<p style="text-align: left;"><strong>Abstract:</strong></p>
<p style="text-align: left;">Increasing organizational productivity by focusing on effectiveness along with the satisfaction of service recipients of service organizations is an inevitable necessity. The present study is an attempt to investigate the impact of knowledge management dimensions on organizational effectiveness in the field of police crime prevention, considering the mediating role of employees' spiritual intelligence. This study is quantitative in terms of data, applied in terms of objective, and correlational in nature. The statistical population of this research consisted of level one and two managers of Strategic Studies Center, Police Science Research Institute, Amin University and Prevention Police in 2022. Based on stratified and simple random sampling, the sample size included 103 participants. The collected data were analyzed by structural equation method using SPSS and Lisrel software. The findings of the research show that knowledge management dimensions have a direct effect of 67% and indirect effect of 48% through the spiritual intelligence of employees on organizational effectiveness. The direct effect of employees' spiritual intelligence on organizational effectiveness is 56%. Also, the dimensions of knowledge management predict 73% of changes in employees' spiritual intelligence. As a result, strengthening the variables of creation, distribution and application of knowledge in the direction of organizational effectiveness should be given serious attention. In addition, the spiritual intelligence of employees as a mediating variable should be strengthened since by strengthening the indicators of spiritual intelligence, the indirect effect of knowledge management dimensions on the organizational effectiveness of the police in the field of crime prevention can be increased.</p>
<p style="text-align: left;"><strong>Key Words:</strong> organizational effectiveness, organizational knowledge, police organization, spiritual intelligence</p>
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<p style="text-align: left;"><strong>1.Introduction</strong></p>
<p style="text-align: left;">Understanding the implications of the dimensions and indicators of knowledge management and spiritual intelligence on organizational effectiveness can be valuable for officials and managers who seek to improve and strengthen performance. However, the necessity of investigating the knowledge management, spiritual intelligence and organizational effectiveness of the police, especially in the field of crime prevention, can be seen as a response to the current environmental conditions and the needs of managers and commanders. On the other hand, increasing the effectiveness of the organization in order to improve the performance of the employees requires nobility and understanding of the direct and indirect effect of the knowledge management and spiritual intelligence components and indicators on the effectiveness of the organization. In fact, by improving the knowledge management and spiritual intelligence indicators, the organizational effectiveness of the police can be improved. In order to achieve organizational goals, including crime prevention, the present research tries to determine the dimensions of the direct and indirect effect of knowledge management through spiritual intelligence as a mediator on the organizational effectiveness of the police in crime prevention.</p>
<ol style="text-align: left;" start="2">
<li><strong>Literature Review</strong></li>
</ol>
<p style="text-align: left;">The present research, which is conducted with the aim of knowing the impact of knowledge management dimensions on organizational effectiveness in the police crime prevention with the mediating role of employees' spiritual intelligence, is based on the dimensions of knowledge management, defined by Bhatt (2001) who considers knowledge management as the process of creating, presenting, distributing and applying knowledge, and spiritual intelligence of Wellman who emphasizes the seven dimensions of spiritual intelligence, including mastery, mindfulness, extrasensory perception, unity, intelligence, trauma, and childhood spirituality, as well as the effectiveness of Robbins (2008) including quality, education development, motivation and flexibility.</p>
<p style="text-align: left;"><strong>3.Methodology</strong></p>
<p style="text-align: left;">This study is quantitative in terms of data, applied in terms of objective, and correlational in nature. The statistical population of this research consisted of level one and two managers of the Strategic Studies Center, Research Institute of Police Sciences and Social Order, Amin University of Police Sciences and Prevention Police in 2022, including 140 participants. The sampling method was based on stratified and simple random sampling method. According to the formula for determining the sample size, 103 participants constituted the sample of the study. Hypotheses testing was conducted using mean tests to analyze the data and calculate the population mean and standard deviation. Additionally, a structural equation model was employed in order to perform multivariate regression, factor analysis, path analysis, and to assess the causal relationship among variables. Also, to measure hidden variables measurable and obvious indicators were used. The data was analyzed using SPSS and Lisrel software.</p>
<p style="text-align: left;"><strong>4.Result</strong></p>
<p style="text-align: left;">The research data and the results obtained through path analysis show that the dimensions of knowledge management not only have a significant direct effect on organizational effectiveness, but also have a greater and stronger effect on spiritual intelligence and that investing through spiritual intelligence has a double effect of 88% directly and indirectly on organizational effectiveness. For this reason, the third and fourth hypotheses of the research were also confirmed.</p>
<p style="text-align: left;"><strong>5.Conclusion</strong></p>
<p style="text-align: left;">The significant effect of knowledge management dimensions on organizational effectiveness has been confirmed in the conducted research. Also, in this research, the effect of knowledge management on organizational effectiveness in crime prevention, and more importantly, the significant large effect of organizational structure on spiritual intelligence have been confirmed. Furthermore, the indirect effect of knowledge management through spiritual intelligence on organizational effectiveness shows that the mediating variable, in addition to the direct effect on organizational effectiveness in the field of crime prevention, also indirectly affects the dimensions which in turn facilitates the management of organizational effectiveness knowledge. Therefore, it is possible to restore and develop the indicators of creation, presentation, distribution and use throughout the organization, especially within the executive layers of the police, and at the same time, consider the indicators of spiritual intelligence including mastery, concern, extrasensory perception and unity which is derived from the nature of humans, in order to increase the intensity of the direct and indirect effect of knowledge management on organizational effectiveness. It should be mentioned that, based on the the findings of the present research, organizational knowledge helps to strengthen spiritual intelligence.</p>
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Byte Pair Encoding (BPE) tokenizers, widely used in Large Language Models, face challenges in multilingual settings, including penalization of non-Western scripts and the creation of tokens with partial UTF-8 sequences. Pretokenization, often reliant on complex regular expressions, can also introduce fragility and unexpected edge cases. We propose SCRIPT (Script Category Representation in PreTokenization), a novel encoding scheme that bypasses UTF-8 byte conversion by using initial tokens based on Unicode script and category properties. This approach enables a simple, rule-based pretokenization strategy that respects script boundaries, offering a robust alternative to pretokenization strategies based on regular expressions. We also introduce and validate a constrained BPE merging strategy that enforces character integrity, applicable to both SCRIPT-BPE and byte-based BPE. Our experiments demonstrate that SCRIPT-BPE achieves competitive compression while eliminating encoding-based penalties for non-Latin-script languages.
Unmanned aerial vehicles (UAVs) are increasingly employed in diverse applications such as land surveying, material transport, and environmental monitoring. Following missions like data collection or inspection, UAVs must land safely at docking stations for storage or recharging, which is an essential requirement for ensuring operational continuity. However, accurate landing remains challenging due to factors like GPS signal interference. To address this issue, we propose a deviation warning system for UAV landings, powered by a novel vision-based model called AeroLite-MDNet. This model integrates a multiscale fusion module for robust cross-scale object detection and incorporates a segmentation branch for efficient orientation estimation. We introduce a new evaluation metric, Average Warning Delay (AWD), to quantify the system's sensitivity to landing deviations. Furthermore, we contribute a new dataset, UAVLandData, which captures real-world landing deviation scenarios to support training and evaluation. Experimental results show that our system achieves an AWD of 0.7 seconds with a deviation detection accuracy of 98.6\%, demonstrating its effectiveness in enhancing UAV landing reliability. Code will be available at https://github.com/ITTTTTI/Maskyolo.git
Integrating gridded weather and earth observation data into impact evaluations holds great promise. It allows researchers to capture environmental context, external shocks, and even to measure outcomes (e.g., land cover change, agricultural production) that surveys might miss due to spatial or temporal data collection constraints. However, with great power comes great responsibility: the increasing ease of extracting time series from these datasets belies potentially complex geospatial and measurement issues that can affect the magnitude, direction, as well as interpretation of impact evaluation estimates. This chapter highlights several of the most common issues while providing resources to help guide researchers to thoughtfully use (and avoid misuse) of weather, vegetation, land cover, and extreme event data in the context of geospatial impact evaluation.
Marek Tiits, Tarmo Kalvet, Chahinez Ounoughi
et al.
Researchers have long used gravity models to analyze international trade patterns, identify export opportunities, and negotiate trade agreements. Recent research has emphasized the significance of relatedness and product complexity research in developing robust economic development strategies. This paper presents a novel approach, incorporating relatedness and product complexity as integral elements for interpreting export potential within gravity models powered by machine learning. Our approach stands out for its proficiency in accurately predicting bilateral trade values at a detailed product group level, providing valuable insights for policymakers and other stakeholders. The research leverages random forest machine learning models for predictions and incorporates relatedness and complexity to reveal new dimensions in international trade analysis.
We present a hybrid mechanism for recommending similar cases of labor and employment litigations. The classifier determines the similarity based on the itemized disputes of the two cases, that the courts prepared. We cluster the disputes, compute the cosine similarity between the disputes, and use the results as the features for the classification tasks. Experimental results indicate that this hybrid approach outperformed our previous system, which considered only the information about the clusters of the disputes. We replaced the disputes that were prepared by the courts with the itemized disputes that were generated by GPT-3.5 and GPT-4, and repeated the same experiments. Using the disputes generated by GPT-4 led to better results. Although our classifier did not perform as well when using the disputes that the ChatGPT generated, the results were satisfactory. Hence, we hope that the future large-language models will become practically useful.
Robinroy Peter, Lavanya Ratnabala, Demetros Aschu
et al.
Mastering autonomous drone landing on dynamic platforms presents formidable challenges due to unpredictable velocities and external disturbances caused by the wind, ground effect, turbines or propellers of the docking platform. This study introduces an advanced Deep Reinforcement Learning (DRL) agent, Lander:AI, designed to navigate and land on platforms in the presence of windy conditions, thereby enhancing drone autonomy and safety. Lander:AI is rigorously trained within the gym-pybullet-drone simulation, an environment that mirrors real-world complexities, including wind turbulence, to ensure the agent's robustness and adaptability. The agent's capabilities were empirically validated with Crazyflie 2.1 drones across various test scenarios, encompassing both simulated environments and real-world conditions. The experimental results showcased Lander:AI's high-precision landing and its ability to adapt to moving platforms, even under wind-induced disturbances. Furthermore, the system performance was benchmarked against a baseline PID controller augmented with an Extended Kalman Filter, illustrating significant improvements in landing precision and error recovery. Lander:AI leverages bio-inspired learning to adapt to external forces like birds, enhancing drone adaptability without knowing force magnitudes.This research not only advances drone landing technologies, essential for inspection and emergency applications, but also highlights the potential of DRL in addressing intricate aerodynamic challenges.
Daisy Damando, B. Gérard Josias Yaméogo, Hermine Zimé-Diawara
et al.
La résistance des moustiques aux insecticides chimiques conventionnels demeure une préoccupation majeure pour les programmes de lutte anti-vectorielle. Les recherches s’orientent actuellement vers l’utilisation d’alternatives naturelles, les bio insecticides. L’activité insecticide de certaines plantes comme le Calotropis procera (Ait.) R.br (Apocynaceae) pourrait avoir un grand intérêt dans ce domaine. Dans cette étude, nous avons effectués un criblage des extraits de feuilles de C. procera (aqueux, hydro- éthanolique, méthanolique et éthanolique) par HPTLC, puis mesurer leur teneur en stérol, triterpène et en cardénolides. Nous avons étudié également l’activité larvicide des extraits aqueux et hydro- éthanolique contre le vecteur de la dengue Aedes aegypti selon la méthodologie décrite par l’OMS.
Le profil chimique des différents extraits montre la présence de flavonoïdes, tanins, stérols et triterpènes, coumarines, alcaloïdes et cardénolides. La plus grande activité larvicide a été obtenue avec l’extrait hydro-éthanolique provenant de feuilles récoltées en saison sèche dans la localité de Kombissiri (partie Centre Sud du Burkina) avec une DL50 de (1,58 mg/ml ± 1,51 ; 1,66 mg/ml)
Les résultats obtenus indiquent que l’extrait hydro-éthanolique de C. procera pourrait servir à la formulation d’un bio-insecticide écologique et peu coûteux pour lutter contre les larves d'Aedes aegypti.
This interview highlights the extensive research project African Water Cities by architectural studio NLÉ, which explores intersections of rapid urbanization and climate change in the African context. NLÉ proposes new strategies for addressing water, culture and heritage management in Africa as Sub-Saharan Africa experiences the second-fastest rates of urbanization and population growth in the world. The discussion also addresses whether and how these strategies fit within the scope of the UN SDGs.
To reduce the heat island effect brought by conventional asphalt roofing products with high solar absorption, we propose a category of self-cleaning and self-cooling composited roofing tile (CRT) made of old corrugated containers (OCC) and tung oil inspired by traditional Chinese oil-paper umbrellas, which can be fabricated at scale and easily recycled for roofing applications. Compared with asphalt shingles, CRTs can improve the solar reflection due to the randomized structure of the cellulose microfibers, contributing to a significant temperature difference of ∼13.2 °C. In addition, they have a high thermal emissivity of 0.93 in the atmospheric window, radiating great amounts of heat into the cold outer space (∼ 3 K). The top layer tung oil film smeared on the OCC pulp fibers transforms the roofing tile from a waterabsorbing to a waterproofing state and significantly enhances the mechanical strength, contributing to a stable thermal performance in outdoor applications. Furthermore, dyed CRTs can selectively reflect visible light for desired colors and effectively reflect near-infrared light to reduce solar heating, which synchronously achieves roof cooling and aesthetic variety. These cheap, eco-friendly, and multifunctional roofing tiles can provide a value-added path for OCC recycling, which may inspire more radiative cooling composites purely from recycling waste towards an energy-saving and sustainable society.
Energy industries. Energy policy. Fuel trade, Renewable energy sources
Stabilnost na male poremećaje, koja se još naziva statička stabilnost ili modalna analiza, se bavi stabilnošću sistema u slučaju malih poremećaja kao što su promene u potrošnji ili proizvodnji na satnom i dnevnom nivou. Prednost ove vrste analize je njeg globalni karakter jer daje sve sopstvene vrednosti matrice stanja sistema, odnosno polove, u okviru jednog sistemskog proračuna. Postojanje samo jednog pola sistema sa pozitivnim realnim delom ukazuje na nestabilan sistem. Mera relativnog učešća određene promenljive stanja, vezane za određeni generator, i određenog pola sistema dobija se računanjem faktora učešća. Sortiranjem faktora učešća za sve polove sistema u opadajući redosled i uspostavljanje korelacije sa tačno određenom promenljivom stanja nekog generatora dobija se povratna sprega do generatora koji su dominantni uzročnici postojanja polova sistema sa pozitivnim realnim delom. U radu se prikazuje izračunavanje sopstvenih vrednosti sistema i uspostavljanje korisničke povratne sprege do generatora uzročnika pojave nestabilnog pola. Uspostavljanje ove povratne sprege i eliminacija nestabilnog pola sistema promenom parametara generatora je demonstrirana na primeru realnog distributivnog sistema ogranka Leskovac sa preko 2500 čvorova i priključenih više od 40 sinhronih generatora.
Energy industries. Energy policy. Fuel trade, Economics as a science
This paper develops a new data-driven approach to characterizing latent worker skill and job task heterogeneity by applying an empirical tool from network theory to large-scale Brazilian administrative data on worker--job matching. We microfound this tool using a standard equilibrium model of workers matching with jobs according to comparative advantage. Our classifications identify important dimensions of worker and job heterogeneity that standard classifications based on occupations and sectors miss. The equilibrium model based on our classifications more accurately predicts wage changes in response to the 2016 Olympics than a model based on occupations and sectors. Additionally, for a large simulated shock to demand for workers, we show that reduced form estimates of the effects of labor market shock exposure on workers' earnings are nearly 4 times larger when workers and jobs are classified using our classifications as opposed to occupations and sectors.
Large Language Model (LLM)-based generative AI systems, such as ChatGPT, demonstrate zero-shot learning capabilities across a wide range of downstream tasks. Owing to their general-purpose nature and potential to augment or even automate job functions, these systems are poised to reshape labor market dynamics. However, predicting their precise impact \textit{a priori} is challenging, given AI's simultaneous effects on both demand and supply, as well as the strategic responses of market participants. Leveraging an extensive dataset from a leading online labor platform, we document a pronounced displacement effect and an overall contraction in submarkets where required skills closely align with core LLM functionalities. Although demand and supply both decline, the reduction in supply is comparatively smaller, thereby intensifying competition among freelancers. Notably, further analysis shows that this heightened competition is especially pronounced in programming-intensive submarkets. This pattern is attributed to skill-transition effects: by lowering the human-capital barrier to programming, ChatGPT enables incumbent freelancers to enter programming tasks. Moreover, these transitions are not homogeneous, with high-skilled freelancers contributing disproportionately to the shift. Our findings illuminate the multifaceted impacts of general-purpose AI on labor markets, highlighting not only the displacement of certain occupations but also the inducement of skill transitions within the labor supply. These insights offer practical implications for policymakers, platform operators, and workers.
Laws that govern land acquisition can lock in old paradigms. We study one such case, the Coal Bearing Areas Act of 1957 (CBAA) which provides minimal social and environmental safegaurds, and deviates in important ways from the Right to Fair Compensation and Transparency in Land Acquisition, Rehabilitation and Resettlement Act 2013 (LARR). The lack of due diligence protocol in the CBAA confers an undue comparative advantage to coal development, which is inconsistent with India's stance to phase down coal use, reduce air pollution, and advance modern sources of energy. We argue that the premise under which the CBAA was historically justified is no longer valid due to a significant change in the local context. Namely, the environmental and social costs of coal energy are far more salient and the market has cleaner energy alternatives that are cost competitive. We recommend updating land acquisition laws to bring coal under the general purview of LARR or, at minimum, amending the CBAA to ensure adequate environmental and social safeguards are in place, both in letter and practice.
India's third Moon mission Chandrayaan 3 will deploy a lander and a rover at a high latitude location of the Moon enabling us to carry out first ever in-situ science investigations of such a pristine location that will potentially improve our understanding on primary crust formation and subsequent modification processes. The primary landing site (PLS), is situated at 69.367621 degS, 32.348126 degE. As a contingency, an alternate landing site (ALS) was also selected at nearly the same latitude but nearly 450 km west to PLS. In this work, a detailed study of the geomorphology, composition, and temperature characteristics of ALS has been carried out using the best-ever high resolution Chandrayaan 2 OHRC DEMs and Ortho images, datasets obtained from Chandrayaan 1 and on-going Lunar Reconnaissance Orbiter. For understanding the thermophysical behaviour, we used a well-established thermophysical model. We found that the Chandrayaan 3 ALS is characterised by a smooth topography with an elevated central part. The ALS is a scientifically interesting site with a high possibility of sampling ejecta materials from Tycho and Moretus. Based on the spectral and elemental analysis of the site, Fe is found to be near approx. 4.8 wt.%, with Mg approx. 5 wt.%, and Ca approx. 11 wt.%. Compositionally, ALS is similar to PLS with a highland soil composition. Spatial and diurnal variability of around 40 K and 175 K has been observed in the surface temperatures at ALS. Although belonging to similar location like PLS, ALS showed reduced daytime temperatures and enhanced night-time temperatures compared to PLS, indicating a terrain of distinctive thermophysical characteristics. Like PLS, ALS is also seems to be an interesting site for science investigations and Chandrayaan 3 is expected to provide new insights into the understanding of lunar science even if it happens to land in the alternate landing site.
The carbon emissions vary over space and time in China, as well as driving forces. It is particularly important to analyze the spatiotemporal variations and driving factors of China’s per capita carbon emissions. This study adopted global Moran’ I and local indicators of spatial association to analyze the spatial autocorrelation of per capita carbon emissions in China during 2004–2019 and discussed the driving factors of per capita carbon emissions by geographically and temporally weighted regression model. The results demonstrated a positive spatial correlation between inter-provincial per capita carbon emissions, but this correlation has gradually decreased since 2008. The High-High clusters were concentrated in the Bohai Economic Rim and the Low-Low clusters were mainly located in the south. Driving factors of per capita carbon emission at the provincial level have spatiotemporal heterogeneity. From 2004 to 2019, per capita GDP, urbanization rate, and energy intensity are the main contributors to per capita carbon emissions, and the role of per capita GDP is weakening, while urbanization rate and energy intensity change in the opposite direction. Foreign direct investment is the main disincentive in most regions. These findings provided a reference for emission reduction policies implemented in different regions.
Landing is a challenging part of autonomous drone flight and a great research opportunity. This PhD proposes to improve on fiducial autonomous landing algorithms by making them more flexible. Further, it leverages its location, Iceland, to develop a method for landing on lava flows in cooperation with analog Mars exploration missions taking place in Iceland now - and potentially for future Mars landings.
Falling cat problem is well-known where cats show their super aerial reorientation capability and can land safely. For their robotic counterparts, a similar falling quadruped robot problem, has not been fully addressed, although achieving safe landing as the cats has been increasingly investigated. Unlike imposing the burden on landing control, we approach to safe landing of falling quadruped robots by effective flight phase control. Different from existing work like swinging legs and attaching reaction wheels or simple tails, we propose to deploy a 3-DoF morphable inertial tail on a medium-size quadruped robot. In the flight phase, the tail with its maximum length can self-right the body orientation in 3D effectively; before touch-down, the tail length can be retracted to about 1/4 of its maximum for impressing the tail's side-effect on landing. To enable aerial reorientation for safe landing in the quadruped robots, we design a control architecture, which has been verified in a high-fidelity physics simulation environment with different initial conditions. Experimental results on a customized flight-phase test platform with comparable inertial properties are provided and show the tail's effectiveness on 3D body reorientation and its fast retractability before touch-down. An initial falling quadruped robot experiment is shown, where the robot Unitree A1 with the 3-DoF tail can land safely subject to non-negligible initial body angles.