High Level Of Efficiency Research Articles

Real-Time Threat Prevention System for Mitigating Intrusions by Dogs in Livestock Farming using IoT and Machine Learning

One of the challenges encountered by farmers engaged in livestock farming is the menace posed by stray or ownerless dogs, causing harm to the animals being raised on the farm. This not only adversely affects the health of the animals but also impacts the overall cost associated with their upbringing. Consequently, this research introduces the development of a sophisticated system aimed at preventing threats and intrusions by dogs that pose harm to farm animals. The system leverages Internet of Things (IoT) technology and employs Machine Learning algorithms, specifically Convolutional Neural Network, for real-time tracking and monitoring. The research findings reveal that the developed system demonstrates a high level of efficiency, swiftly and accurately classifying animals entering areas equipped with cameras, achieving an impressive accuracy rate of 92.54%. Furthermore, the system is equipped to promptly notify users and emit deterrent sounds to repel dogs entering the monitored area, enhancing its effectiveness in safeguarding livestock and optimizing farm management practices.

Analysis of the spatial–temporal evolution and driving factors of carbon emission efficiency in the Yangtze River economic Belt

As an important economic growth pole and ecological area in China, the urban agglomeration of the Yangtze River Economic Belt (YREB) is the key to carbon emission reduction. Exploring the spatial–temporal evolution and driving variables of its carbon emission efficiency (CEE) is crucial for realizing the goals of carbon peaking and carbon neutrality. The super-efficiency SBM model, the nuclear density method, and the spatial autocorrelation method were used to discuss the spatial–temporal CEE characteristics of 105 cities in the YREB. On the driving factors of carbon emissions, the geographic detector and the Tobit model were combined to explore the spatial differentiation characteristics of the driving factors from the perspective of heterogeneity, and concurrently analyze the single-factor’s effecting intensity and impacting direction, as well as the dual-factors’ interaction effects. The findings indicated that the CEE of YREB generally showed a slow upward trend during 2006–2021. From the perspective of time dynamic evolution, the differentiation of efficiency intensified, and the overall development was toward the high efficiency level. Furthermore, the results of spatial pattern evolution showed that the CEE presents the pattern of “downstream areas > midstream areas > upstream areas”, “high in the east and low in the west”, “hot in the east and cold in the west”, while the spatial clustering effect was significant, showing the distributions of low-low clustering or high-high clustering. Moreover, the results of the geographic detector showed that government intervention, economic growth, and technological progress were the main driving factors. In addition, the interactions government intervention and the other factors were significantly detected. Tobit regression results showed that technological advancement and economic growth had a favorable impact on CEE, but foreign investment, urbanization, and government involvement had negative impacts. In the future, the correlations between provinces and cities should be strengthened and amplified to promote the integrated green development, as well as to improve the ecological environments.

Pulmonary Vein Isolation with Pulsed Field Ablation and Size-Adjustable Cryo-Balloon: A Comparative Procedural Analysis of First-Time Use.

Background: Pulmonary vein isolation (PVI) is the standard of care for the treatment of symptomatic atrial fibrillation (AF). Novel techniques for PVI are the thermal size-adjustable cryo-balloon (CB) system and non-thermal pulsed field ablation (PFA) system. There are currently no data available for a direct comparison between these two systems. Furthermore, with new techniques, it is important to ensure a high level of efficiency and safety during treatment right from initial use. Therefore, the aim of this study was to directly compare the procedural data and safety of these two new PVI techniques in first-time users. Methods: We conducted a single-center prospective study involving 100 consecutive patients with symptomatic atrial fibrillation who underwent first-time PVI using either size-adjustable CB PVI or PFA PVI from July 2023 to March 2024. Results: Acute PVI was achieved in 100% of patients in both groups. First-pass isolation (FPI) was more frequently achieved in the PFA group compared to the size-adjustable CB group. The mean procedural duration and fluoroscopy dose were significantly shorter in the PFA cohort (p < 0.001). Furthermore, a significant reduction in fluoroscopy time was observed during the learning curve within the PFA group (p = 0.023). There were no major complications in both groups. Conclusions: Both systems demonstrate good effectiveness and safety during PVI performed by first-time users. However, the PFA group exhibited a significantly shorter procedural duration.

Using active cooling/heating for 1C1R grey-box model parameter identification in an actual environment: A proof-of-concept study

A model predictive control (MPC) has been important method for managing both comfort and energy saving in heating, ventilation, and air conditioning (HVAC) problems. However, recent data-driven models for MPC, relying on extensive training data, pose challenges for practical implementation in real-world thermal environments due to their time-consuming nature. The grey-box approach, utilizing a simple RC model as a thermal environment proxy, is well known for its data efficiency. Nevertheless, numerical instability arising from the multi-collinearity of observed data can degrade the model’s time efficiency. To deal with this issue, we propose and implement a rapid and stable parameter identification method that integrates active cooling-heating within the actual thermal environment. Our method requires only a single day of data to estimate basic RC model parameters, allowing for immediate use of the RC model in MPC as soon as an air conditioner is installed. Practical Application Using a grey-box model for MPC in HVAC systems is efficient, but the multicollinearity problem of observed data undermines its time efficiency. Our method deals with this challenge by enabling immediate model parameter identification with just 1 day’s data, incorporating active cooling-heating. We demonstrate it in an actual thermal environment. The high level of time and data efficiency afforded by our method is particularly beneficial for IoT applications like malfunction detection and adaptive room reparameterization associated with room rearrangement.

On Some Issues of Modeling the Processes of Monitoring the Effectiveness of the System of Subjects of Public Control in Russia

Monitoring of the effectiveness of the public control system in the Russian Federation. The work analyzes the role and place of this institution of civil society in the system of its other institutions. The role and importance of the institution of public control is substantiated as a key legal guarantee of the implementation, protection and defense of both the constitutional principles of democracy and the participation of Russian citizens in the management of state affairs, as well as the entire system of human and civil rights and freedoms, the rights and legitimate interests of public associations and other non-state non-profit organizations. The author's classification of subjects of public control is carried out. The importance of these subjects achieving a high level of efficiency of their organization and activities is substantiated. It is noted that the dynamics of changes in the level of efficiency of functioning of subjects of public control should be constantly monitored both by public authorities, which are responsible for organizing budgetary financing of the activities of subjects of public control, and by higher-level varieties of these subjects. The article discusses the main features of monitoring the effectiveness of the public control system in Russia, and the author’s definition of it is formulated and justified. The analysis and formalization of the main indicators of the monitoring system of the effectiveness of the public control system in the Russian Federation is carried out. The authors formalized and investigated the main problems that hinder the organization and functioning of the system for monitoring the effectiveness of the public control system in the Russian Federation. The work has developed and justified a system of measures to resolve these problems in order to ensure the preservation and development of the institution of public control in Russia.

Hedge-Algebras-based hybrid control of earthquake-induced buildings using upgraded tuned liquid column dampers

Upgraded Tuned Liquid Column Damper (UTLCD) is an interesting variant of Tuned Liquid Column Damper, which was developed recently. UTLCD consists of a conventional Tuned Liquid Column Damper (TLCD) integrated into an undamped Tuned Mass Damper (TMD). Based on this device, a hybrid type of UTLCD (HUTLCD) is developed to minimize the vibration response of earthquake-induced buildings. HUTLCD is constituted of two independent components, including an active UTLCD (AUTLCD) and a passive UTLCD (PUTLCD). These individual components are connected in parallel to a high-rise building. In this research, the Hedge-Algebras theory (HA) is utilized to design controllers for AUTLCD. This study also uses a new algorithm, Balancing Composite Motion Optimization (BCMO), which has recently been introduced to solve optimization problems. The performance of HUTLCD is compared with that of a pure AUTLCD or a pure PUTLCD, which have the same weight as the HUTLCD. The results achieved from the present work confirm that the HUTLCD is more effective than the pure PUTLCD for the vibration reduction capacity and is better than the pure AUTLCD for both aspects of the vibrational control capacity (for most cases compared with) and saving control energy (for all cases considered). Moreover, the HUTLCD is not more robust than the AUTLCD as the structural stiffness rises, but the HUTLCD still offers higher control effectiveness compared with the AUTLCD or PUTLCD when the structural stiffness decreases. In addition, although the data used to design the optimum HA-based controller is the El Centro earthquake, this controller maintains a high efficiency level for numerous other earthquakes.

Measurement of efficiency and its drivers in the Chilean banking industry.

This paper estimates efficiency measures for the banking system in Chile for the period 2000-2019. In contrast to previous studies, we use input-distance functions, introduce the nonparametric slack-based model, and choose the intermediate inputs approach in determining inputs and outputs. Our results suggest that the Chilean system has achieved relatively high levels of efficiency, although with no significant variation over the sample period. Ownership (government, foreign and public) and size had a positive impact on efficiency. On average, mergers and acquisitions seem to have targeted highly efficient banks in order to improve the overall efficiency of the controlling institution in the short run. Other sources of efficiency gains could be an increase in bond funding or a reduction in expenses and capital holdings. The latter could be induced by deepening the local derivatives market.

Deep Learning and High-Resolution Anoscopy: Development of an Interoperable Algorithm for the Detection and Differentiation of Anal Squamous Cell Carcinoma Precursors-A Multicentric Study.

High-resolution anoscopy (HRA) plays a central role in the detection and treatment of precursors of anal squamous cell carcinoma (ASCC). Artificial intelligence (AI) algorithms have shown high levels of efficiency in detecting and differentiating HSIL from low-grade squamous intraepithelial lesions (LSIL) in HRA images. Our aim was to develop a deep learning system for the automatic detection and differentiation of HSIL versus LSIL using HRA images from both conventional and digital proctoscopes. A convolutional neural network (CNN) was developed based on 151 HRA exams performed at two volume centers using conventional and digital HRA systems. A total of 57,822 images were included, 28,874 images containing HSIL and 28,948 LSIL. Partial subanalyses were performed to evaluate the performance of the CNN in the subset of images acetic acid and lugol iodine staining and after treatment of the anal canal. The overall accuracy of the CNN in distinguishing HSIL from LSIL during the testing stage was 94.6%. The algorithm had an overall sensitivity and specificity of 93.6% and 95.7%, respectively (AUC 0.97). For staining with acetic acid, HSIL was differentiated from LSIL with an overall accuracy of 96.4%, while for lugol and after therapeutic manipulation, these values were 96.6% and 99.3%, respectively. The introduction of AI algorithms to HRA may enhance the early diagnosis of ASCC precursors, and this system was shown to perform adequately across conventional and digital HRA interfaces.

Facile Fabrication of Porous Adsorbent with Multiple Amine Groups for Efficient and Selective Removal of Amaranth and Tartrazine Dyes from Water.

The development of an advanced dye adsorbent that possesses a range of beneficial characteristics, such as high adsorption capacity, swift adsorption kinetics, selective adsorption capability, and robust reusability, remains a challenge. This study introduces a facile method for fabricating an amine-rich porous adsorbent (ARPA), which is specifically engineered for the adsorptive removal of anionic dyes from aqueous solutions. Through a comprehensive assessment, we have evaluated the adsorption performance of ARPA using two benchmark dyes: amaranth (ART) and tartrazine (TTZ). Our findings indicate that the adsorption process reaches equilibrium in a remarkably short timeframe of just 20 min, and it exhibits an excellent correlation with both the Langmuir isotherm model and the pseudo-second-order kinetic model. Furthermore, ARPA has demonstrated an exceptional maximum adsorption capacity, with values of 675.68 mg g-1 for ART and 534.76 mg g-1 for TTZ. In addition to its high adsorption capacity, ARPA has also shown remarkable selectivity, as evidenced by its ability to selectively adsorb TTZ from a mixed dye solution, a feature that is highly desirable for practical applications. Beyond its impressive adsorption capabilities, ARPA can be efficiently regenerated and recycled. It maintains a high level of original removal efficiency for both ART (76.8%) and TTZ (78.9%) even after five consecutive cycles of adsorption and desorption. Considering the simplicity of its synthesis and its outstanding adsorption performance, ARPA emerges as a highly promising material for use in dye removal applications. Consequently, this paper presents a straightforward and feasible method for the production of an effective dye adsorbent for environmental remediation.

Equipment design and parameters recommendation of rubber modified 3D printed asphalt in pavement maintenance

As an advanced technology for pavement crack maintenance with high automation level, high precision and high efficiency, 3D asphalt printing technology can effectively improve worker safety and ensure the stability of maintenance quality. In order to optimize the printing parameters of rubber modified 3D printed asphalt (R-3DPA), a three-axis asphalt 3D printer was developed in this study through the design and selection of each component, and it was used for the 3D printing of R-3DPA strips, layers, and structures. Based on the comparison of continuity, uniformity, and width of R-3DPA strips, the recommended extrusion nozzle diameter, printing temperature, and printing speed parameter combinations were superiorly selected. According to the analysis of filling degree, width error and thickness of R-3DPA layers, the optimal printing strip width was recommended. Through the evaluation of integrity, surface characteristics and accuracy of R-3DPA structures, the suitable printing layer thickness was determined. The test results showed that the three-axis asphalt 3D printer can be used to measure the parameters of R-3DPA, and the printing accuracy and scale meet the requirements. The recommended parameters for R-3DPA are an extrusion nozzle diameter of 2 mm, a printing temperature of 180 °C, a printing speed of 20 mm/s, a printing strip width of 2.25 mm and a printing layer thickness of 0.4 mm, resulting in structures with good integrity and precision.

Extracting building outlines based on convolutional neural networks using the property of linear connectivity

Abstract. For years, researchers have been developing an automated method that can replace humans by drawing the outlines of individual buildings in a vector format, which plays an important role in GIS creation, environmental monitoring, urban planning, population density estimation, and energy supply. There is no doubt that this is an extremely difficult task, not only because of the labor required to develop such a highly intelligent algorithm, but also because of the challenges posed by imperfect imaging conditions, different building structures, and the complexity of the background. One of the current challenges in extracting building outlines is to accurately recreate the polygonal boundaries of the building while extracting vectorized building masks as output for direct use in various applications. This work provides a comprehensive workflow for building extraction and improves the predicted area of buildings through boundary regularization. First, a convolutional neural network is used to train instance segmentation model, then regularization and vectorization processes are performed. The main difference from existing methods is a new regularization method based on the concepts of linear connectivity and convexity of a set of points. This approach can effectively identify and remove points that do not belong to the detected building but were incorrectly segmented by the algorithm. Based on the results of experiments, the algorithm showed a high level of efficiency, comparable to leading methods for extracting building boundaries as PolyWorld.

The design and research of two-stage series-connected helium turbine expanders for hydrogen liquefaction system

The helium turbine expander, is a crucial part of hydrogen liquefaction systems, that determines the overall efficiency of the system. In this paper, a two-stage series-connected helium turbine expanders is designed to achieve a hydrogen liquefaction capacity of 1.7 tonnes per day (TPD). The proposed expander is based on the reverse Brayton cycle and realizes a high pressure ratio of 8.32 to meet the design objective. A dynamic design process in the expansion end is developed and numerically simulated based on the turbine expander design methodology. The off-rated condition performance of the turbine expander is predicted by adjusting the rotational speed and flow rate. The results show that the reasonable rotational speed range of the turbine expander is 46000–54000 RPM, and the reasonable flow coefficient range is 0.6–1.2, which can maintain a high level of efficiency. The internal flow characteristics of the turbine expander are analysed through numerical simulation. The results show that the outlet temperature is suitable for hydrogen liquefaction, and flow loss mainly occurs in the upper-middle part of the impeller and near the tip clearance, followed by the throat of the nozzle. In the prototype tests to verify the rationality of the design of the turbine expanders, the experimental results show that the two turbine expanders can achieve a temperature drop of 31 K, and isentropic efficiencies of 76.8 % and 80.5 %, respectively, which are higher than the design requirements of 75 %. The actual outlet temperature and isentropic efficiency are compared with the numerical simulation results. The errors are less than 4 % and 10 %, respectively, which means that the prototype manufacturing precision meets the system requirements perfectly. Thus, the design of the two-stage series-connected turbine expanders can satisfy the performance design requirements.

High-Speed Spatial–Temporal Saliency Model: A Novel Detection Method for Infrared Small Moving Targets Based on a Vectorized Guided Filter

Infrared (IR) imaging-based detection systems are of vital significance in the domains of early warning and security, necessitating a high level of precision and efficiency in infrared small moving target detection. IR targets often appear dim and small relative to the background and are easily buried by noise and difficult to detect. A novel high-speed spatial–temporal saliency model (HS-STSM) based on a guided filter (GF) is proposed, which innovatively introduces GF into IR target detection to extract the local anisotropy saliency in the spatial domain, and substantially suppresses the background region as well as the bright clutter false alarms present in the background. Moreover, the proposed model extracts the motion saliency of the target in the temporal domain through vectorization of IR image sequences. Additionally, the proposed model significantly improves the detection efficiency through a vectorized filtering process and effectively suppresses edge components in the background by integrating a prior weight. Experiments conducted on five real infrared image sequences demonstrate the superior performance of the model compared to existing algorithms in terms of the detection rate, noise suppression, real-time processing, and robustness to the background.

Thermodynamic analysis and multi-objective optimization of an ORC-based solar-natural gas driven trigeneration system for a residential area

Regarding the growing demands for energy, depletion of fossil fuel resources, and the adverse environmental consequences associated with their utilization, especially global warming, it is imperative to increase the share of renewable resources within energy systems. During the summer season, the residential sector in Khuzestan province, Iran, experiences a significant surge in energy consumption. This elevated demand is mostly attributed to the region's severe temperatures, requiring the extensive use of cooling equipment. As a result, taking into account the high potential for solar energy in the nation, this research first uses TRNSYS software to calculate the cooling and heating loads of a residential area in the province of Khuzestan. Next, a solar-natural gas Combined Cooling, Heating and Power (CCHP) system is designed and modeled using Engineering Equation Solver (EES) software, with the goal of supplying the cooling load. Then, using the particle swarm optimization technique, the system's multi-objective optimization was completed. The results showed that a fuel consumption rate of 0.04274 kg/s is required in steady state conditions to supply a cooling load of 1446.2 kW for the region with 1000 m2 of solar collecting area. A total of 465.7 kW of electric power and 3336 kW of heating are produced. The system's energy and exergy efficiencies were determined to be 186.6 % and 27.81 %, respectively. Eventually, the findings of this study demonstrated that the proposed configuration has the potential to provide multiple positive outcomes with a high level of efficiency, and improve the share of renewable energy.

An ANN Hardness Prediction Tool Based on a Finite Element Implementation of a Thermal–Metallurgical Model for Mild Steel Produced by WAAM

WAAM has emerged as a promising technique for manufacturing medium- and large-scale metal parts due to its high material deposition efficiency and automation level. However, its high heat accumulation and complex thermal evolution strongly affect the resulting microstructures and mechanical properties. The heterogeneous and unpredictable nature of these properties hinder the widespread application of WAAM in the steel construction industry. In this study, an artificial neural network (ANN) hardness model is developed, based on a thermal–metallurgical model for mild steel. The objective is to establish non-linear relationships between the input process parameters and the desired output, i.e., hardness. The thermal–metallurgical model utilizes a well-distributed heat source model, a death-and-birth algorithm, and a metallurgical model to simulate the temperature field and to calculate the microstructure phase fraction. The temperature prediction errors at four thermocouple positions are mostly below 20%. Because of the limited experimental data, twenty-five simulation experiments are performed using the L25 orthogonal array based on the Taguchi method. The analysis of variance (ANOVA) reveals that the travel speed has the greatest impact on hardness. With the dataset from the thermal–metallurgical model, an ANN model to predict hardness is developed. A comparison to experimental data shows excellent performance and accuracy, with the Mean Absolute Percentage Error (MAPE) of ANN predictions within 10% of the targeted hardness.

Development of highly bacterial filtration efficient and antibacterial cellulose acetate/gum rosin composite nanofibers for facemask application

Cellulose acetate (CA) is a non-toxic, renewable, and biodegradable polymeric material that can be effectively electrospuned into bacterial filtration efficient nanofiber membrane for face mask application. However, its fragile and non-antibacterial nature influenced its scalability. In this context, natural antibacterial gum rosin (GR) additive can be explored. Therefore, the present study aimed to produce a CA/GR composite nanofibers membrane for the finest bacterial filtration, excellent antibacterial moiety, and improved tensile properties for facemask application. Hence, in this work, we have studied the effect of GR concentrations (0–15 g) on the needleless electrospinning behavior and fibers' morphology through rheology, electrical conductivity, and SEM analysis. These analyses revealed that GR significantly affects the fibers' spinning behavior, morphology, and diameter of the produced fibers. Later, ATR-FTIR spectroscopy mapped the functional changes in the produced nanofibers that affirmed the integration of GR with CA polymer. This modification resulted in a 3-fold rise in tensile strength and an 11-fold decline in elongation% in 15 g CA/GR composite nanofibers membrane than the control sample. Furthermore, it has shown 98.79 ± 0.10% bacterial filtration efficiency and ∼ 93 % reduction in Staphylococcus Aureus and Klebsiella Pneumoniae bacterial growth, elucidating a high-efficiency level for potential facemask application.

Azobenzene-Integrated NHC Ligands: A Versatile Platform for Visible-Light-Switchable Metal Catalysis.

A new class of photoswitchable NHC ligands, named azImBA, has been developed by integrating azobenzene into a previously unreported imidazobenzoxazol-1-ylidene framework. These rigid photochromic carbenes enable precise control over confinement around a metal's coordination sphere. As a model system, gold(I) complexes of these NHCs exhibit efficient bidirectional E-Z isomerization under visible light, offering a versatile platform for reversibly photomodulating the reactivity of organogold species. Comprehensive kinetic studies of the protodeauration reaction reveal rate differences of up to 2 orders of magnitude between the E and Z isomers of the NHCs, resulting in a quasi-complete visible-light-gated ON/OFF switchable system. Such a high level of photomodulation efficiency is unprecedented for gold complexes, challenging the current state-of-the-art in photoswitchable organometallics. Thorough investigations into the ligand properties paired with structure-reactivity correlations underscored the unique ligand's steric features as a key factor for reactivity. This effective photocontrol strategy was further validated in gold(I) catalysis, enabling in situ photoswitching of catalytic activity in the intramolecular hydroalkoxylation and -amination of alkynes. Given the significance of these findings and its potential as a widely applicable, easily customizable photoswitchable ancillary ligand platform, azImBA is poised to stimulate the development of adaptive, multifunctional metal complexes.

Delivering a sleep intervention across a youth mental health service using non-expert practitioners: A service evaluation.

Poor sleep is highly prevalent in young people and increases risk of mental health difficulties, yet access to sleep interventions remains limited. This paper evaluates the use of a sleep intervention delivered by non-expert practitioners in a secondary care youth mental health service. Assistant psychologists were trained to deliver a six-session 1:1 cognitive-behavioural sleep intervention adapted for use with young people with mental health difficulties. A within-subject design assessed clinical outcomes relating to sleep (Insomnia Severity Index), psychological distress and personal goals (Goal Based Outcome Measures) at four time points. High referral, intervention take-up (82.82%) and completion (70%) rates were reported, together with high baseline levels of insomnia (Insomnia Severity Index mean 20.47, SD 3.68) and poor sleep efficiency (56.36%, SD 17.23). Fifty-six young people (average age 19.2 years, SD 3.25) were included in the outcome analysis. Statistically and clinically significant improvements were seen across all outcome measures, with 68% no longer meeting clinical threshold (ISI ≥15) for insomnia at endpoint. This study demonstrates exceptionally high levels of clinical need and engagement with a sleep intervention adapted specifically for young people with mental health difficulties. Whilst limited by the uncontrolled design, large improvements in insomnia and psychological distress support its effectiveness and utility in clinical settings. More robust implementation and evaluation is warranted in broader youth mental health services to promote earlier access.

Substantiating the rational parameters and operation modes for the hemp seed centrifugal dehuller

The object of research is technological processes, seeds of industrial hemp, and working bodies of the dehuller. A centrifugal-type device for crushing hemp seeds with a closed sector-type working body has been designed. Owing to this, the task related to seed dehulling was solved with a high level of efficiency in separating the seed coat from the kernel. The rational parameters for the dehuller have been substantiated: the diameter of the impeller is 162 mm, the gap between the impeller and the seed repelling panel is 80 mm, the frequency of rotation of the impeller is 2000 min–1. It was established that with the specified parameters and moisture content of the seeds within the limits of conditional (12.0–13.0 %), it is advisable to carry out dehulling process without preliminary separation of the seeds into fractions by width. It was established that an increase in seed size leads to a corresponding increase in the weight share of seed kernels. About 58.2 % of the main mass of seeds is the average fraction with a width of 2.5 to 3.0 mm. It was found that reducing the diameter of the impeller (from 236 mm to 162 mm) at a seed moisture content of 8.8 % improved the efficiency of dehulling. At a rotation frequency of the impeller of 2000 min–1, the highest total number of intact and destroyed kernels (23.23–29.33 %) was achieved for the two studied moistures. With an increase in seed moisture content from 8.8 % to 12.0 %, the number of dehulled kernels in the hempseed cake increased. It was noted that for seeds with a moisture content of 8.8 %, an increase in the gap led to a decrease in the dehulling efficiency for each of the three investigated seed fractions. The total number of dehulled kernels under such conditions decreased by 2.4–6.8 % and amounted to 16.4–26.9 %. For seeds with a moisture content of 12.0 %, an increase in the gap, on the contrary, increased the dehulling efficiency for each of the three investigated seed fractions. The total number of dehulled kernels for seeds of marked moisture increased within the range of 1.4–3.6 % and amounted to 27.4–31.0 %

How Does Fintech Lending Affect Islamic Local Banks' Efficiency During COVID-19 Pandemic in Indonesia?

Fintech lending, also known as peer-to-peer (P2P) lending or online loans, refers to lending provided by non-bank financial technology-based companies that provide financial services by connecting lenders and borrowers on a specific online platform. This paper investigates the impact of fintech lending development on the efficiency of Islamic local banks in Indonesia. Local banks are chosen because they differ from national commercial banks in terms of their characteristics and products, and they also serve limited customers in a specific local area (i.e., provincial level). The presence of Islamic local banks in Indonesia is unique because it fills the gap and has a significant contribution to Muslims who do not have (or do not need) access to larger Islamic commercial banks. This is particularly noteworthy given that Indonesia is the most populous Muslim country. Using a sample of 161 Islamic local banks in Indonesia and provincial-level fintech lending data from 2020Q1 to 2020Q4, we find that fintech development in Indonesia erodes Islamic local banks’ efficiency, suggesting that their presence is significant and could also be damaging for local banks. However, our finding reveals that the negative effects of fintech development can be reduced in banks with higher levels of efficiency. Our results call for policymakers to monitor the development of fintech to maintain a lending ecosystem that can ultimately relate to financial service stability.