metrics, this algorithm significantly enhances the control precision of solar panel tracking and improves solar energy utilization efficiency. It holds substantial significance for promoting the transition of the energy structure toward cleaner and more sustainable sources. Full article

With rapid advances in nanotechnology and synthetic biology, biological nanonetworks are emerging for biomedical and environmental applications within the Internet of Bio-NanoThings. While they rely on molecular communication, experimental validation remains limited, especially for non-ideal effects such as molecular accumulation. In this work, we present a novel table-top experimental system that emulates the core functionalities of a biological nanonetwork and is straightforward to reproduce in standard laboratory environments, also making it suitable for educational demonstrations. To the best of our knowledge, this is the first experimental platform that incorporates two end nodes capable of acting interchangeably as transmitter and receiver, thereby enabling true bidirectional molecular communication. Information transfer is realized through controlled release, advection and diffusion of molecules, using molecular concentration coding analogous to concentration shift keying, while the receiver decodes messages by comparing measured concentrations against predefined thresholds. Based on the measurements reported herein, the drop-based algorithm substantially outperforms the threshold-based scheme. Specifically, it reduces first-message latency by more than 2.5× across the tested volumes and reduces latest-message latency by up to 71%, providing approximately 3.7× better message delivery. A key experimental outcome is the observation of channel saturation: beyond a certain operating period, residual molecules accumulate and effectively saturate the medium, inhibiting reliable further message exchange until sufficient clearance occurs. This saturation-induced “channel memory” emerges as a fundamental practical constraint on sustained communication and achievable data rates. Overall, the proposed platform provides a scalable, controllable, and experimentally accessible testbed for systematically studying signal degradation, saturation, clearance dynamics, and throughput limits, thereby bridging the gap between theoretical models and practical implementations in the Internet of Bio-NanoThings era. Full article

Maintaining stable pressure in the oxidation–compressor section of purified terephthalic acid (PTA) plants is essential for ensuring efficient and reliable operation. Conventional single-loop proportional integral derivative (PID) controllers frequently perform inadequately because of the large pressure drop between the compressor discharge and reactor inlet, which should ideally remain at approximately 1.2 kg/cm² above the reactor pressure setpoint but can reach up to 2.8 kg/cm² due to downstream vapor-phase disturbances. Through this study, we aimed to address this issue by developing a robust cascade pressure control strategy to improve pressure stability and reduce energy losses. Dynamic process models were constructed using system identification techniques to represent real plant behavior, and the best-performing models—identified based on minimum root mean square error (RMSE)—were determined using the Wade method for pressure indicating controller PIC-101, the Lilja method for PIC-102, and the Smith method for pressure differential indicating controller PDIC-101. The proposed cascade configuration was tuned using the Lopez ISE method and evaluated under representative disturbance scenarios. The results showed that the cascade controller significantly improved pressure control, enhanced disturbance rejection, and lowered the risk of reactor shutdowns compared with the conventional proportional-integral PI-based approach. Overall, this study demonstrated that model-driven cascade control can enhance robustness, operational reliability, and energy efficiency in large-scale PTA oxidation processes. Full article

Electrochemical reduction of carbon dioxide (CO₂RR) to methanol represents a promising approach for sustainable methanol production. Despite this potential, current technological limitations constrain both economic viability and environmental benefits. This research introduces a solar-driven multigeneration system that integrates CO₂RR to enable the coproduction of electricity and green methanol. A comprehensive energy integration analysis was conducted, alongside a combined techno-economic, energy-efficiency, and environmental (3E) assessment. Multiobjective optimization was conducted using the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). For solution selection, the analytic hierarchy process (AHP) was integrated with the order preference by similarity to ideal solution (TOPSIS) methodology. Results indicate that the integrated system achieves a 4.2% reduction in total utility consumption. The optimal levelized cost of methanol (LCOM), net specific carbon emissions (Net_SCE), and energy efficiency (η_EN) are USD 0.526/kg, −1.16 kg CO₂SCE/kg CH₃OH, and 6.52%, respectively. LCOM decreases by 30.6% compared to the initial system, Net_SCE increases by 3.44%, and η_EN improves by 5.84%. Under optimal operating conditions, CH₃OH production capacity and grid power consumption reach 45.27 tons/day and 475.83 MWh/day, respectively. The system does not currently meet the commercial threshold and becomes economically viable only if the electricity price exceeds USD 0.223/kWh. This study provides a valuable reference for future research in system-level integration of CO₂RR and multiobjective solution selection. Full article

Fuel scarcity remains a recurrent challenge in Nigeria, with significant socioeconomic consequences despite the country’s status as a major crude oil producer. This study develops a novel deterministic mathematical model to examine the dynamics of petroleum product distribution in Nigeria’s downstream sector, with particular emphasis on Premium Motor Spirit (PMS). The model explicitly incorporates key disruption and behavioral mechanisms: pipeline vandalism, industrial actions, product diversion, and hoarding that collectively drive persistent fuel shortages. The model’s feasibility, positivity of solutions, and existence and uniqueness were established, ensuring consistency with real-world operational conditions. Five equilibrium points were identified, reflecting distinct operational regimes within the distribution network. A critical distribution threshold was analytically derived and numerically validated, revealing that a minimum supply of approximately 42 million liters of PMS per day is required to satisfy demand and eliminate fuel queues. Local and global stability analyses, conducted using Lyapunov functions and the Routh–Hurwitz criteria, demonstrate that stable fuel distribution is achievable under effective policy coordination and stakeholder compliance. Numerical simulations show that hoarding by private retail marketers substantially intensifies scarcity, while industrial actions by transporters exert a more severe disruption than pipeline vandalism. The results further highlight the stabilizing role of alternative transportation routes, such as rail systems, in mitigating infrastructure failures and road-based logistics risks. Although refinery sources are aggregated and rail transport is idealized, the proposed framework offers a robust and adaptable tool for policy analysis, with relevance to both oil-producing and fuel-import-dependent economies. Full article

Traditional multi-criteria decision-making methods often operate on the assumption of symmetry, presupposing that the positive impact of a criterion’s presence is perfectly complementary to the negative impact of its absence. However, in real-world decision problems, this relationship is frequently asymmetric; some criteria act merely as “delighters,” while others represent “must-have” constraints. This study proposes a novel methodology, the Presence–Absence Synthesis (PAS) Method, which addresses this asymmetry by treating the “Presence Effect” and “Absence Effect” of criteria as two independent dimensions. The method is built upon intuitionistic fuzzy sets (IFSs) to effectively model the uncertainty and hesitation inherent in expert evaluations. The applicability of the proposed approach is demonstrated through a real-world workforce management problem aimed at assigning employees to the most suitable tasks based on their competencies in a retail store. In the study, the suitability scores derived from the PAS method are integrated into a mathematical optimization model for weekly employee scheduling, presenting a two-stage decision support framework. The results and comparisons with the Technique for Order Preference by Similarity to Ideal Solution method reveal that the PAS method more effectively distinguishes critical competency gaps (i.e., criteria with high absence effects), leading to more realistic task assignments and a measurable reduction in operational risks, such as skill mismatches and infeasible schedules. Furthermore, sensitivity analysis confirms that the proposed model yields consistent and robust results under varying conditions. Beyond the retail context, the proposed PAS framework is applicable to a wide range of decision-making problems, including healthcare staff allocation, project team formation, supplier selection, and other resource allocation settings where their presence cannot compensate for the absence of critical criteria. Full article

To address the complex multi-objective optimization problem of “cost–risk–recovery–dilution” in sublevel caving without bottom pillars under uncertainty, this study develops an operational GOOSE-based framework (OGOOSE) integrated with robust ε-constraint modeling. Methodologically, OGOOSE adopts three synergistic mechanisms: Opposition-Based Learning (OBL) for enhanced initial solution quality and spatial coverage symmetry, an Adaptive Inertia Weight (AIW) mechanism to maintain a symmetrical balance between exploration and exploitation, and a Boundary Reflection Mechanism (BRM) to ensure engineering feasibility. For modeling, an “ellipsoid-plane” geometric surrogate is employed, where the ellipsoid’s structural symmetry serves as the ideal baseline, while the Mean-CVaR criterion quantifies the asymmetry of operational risk (negative tail) under uncertainty. Taking robust cost (C) as the primary objective, the four-objective problem is decomposed via the ϵ-constraint method to enforce a balanced Pareto trade-off. Results demonstrate that OGOOSE significantly outperforms GOOSE, WOA, and HHO on CEC2017 benchmarks, achieving the lowest Friedman rank. In the engineering case study, it attains an average dilution rate of 28.95% (the lowest among comparators) without increasing unit cost or compromising recovery, demonstrating stable operational symmetry across economic and quality indicators. Sensitivity analysis of the ε-thresholds identifies an optimal “knee-point” that establishes a manageable balance between risk control (εR) and dilution limits (εP). OGOOSE effectively balances accuracy, stability, and interpretability, providing a robust tool for stabilizing complex mining systems against inherent operational asymmetry. Full article

This study aims to optimize the voltage profile of the grid by obtaining an optimum level of reactive power support from photovoltaic (PV) plants, thereby enhancing the efficiency of PV systems in power distribution networks and ensuring grid stability. Initially, voltage profiles in the sector, together with the structure and operating principles of PV plants, were considered in detail. Subsequently, the limits of reactive power support that can be provided by PV plants were determined. Then, the optimum levels of reactive power from the plants were determined using particle swarm optimization, genetic algorithm, Jaya algorithm, and firefly algorithm separately. The algorithms were tested through simulations conducted on a power distribution system operator in Türkiye. Additionally, a Modbus-based communication application was developed and tested, as a feasibility demonstration, to verify PV inverter accessibility and the capability of remotely writing reactive power reference setpoints. The quantitative optimization results reported in this manuscript are obtained from DIgSILENT PowerFactory simulations using the actual feeder model and time-series profiles. The results have revealed that PV plants can be effectively utilized as reactive power compensators to contribute to the operation of the grid under more ideal voltage profile conditions. In Türkiye, there is no regulatory or market mechanism to support reactive power provision from PV plants. Therefore, this study is novel in the Turkish market. The experimental results confirm that power generation from renewable energy can provide reactive support effectively when needed, which reveals that this approach is both technically feasible and practically relevant. Full article

Efficient waste management is a critical challenge in urban areas. This paper explores the optimization of power consumption in a smart bin management system using LoRa (long-range) communication technology. LoRa’s low-power, wide-area capabilities make it an ideal choice for IoT-based waste management systems. However, energy efficiency remains a crucial factor for ensuring the long-term sustainability of such systems, to avoid frequent intervention and reduce operating costs. This study employs advanced optimization techniques to minimize the energy usage of LoRa nodes while maintaining a reliable data transmission and system performance. By integrating a dynamic scheduling algorithm based on the usage of bins, and a custom adaptive data rate and power algorithm, the proposed solution significantly reduces the system’s energy impact. The performance of the system is evaluated through simulations and real-world deployment, where the results demonstrate a significant reduction in energy usage, over 84%, a longer battery life, and fewer maintenance interventions. The findings provide a scalable and energy-efficient framework for deploying smart waste management systems in resource-constrained environments. Full article

Two-dimensional transition metal dichalcogenides (TMDs) are key materials in ultrafast photonics. However, the performance of conventional TMDs is limited by their bandwidth and carrier recovery time. The novel Dirac semimetal nickel ditelluride (NiTe₂), with its broad-band response and excellent nonlinear properties, emerges as an ideal candidate for saturable absorber (SA) materials. In this work, we report, for the first time, the application of NiTe₂ in the ytterbium-doped fiber laser, demonstrating stable passive Q-switching operation. The nonlinear transmission curve reveals a modulation depth of 6.82% at 1 µm and a saturation intensity of 2.12 MW/cm². Using an all-fiber ring cavity structure, stable Q-switched pulses with a central wavelength of 1031 nm were achieved at a pump threshold of 94 mW, with a maximum pulse repetition frequency of 30.1 kHz. The minimum pulse width reached 2.3 μs, and the single-pulse energy increased to 3.05 nJ, with an impressive radio frequency (RF) spectral signal-to-noise ratio (SNR) of 58.9 dB. This study demonstrates the potential of NiTe₂ as a high-performance SA in the near-infrared region, providing a solid foundation for its future application in ultrafast laser technologies. Full article

Background: Intraventricular tumors represent a minority in the context of brain tumors, but their surgical treatment is particularly complex due to their vascularization and visualization, especially in deep localization. The characteristics of these tumors make them ideal candidates for minimally invasive surgical strategies such as the tubular retractor technique, above all in the elderly population. Objectives: A 1-year multi-center, retrospective case series was performed: the authors describe their preliminary experience using a neuronavigated tubular retractor in the management of 11 cases of intraventricular meningiomas. Methods: Clinical and radiological findings were examined to define the outcomes. We used an alternative tubular retractor system obtained using a modified preexisting general surgery trocar (ENDOPATH XCEL 15 mm trocar) or the NICO System BrainPath. Results: Gross total resection, defined as the removal of all the tumor visible from the brain scans, was achieved in all cases. Ten out of eleven of the patients did not experience major complications or permanent neurological deficits. Four patients presented transitory post-operative agitation, visual blurring and transient hemiparesis. All patients (mean age 72.6 years) were discharged from the hospital in 5–7 days. Conclusions: Our preliminary experience suggests that the use of navigated tubular retractors, by displacing the fibers and hence minimizing the damage to the surrounding cerebral parenchyma, is feasible and safe, representing a minimally invasive technique for a personalized and patient-tailored approach. The use of the selective ultrasonic aspirator makes it possible to excise the tumor through the narrow corridor of the tubular lumen of around 2 cm, and this technique can also be improved using both endoscope and microscope guidance. Full article

The shift toward Industry 5.0 places human-centred and digitally integrated metrology at the core of modern manufacturing, particularly in the automotive sector, where portable Laser Line Triangulation (LLT) systems must combine accuracy with operator usability. This study addresses the challenge of operator-induced variability by evaluating how algorithmic strategies and mechanical support features jointly influence the performance of a portable LLT device derived from the G3F sensor. A comprehensive Measurement System Analysis was performed to compare three feature extraction algorithms—GC, FIR, and Steger—and to assess the effect of a masking device designed to improve mechanical alignment during manual measurements. The results highlight distinct algorithm-dependent behaviours in terms of repeatability, reproducibility, and computational efficiency. More sophisticated algorithms demonstrate improved sensitivity and feature localisation under controlled conditions, whereas simpler gradient-based strategies provide more stable performance and shorter processing times when measurement conditions deviate from the ideal. These differences indicate a trade-off between algorithmic complexity and operational robustness that is particularly relevant for portable, operator-assisted metrology. The presence of mechanical alignment aids was found to contribute to improved measurement consistency across all algorithms. Overall, the findings highlight the need for an integrated co-design of algorithms, calibration procedures, and ergonomic aids to enhance repeatability and support operator-friendly LLT systems aligned with Industry 5.0 principles. Full article

In industrial combustion processes, high concentrations of propane (C₃H₈) gas are employed. Therefore, developing gas-detecting devices that operate under high concentrations, elevated temperatures, and short response times is crucial. This paper presents the design, simulation, and construction of a novel propane (C₃H₈) gas detector. The design was based on the dynamic electrical response of a gas sensor fabricated with cobalt antimoniate (CoSb₂O₆). The simulation considered the device structure and programming criteria, and the final prototype was constructed according to the sensor response, design parameters, and operating principles. Design, simulation, and fabrication results were in concordance, confirming the correct operation of the detector at high gas concentrations. A mathematical model was derived from the sensor’s electrical response, establishing a resistance value that allowed a two-second response time. This resistance was used to adapt the signal between the gas sensor and the PIC18F2550 microcontroller. Input/output signals, safety criteria, and functionality principles were considered in the programming device. The resulting propane (C₃H₈) gas detector operates at 300 °C, detects high C₃H₈ concentrations, and achieves a 2 s response time, making it ideal for industrial applications where combustion monitoring is essential. Full article