Search Results (3,828)

Agricultural mechanization and advanced technology have developed significantly in the coffee industry. However, there are still requirements for human laborers to operate, monitor crop health care, and manage production. The integration of advanced technology can significantly enhance the production efficiency and management practices of agricultural enterprises. This paper aims to address these gaps by proposing and implementing a computer vision-based attendance tracking system on mobile platforms that are suitable for the requirements and limitations of agricultural enterprises. First, the face detection process involves interpreting and locating facial structure. Next, the model transforms a photographic image of a human face into digital data based on the unique features and facial structure. We utilize the InsightFace model with the buffalo_l variant, as well as ArcFace with a ResNet backbone, as a facial recognition algorithm. After capturing a facial image, the system conducts a matching process against the existing database to verify identity. Finally, we implement a mobile application prototype on both iOS and Android platforms, ensuring accessibility for farm workers. As a result, our system achieved 95.2% accuracy on the query set, with an average processing time of <200 ms per image (including face detection, embedding extraction, and database matching). The system performs real-time attendance monitoring, automatically recording the entry and exit times of farm workers using facial recognition technology, and enables quick registration of new workers. Our work is expected to enhance transparency and fairness in the human management process, focusing on the coffee farm use case. Full article

To improve the identification accuracy of leakage layer location in an open-hole well with a distributed fiber optic temperature system, a transient temperature field heat transfer numerical calculation model for bare hole wellbore leakage process was established based on process of the distributed fiber optic open-hole well temperature measurement technology, considering factors such as drilling fluid frictional pressure drop, casing section and bare hole section boundary conditions. The distributed fiber optic test data was compared with the calculation model, and the wellbore calculated temperature distribution was consistent with the test temperature curve, and the temperature characteristics of the leakage layer location were obvious, with a maximum error of less than 5.5%. The calculation results show that when using distributed fiber optic open-hole well leak detection, by extending the continuous injection time of drilling fluid to 30 min and increasing the injection flow rate of drilling fluid by 30 L/s, the temperature at the wellbore leak location reaches 2.7 °C and 6.6 °C, respectively, which can reduce the difficulty of identifying the leak location and improve the accuracy of leak location identification. However, after changing the type of drilling fluid, the calculated wellbore temperature distribution showed a difference of no more than 0.01 °C. When detecting the location of the leakage layer in open-hole wells with high temperature gradients, the temperature difference at the leakage layer is more pronounced, which can reduce the difficulty of leak location via distributed fiber optic system. Full article

Location-based services (LBSs) and positioning systems have spread worldwide due to the emergence of Internet of Things (IoT) and other application domains that require real-time estimation of the position of a person, tag, or asset in general in order to provide users with services and apps with added value. Whereas Global Navigation Satellite Systems (GNSSs) are well-established solutions outdoors, positioning is still an open challenge indoors, where different sensory technologies may be considered for that purpose, such as radio frequency, infrared, or ultrasounds, among others. With regard to ultrasonic systems, previous works have already developed indoor positioning systems capable of achieving accuracies in the range of centimeters but limited to a few square meters of coverage and severely affected by the Doppler effect coming from moving targets, which significantly degrades the overall positioning performance. Furthermore, the actual bandwidth available in commercial transducers often constrains the ultrasonic transmission, thus reducing the position accuracy as well. In this context, this work proposes a novel excitation and processing method for an ultrasonic positioning system, which significantly improves the transmission capabilities between an emitter and a receiver. The proposal employs a superheterodyne approach, enabling simultaneous transmission and reception of signals across multiple channels. It also adapts the bandwidths and central frequencies of the transmitted signals to the specific bandwidth characteristics of available transducers, thus optimizing the system performance. Binary spread spectrum sequences are utilized within a multicarrier modulation framework to ensure robust signal transmission. The ultrasonic signals received are then processed using filter banks and matched filtering techniques to determine the Time Differences of Arrival (TDoA) for every transmission, which are subsequently used to estimate the target position. The proposal has been modeled and successfully validated using a digital twin. Furthermore, experimental tests on the prototype have also been conducted to evaluate the system’s performance in real scenarios, comparing it against classical approaches in terms of ranging distance, signal-to-noise ratio (SNR), or multipath effects. Experimental validation demonstrates that the proposed narrowband scheme reliably operates at distances up to 40 m, compared to the 34 m limit of conventional wideband approaches. Ranging errors remain below 3 cm at 40 m, whereas the wideband scheme exhibits errors exceeding 8 cm. Furthermore, simulation results show that the narrowband scheme maintains stable operation at SNR as low as $-32$ dB, whereas the wideband one only achieves up to $-17$ dB, highlighting the significant performance advantages of the proposed approach in both experimental and simulated scenarios. Full article

Mating disruption (MD) is an environmentally friendly pest management approach that uses synthetic pheromones to interfere with insect mate location and reproduction. This review summarizes current progress in the application of MD for stored-product pests, with emphasis on Lepidoptera (Plodia interpunctella Hübner and Ephestia kuehniella Zeller (Pyralidae)) and Coleoptera (Sitophilus spp. (Curculionidae)). For moth pests, numerous studies have demonstrated substantial suppression of mating and population growth under both laboratory and field conditions, particularly when MD is integrated with sanitation, monitoring and other IPM measures. Conversely, MD applications against beetles have been less successful due to their aggregation-based communication and lower volatility of their pheromones. Advances in pheromone formulation technology, including polymer dispensers, microencapsulated sprays and aerosol emitters, have improved pheromone stability and controlled release, although achieving uniform coverage in large and aerated storage environments remains challenging. The integration of MD with biological control, temperature management and reduced fumigant use offers promising directions for sustainable pest suppression. Continued development of smart-release devices, long-term field validation and integration with automated monitoring systems will further enhance the feasibility and cost-effectiveness of MD. Overall, MD represents a key behavioral component in reducing pesticide reliance and promoting sustainable management of stored-product pests. Full article

The emergence and widespread use of low-orbit satellite communication systems has become one of the triggers for the development of the Internet of Vehicles (IoV) technology. The main goal of this integration was to increase the level of vehicle safety not only in cities and their suburbs but especially in remote areas of the country. Despite its effectiveness, satellite IoV remains susceptible to attacks on the radio channel. One of the effective ways to counter such attacks is to use wireless transmission systems with the Frequency-Hopping Spread Spectrum (FHSS) method. The effectiveness of FHSS systems largely depends on the operation of the pseudorandom code generator (PRCG), which is used to calculate the new operating frequency code (number). This generator must have the following properties. Firstly, it must have high cryptographic resistance to guessing a new operating frequency number by an attacker. Secondly, since this generator will be located on board the spacecraft, it must have high fault tolerance. The conducted studies have shown that substitution–permutation network “Kuznechik” (SPNK) meets these requirements. To ensure the property of resilience to failures and malfunctions, it is proposed to implement SPNK in codes of redundant residual class systems in polynomials (RCSP) using the isomorphism of the Chinese Remainder Theorem in polynomials. RCSP codes are an effective means of eliminating computation errors caused by failures and malfunctions. The aim of this work is to increase the fault tolerance of PRCG based on SPNK transformation by using the developed error correction algorithm, which has lower hardware and time costs for implementation compared to the known ones. The comparative analysis showed that the developed algorithm for error correction in RCSP codes provides higher fault tolerance of PRCG compared with other redundancy methods. Unlike the “2 out of 3” method of duplication, the developed algorithm ensures the operational state of PRCG not only when the first failure occurs but also during the subsequent second one. In the event of a third failure, RCSP is able to correct 73% of errors in the informational residues of code combination, while the “2 out of 3” duplication method makes it possible to fend off the consequences of only the first failure. Full article

This study employs a multilevel modeling approach to describe the deformation of specimens made from austenitic Wire Arc Additive Manufactured (WAAM) steel 308LSi. Two WAAM processing modes were investigated: (1) the Cold Metal Transfer (CMT) method and (2) Cold Metal Transfer combined with interlayer deformation strengthening (hammer peening/forging). Test specimens were cut from the deposited walls at 0° and 90° relative to the deposition direction. The grain and dendritic structures of the specimens were analyzed using optical stereomicroscopy. A statistical multilevel model has been developed, accounting for the features of the grain-dendritic and defect structures under various technological deposition modes. Parameter identification and model verification were conducted based on experimental data from uniaxial tensile tests of 308LSi steel specimens. The maximum deviation of the numerical results from the experimental data during the identification stage under uniaxial tensile loading did not exceed 3%, and during the verification stage it did not exceed 10%; the overall mean deviation did not exceed 1% for the identification stage and 2% for the verification stage. The model effectively captured the anisotropic mechanical behavior of WAAM-processed samples. The maximum calculated yield strength 360 MPa was obtained for specimens cut at an angle of 45°, while the minimum value 331 MPa was observed for vertically oriented specimens. Specimens subjected to interlayer forging (hammer peening) exhibited isotropic material properties. Explicit multilevel modeling, incorporating the presence of MnO oxide inclusions located within the austenite matrix, was performed. The results showed good correlation with experimental data and confirmed the localization of fatigue cracks at the phase boundary-matrix-oxide interface. Full article

In this work, a dual-band, dual-mode, circularly polarized fully woven textile antenna with capability for Simultaneous Wireless Information and Power Transfer (SWIPT) in wearable applications is presented. The power and the data transfer modes work at 2.4 and 5.4 GHz, respectively. The radiating element is based on a square patch with an asymmetrical U-shaped slot and a chamfered corner. A single-diode rectifier, required for the power transfer mode, is mounted on a carrier thread and then connected to the antenna through a T-match network located at one of the patch corners. This feeding technique simultaneously provides complex conjugate matching to the rectifier and circular polarization. On the other hand, a coaxial probe port is used for the data transfer mode. A prototype was implemented and experimentally characterized. Regarding the power transfer mode, the measured RF-DC conversion efficiency is about 50% when the available power at the rectifier input is −10 dBm, and the axial ratio is smaller than 3 dB. In the data transfer mode, the antenna gain and the axial ratio are 0 and 2 dB, respectively. The experimental results are in good agreement with simulations, validating the proposed structure and design methods, and they are comparable to the state of the art for textile antennas/rectennas. Furthermore, the combination of the fully woven technology and the proposed single-layer layout provides a large degree of integration and robustness, which are valuable characteristics for wearable devices. Full article

This study presents the e[kent-im] model, a map-based crowdsourcing initiative that digitizes and safeguards urban memory and cultural heritage through community participation and digital tools. The model facilitates the collection, archiving, and dissemination of urban memories by fostering intergenerational knowledge transfer and encouraging civic engagement in heritage preservation. Implemented in the historical center of Kütahya/Türkiye, the project gathered 150 memories and stories from 12 senior participants aged 50–85, which were linked to 303 historical visuals sourced from personal archives. These materials were integrated into a custom-designed web and mobile interface (Mapotic Pro) enriched with metadata categories such as type, period, and location, enabling users to filter and navigate content effectively and watch the videos enriched with participant narratives. A digital city archive matrix was also developed to systematically organize the collected data and support the web-based platform. To assess the platform’s effectiveness, a pilot study with 15 young participants aged 18–28 was conducted. During a self-guided city tour, participants engaged with historical content on the platform and provided feedback through pre- and post-test evaluations. Results indicated heightened awareness of and interest in cultural heritage, demonstrating the model’s potential as both an interactive archive and a tool facilitating intergenerational heritage awareness. Overall, this study highlights the model’s adaptability, scalability, and capacity to bridge generational and technological divides. Full article

This study examines the impact of Environmental, Social, and Governance (ESG) strategies on reducing air pollution in the West Kazakhstan region, a major hub for Kazakhstan’s oil and gas industry. A spatial analysis of atmospheric emissions reveals an uneven distribution of emission sources, predominantly concentrated in the northern industrialized part of the region, where the Karachaganak oil and gas condensate field is located. The ESG model of Karachaganak Petroleum Operating b.v. (KPO), implemented as an integrated management system based on Global Reporting Initiative (GRI) standards, is compared with the ESG strategies of leading oil and gas companies in Kazakhstan and globally, aligning with current international research trends. The analysis underscores the interdependence of technological and social aspects in the transition to a low-carbon economy, confirming the importance of integrating the environmental, social, and governance components of ESG into a unified strategic planning framework for sustainable development. Using econometric modeling, the study establishes a relationship between ESG indicators and the reduction in atmospheric pollution and provides a forecast for emission reductions by 2030. The key measures proposed to improve regional air quality are linked to long-term decarbonization strategies within the context of the sustainable development of the entire region. The proposed algorithm for implementing ESG principles helps to identify the concentration of functions and associated risks at different management levels within Highly Polluting Enterprises (HPEs) and optimizes business processes by focusing efforts on air pollution mitigation. The findings are applicable to other countries, as oil and gas producers worldwide face a number of common air pollution challenges. Full article

This study presents an optimization-driven design of a wireless communications network to continuously transmit environmental variables—temperature, humidity, weight, and water usage—in poultry farms. The reference site is a four-shed facility in Quito, Ecuador (each shed $120\mathrm{m}\times 12\mathrm{m}$) with a data center located $200\mathrm{m}$ from the sheds. Starting from a calibrated log-distance path-loss model, coverage is declared when the received power exceeds the receiver sensitivity of the selected technology. Gateway placement is cast as a mixed-integer optimization that minimizes deployment cost while meeting target coverage and per-gateway capacity; a capacity-aware greedy heuristic provides a robust fallback when exact solvers stall or instances become too large for interactive use. Sensing instruments are Tekon devices using the Tinymesh protocol (IEEE 802.15.4g), selected for low-power operation and suitability for elongated farm layouts. Model parameters and technology presets inform a pre-optimization sizing step—based on range and coverage probability—that seeds candidate gateway locations. The pipeline integrates MATLAB R2024b and LpSolve 5.5.2.0 for the optimization core, Radio Mobile for network-coverage simulations, and Wireshark for on-air packet analysis and verification. On the four-shed case, the algorithm identifies the number and positions of gateways that maximize coverage probability within capacity limits, reducing infrastructure while enabling continuous monitoring. The final layout derived from simulation was implemented onsite, and end-to-end tests confirmed correct operation and data delivery to the farm’s data center. By combining technology-aware modeling, optimization, and field validation, the work provides a practical blueprint to right-size wireless infrastructure for agricultural monitoring. Quantitatively, the optimization couples coverage with capacity and scales with the number of endpoints M and candidate sites N (binaries $M+N+MN$). On the four-shed case, the planner serves 72 environmental endpoints and 41 physical-variable endpoints while keeping the gateway count fixed and reducing the required link ports from 16 to 4 and from 16 to 6, respectively, corresponding to optimization gains of up to 82% and 70% versus dense baseline plans. Definitions and a measurement plan for packet delivery ratio (PDR), one-way latency, throughput, and energy per delivered sample are included; detailed long-term numerical results for these metrics are left for future work, since the present implementation was validated through short-term acceptance tests. Full article

Underwater Wireless Sensor Networks (UWSNs) are widely used technologies in aquatic environments. However, these types of networks face several constraints caused by the mobility of nodes, energy consumption, and constraints due to acoustic communication. In light of this, the location of nodes appears as a promising axis for improving the services expected from these networks. To address these, we suggest the LUCOTED approach—a Linear Constraint Optimization Technique for estimating unknown node positions by selecting anchor nodes with the highest energy and shortest distance, based on randomly initialized conditions. It achieves 98% accuracy, exceeding Gradient Descent and Trilateration methods. Moreover, our method LUCOTED outperforms the DEEC algorithm in terms of error when the number of anchor nodes is below 80 and achieves higher accuracy than the EPRP technique when the number exceeds 100. Full article

Precision agriculture increasingly recognizes the transformative potential of unmanned aerial systems (UASs) for crop monitoring and field assessment, yet research consistently highlights significant usability barriers as the main constraints to widespread adoption. Complex mission planning processes, including detailed flight plan creation and way point management, pose substantial technical challenges that mainly affect non-expert operators. Farmers and their teams generally prefer user-friendly, straightforward tools, as evidenced by the rapid adoption of GPS guidance systems, which underscores the need for simpler mission planning in UAS operations. To enhance accessibility and safety in UAS control, especially for non-expert operators in agriculture and related fields, we propose a Secure UAS Control Framework (SAUCF): a comprehensive system for natural-language-driven UAS mission management with integrated dual-factor biometric authentication. The framework converts spoken user instructions into executable flight plans by leveraging a language-model-powered mission planner that interprets transcribed voice commands and generates context-aware operational directives, including takeoff, location monitoring, return-to-home, and landing operations. Mission orchestration is performed through a large language model (LLM) agent, coupled with a human-in-the-loop supervision mechanism that enables operators to review, adjust, or confirm mission plans before deployment. Additionally, SAUCF offers a manual override feature, allowing users to assume direct control or interrupt missions at any stage, ensuring safety and adaptability in dynamic environments. Proof-of-concept demonstrations on a UAS plat-form with on-board computing validated reliable speech-to-text transcription, biometric verification via voice matching and face authentication, and effective Sim2Real transfer of natural-language-driven mission plans from simulation environments to physical UAS operations. Initial evaluations showed that SAUCF reduced mission planning time, minimized command errors, and simplified complex multi-objective workflows compared to traditional waypoint-based tools, though comprehensive field validation remains necessary to confirm these preliminary findings. The integration of natural-language-based interaction, real-time identity verification, human-in-the-loop LLM orchestration, and manual override capabilities allows SAUCF to significantly lower the technical barrier to UAS operation while ensuring mission security, operational reliability, and operator agency in real-world conditions. These findings lay the groundwork for systematic field trials and suggest that prioritizing ease of operation in mission planning can drive broader deployment of UAS technologies. Full article

The research methodology involved creating a 3D sample model that featured both flat and cylindrical surfaces inclined at angles ranging from 0° to 90° relative to the XY plane. The study investigated the surface topography of additively manufactured samples produced using various technologies, including Fused Filament Fabrication (FFF), masked Stereolithography (mSLA), PolyJet (PJ), and Selective Laser Sintering (SLS). The focus was on how material type, print angle, and measurement location influenced the results. The materials used in the study included PLA, PETG, acrylic resins, PA2200, and VeroClear. Due to the optical properties of the materials used, measurements were carried out on replicas that were prepared using a RepliSet F5 silicone compound from Struers. Consequently, a methodology was developed for measuring surface roughness using the Alicona microscope based on these replicas. A 10× objective lens was used during the measurements, and the pixel size was 0.88 µm × 0.88 µm. Each time, an area of approximately 1 mm × 4 mm was measured. The lowest roughness values were observed for mSLA samples (Sa = 6.72–8.54 µm, Spk + Sk + Svk = 33.36–42.16 µm), whereas SLS exhibited the highest roughness (Sa = 27.86 µm, Spk + Sk + Svk = 183.79 µm). PJ samples exhibited intermediate roughness with significant anisotropy (Sa = 11.65 µm, Spk + Sk + Svk = 72.1 µm), which was strongly influenced by the print angle. FFF surfaces showed directional patterns and layer-dependent roughness, with the Sa parameter being the same (12.44 µm) for both PETG and PLA materials. The steepest slopes were observed for SLS surfaces (Sdq = 7.67), while mSLA exhibited the flattest microstructure (Sdq = 0.48–0.89). Statistical analysis confirmed that material type significantly influenced topography in mSLA, while print angle strongly affected PJ and FFF (although for FFF, further studies would be beneficial). The results of the research conducted can be used to develop a methodology for optimizing the printing process to achieve the required geometric surface structure. Full article

With the continuous development of semiconductor manufacturing technology and information technology, the sizes of wafer chips are becoming smaller and the variety is increasing, which has put forward high requirements for wafer chip precision manufacturing and packaging workshops. On the one hand, the market demand for multiple varieties and small batches will increase the difficulty of scheduling. On the other hand, the complex manufacturing environment brings various types of dynamic events that will disrupt production plans. Accordingly, this work researches the wafer chip precision packaging workshop rescheduling problem under events of machine breakdown, emergency order inserting and original order modification. Firstly, the mathematical model for the addressed problem is established, and the rolling horizon technology is adopted to deal with multiple dynamic events. Then, a hybrid algorithm combining an improved firefly optimization framework and variable neighborhood search strategy is proposed. The population evolution mechanism is designed based on the location-updating law of fireflies in nature. The variable neighborhood search is applied for avoiding local optima and sufficiently exploring in the neighborhood. At last, the test results of comparative experiments and engineering cases indicate that the proposed method is effective and stable and is superior to the current advanced algorithms. Full article

Surface subsidence, a major geological hazard induced by mining activities, severely compromises the sustainable economic development of mining areas and the safety and stability of residents’ livelihoods. Consequently, long-term and effective monitoring and prediction of mining areas are essential. Aiming to identify the key characteristic of narrow and elongated mining areas—where the strike length is significantly greater than the dip length—this study proposes a surface subsidence monitoring method integrating Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) and the Improved Probability Integral Method (IPIM). Specifically, this method utilizes SBAS-InSAR technology to acquire cumulative subsidence results of low-gradient deformation zones in mining areas. To address the issue of excessively fast edge convergence in traditional Probability Integral Method (PIM) applications for narrow and elongated mining areas, the traditional PIM is adjusted by modifying the dip-direction influence radius parameter; this adjustment alters the shape of the dip-direction subsidence curve at the edge of the subsidence basin, thereby resolving the convergence problem. Meanwhile, based on the InSAR deformation gradient theory, the subsidence edge and subsidence center are identified, and the corresponding threshold is determined. The results of SBAS-InSAR and IPIM are then fused via the inverse distance squared weighting (IDSW) method to eliminate discontinuous boundaries in fused results and obtain complete surface subsidence data of the mining area. Taking the 31109-1 working face of the Lijiahao Coal Mine as the study area, 14 scenes of Sentinel-1A imagery and field leveling data of the working face were used to validate the feasibility and accuracy of the proposed method. The results indicate that a distinct rectangular subsidence basin was formed in the working face during the monitoring period. The maximum subsidence measured by the integrated method is 3453 mm, and the location, subsidence curve, and variation trend of the monitored subsidence basin are basically consistent with actual mining conditions. The maximum relative errors of subsidence in the strike and dip directions are 5.2% and 4.1%, respectively. This method can effectively compensate for the limitations of SBAS-InSAR and PIM when applied individually to surface subsidence monitoring in narrow and elongated mining areas, enabling the acquisition of refined subsidence information for the entire mining basin. The research results provide a scientific basis for subsidence monitoring and early warning, disaster prevention and mitigation, and the rational development and utilization of resources in mining areas. Full article