Miracle-dz/UniFAR-FTUs huggingface dataset:
Effectively Detecting Loop Closures using Point Cloud Density Maps

—The ability to detect loop closures plays an essential role in any SLAM system. Loop closures allow correcting the drifting pose estimates from a sensor odometry pipeline. In this paper, we address the problem of effectively detecting loop closures in LiDAR SLAM systems in various environments with longer lengths of sequences and agnostic of the scanning pattern of the sensor. While many approaches for loop closures using 3D LiDAR sensors rely on individual scans, we propose the usage of local maps generated from locally consistent odometry estimates. Several recent approaches compute the maximum elevation map on a bird’s eye view projection of point clouds to compute feature descriptors. In contrast, we use a density image bird’s eye view representation, which is robust to viewpoint changes. The utilization of dense local maps allows us to reduce the complexity of features describing these maps, as well as the size of the database required to store these features over a long sequence. This yields a real-time application of our approach for a typical robotic 3D LiDAR sensor. We perform extensive experiments to evaluate our approach against other state-of-the-art approaches and show the benefits of our proposed approach.
Mixed-integer non-linear programming approach for identifying parameters inconsistencies in load flow calculations

For several decades, electrical networks have experienced continuous expansion in size and scale, facilitating the transmission of electrical power from sources to end-users. The growing complexity of these networks necessitates the use of tools like Load Flow (LF) calculations, which are crucial for power systems engineering, playing a critical role in ensuring grid reliability and efficiency [1]. This numerical analysis of electric power within interconnected systems determines steady-state voltages, currents, and power flows across network components [4]. Given the significance of LF calculations, the power systems community has invested substantial efforts in enhancing the convergence of the methods used to solve them [6], including the associated optimization problem known as Optimal Power Flow [2, 3]. Despite these efforts, real-world scenarios, especially in power system planning, often involve imperfect or erroneous network parameters, leading to challenging convergence issues for existing methods [5]. The physical problem studied may be mathematically infeasible due to complex inconsistencies in the parameters, yet the divergence of applied methods is not a clear certificate of this. Consequently, power system engineers usually engage in iterative processes to pinpoint the moment at which divergences occur and identify the responsible parameters, making it a time-consuming endeavor. This research project explores a novel approach centered around Mixed-Integer Non-Linear Programming (MINLP) to detect inconsistencies or errors in the parameters of a power network, on which LF calculations are applied and divergent. The MINLP is obtained by utilizing the non-linear LF equations as constraints and transforming the various network parameters involved in these equations into penalized variables. This transformation facilitates parameters adjustments -or corrections-while the MINLP minimizes them, using binary variables as activators of these modifications. This introduces additional variables and constraints into the optimization problem, following a conventional form in operations research. Solving the MINLP identifies thus the inconsistencies in the
Efficient and Accurate Transformer-Based 3D Shape Completion and Reconstruction of Fruits for Agricultural Robots

—Robots that operate in agricultural environments need a robust perception system that can deal with occlusions, which are naturally present in agricultural scenarios. In this paper, we address the problem of estimating 3D shapes of fruits when only partial observations are available. Generally speaking, such a shape completion can be realized by exploiting prior knowledge about the geometry of the fruit. This is typically done by template matching using traditional optimization algorithms, which are slow but accurate, or by encoding such knowledge into the weights of a neural network, leading to faster but often less accurate estimates. Our approach combines the best of both worlds. It exploits the benefit of having a template representing our object of interest with the advantages of using a neural network to learn how to deform a template. Our experimental evaluation demonstrates that our approach yields accurate estimation at a competitively low inference time in challenging greenhouse environments.
Overcoming Challenges To Sustainability Of Microfinance Institutions: A Review

Microfinance, which seeks to include the poor in the financial system, has grown over time. It refers to financial services for low-income or remote communities that lack access to traditional banking services. Microfinance institutions (MFIs) provide underserved groups with small loans to establish or expand small businesses, thereby assisting them in breaking the cycle of poverty. There are numerous benefits to microfinance such as increased domestic income, entrepreneurial spirit, and social standards; encouraging micro-entrepreneurship, and promoting economic and social gender equality. Despite these advantages, MFI sustainability is a significant concern. A viable MFI can provide long-term assistance to its clients, thereby reducing poverty. Without sustainability, even the most valuable organisations can fail and damage their beneficiaries. Existing research has shed light on challenges to MFI sustainability including operational obstacles, governance concerns, economic factors, and intrinsic factors such as mission drift. While many researchers have examined specific difficulties, there is an absence of comprehensive review studies that detail all these challenges and mitigation solutions. This paper fills the gap by reviewing MFI sustainability concerns and proposes interventions to mitigate the challenges from the existing literature with a view to strengthening microfinance in the future.
Sphagnum y sus microbiontes, una fuente prolífica de aplicaciones

The bryophytes are the first terrestrial plants, characterized by an alternation of generations: the haploid gametophyte (dominant generation) and the diploid sporophyte, which depends nutritionally on the gametophyte. Among them, stands out the genus Sphagnum, formed by two types of cells: chlorocysts and hyalocysts. The hyalocysts are dead cells which accumulate water and are the niche of microorganisms associated with them. Sphagnum mosses can host different types of microbionts: symbionts, commensals, parasites or pathogens. These relationships are highly specific and may be both intracellular and epiphytic. The main microorganisms found in Sphagnum mosses are antifungal bacteria, facultative human pathogens, methanotrophic and methanogenic bacteria as well as cyanobacteria and different types of fungi. Each of these associations hide applications of great importance in agriculture, medicine and biotechnology. These show that sphagnages are a source of natural fungicides, biofertilizers, hormones such as indoleacetic acid (IAA) that promote plant growth, and even tools to reduce climate change and combat human diseases. Therefore, Sphagnum and its microbiota are of great interest to science and hides multiple applications.
Multi-Classiﬁer Feature Fusion-Based Road Detection for Connected Autonomous Vehicles

: Connected autonomous vehicles (CAVs) currently promise cooperation between vehicles, providing abundant and real-time information through wireless communication technologies. In this paper, a two-level fusion of classiﬁers (TLFC) approach is proposed by using deep learning classiﬁers to perform accurate road detection (RD). The proposed TLFC-RD approach improves the classiﬁcation by considering four key strategies such as cross fold operation at input and pre-processing using superpixel generation, adequate features, multi-classiﬁer feature fusion and a deep learning classiﬁer. Speciﬁcally, the road is classiﬁed as drivable and non-drivable areas by designing the TLFC using the deep learning classiﬁers, and the detected information using the TLFC-RD is exchanged between the autonomous vehicles for the ease of driving on the road. The TLFC-RD is analyzed in terms of its accuracy, sensitivity or recall, speciﬁcity, precision, F1-measure and max F measure. The TLFC-RD method is also evaluated compared to three existing methods: U-Net with the Domain Adaptation Model (DAM), Two-Scale Fully Convolutional Network (TFCN) and a cooperative machine learning approach (i.e., TAAUWN). Experimental results show that the accuracy of the TLFC-RD method for the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) dataset is 99.12% higher than its competitors.
CLINICO-THERAPEUTIC STUDIES ON CANINE HEARTWORM DISEASE

Canine heartworm disease (dirofilarosis) is caused by Dirofilaria immitis . Most affected dogs were asymptomatic but the most common clinical features of the disease were progressive weight loss, exercise intolerance, laboured breathing and coughing. Haemogram of the affected dogs revealed marked leukocytosis, eosinophilia and thrombocytopenia. Serum biochemical investigations showed significant increase of alkaline phosphatase, ALT and serum creatinine whereas serum glucose levels in the affected dogs were found significantly decreased. Results also indicated that combined administration of ivermectin @ 0.3 mg/Kg BW orally at two weeks interval for 6 weeks, doxycycline @ 10 mg/Kg BW orally once daily given for 4 weeks and prednisolone @ 0.5 mg/Kg BW orally once daily for first 10 days followed by on alternate days for next 10 days was found effective. Affected dogs showed remarkable reduction in circulating microfilaria along with good improvement of clinical signs.
Beyond the hype: The hard work behind analytics success

Many companies still struggle to figure out how to use analytics to take advantage of their data. In many respects, the hype surrounding the promise of analytics glosses over the hard work necessary to fulfill that promise. It is hard work to understand what data a company has, to monitor the many processes necessary to make data sufficient (accurate, timely, complete, accessible, reliable, consistent, relevant, and detailed), and to improve managers ability to use data. This unsexy side of analytics is where companies need to excel in order to maximize the value of their analytics initiatives, but it is also where many such efforts stall. Most managers are still quite positive about the potential of analytics. They've seen increased interest in analytics over the past few years, and they expect its use to continue to grow in their organizations. In addition, use of analytics for innovation remains steady. Achieving competitive advantage with analytics requires resolve and a sustained commitment to changing the role of data in decision making. This commitment touches many aspects of organizational behavior, from revamping information management to adapting cultural norms.
BIOINFORMATICS APPLICATIONS NOTE

Summary: Assemble is an intuitive graphical interface to analyze, manipulate and build complex 3D RNA architectures. It provides several advanced and unique features within the framework of a semi-automated modeling process that can be performed by homology and ab initio with or without electron density maps. Those include the interactive editing of a secondary structure and a searchable, embedded library of annotated tertiary structures. Assemble helps users with performing recurrent and otherwise tedious tasks in structural RNA research. Availability and Implementation: Assemble is released under an open-source license (MIT license) and is freely available at http://bioinformatics.org/assemble. It is implemented in the Java language and runs on MacOSX, Linux and Windows operating systems.
Fully dynamic recognition algorithm and certificate for directed cographs

This paper presents an optimal fully dynamic recognition algorithm for directed cographs. Given the modular decomposition tree of a directed cograph G, the algorithm supports arc and vertex modification (insertion or deletion) in O(d) time where d is the number of arcs involved in the operation. Moreover, if the modified graph remains a directed cograph, the modular decomposition tree is updated; otherwise, a certificate is returned within the same complexity. © 2006 Elsevier B.V. All rights reserved.
An Asymmetric Watermarking Method in the DCT Domain Based on RC4-Permutation and Chaotic Map

This paper presents an asymmetric watermarking method in the DCT domain for still images based on permutation and chaos. This method uses secret watermark as private key and public watermark as public key. The public watermark has a normal distribution with mean = 0 and variance = 1. The secret watermark is obtained by permutating the public watermark according to combination of a part of RC4 algorithm and a logistic map. The watermark is embedded into mid-frequency components of the DCT block for better robustness. The detection process is implemented by correlation test between the public watermark and the received image. Experiments show that the watermarking method was proved to be robust againts some typical image processings (cropping, JPEG compression, resizing, rotation, sharpening, and noising).
Principals’ Perceptions of Transformational Leadership in Secondary School of Muzaffarabad Azad Kashmir

This study investigates the Levels of transformational leadership among secondary school principals in Muzaffarabad, by a focus on its essential role in shaping educational environments. Transformational leadership, characterized by its emphasis on inspiring and motivating followers, encouragement innovation, and driving progressive change, holds significant importance in the realm of educational institutions. The research employs a quantitative methodology, involving 90 secondary school principals, to investigate into the comprehension and implementation of transformational leadership. Descriptive statistics indication a moderate level of transformational leadership displayed by principals across various dimensions. The findings add valuable understandings to the continuing discourse on educational leadership, involvement associations for the development of impactful platforms and knowledgeable policies. This research highlights the requirement for nuanced leadership approaches to promote educational consequences in Muzaffarabad, underscoring the transformative prospective of effective leadership in the energetic landscape of secondary education.
SMCC : Self-Managing Congestion Control Algorithm

network congestion in the early stage and successfully prevents periodic packet loss that usually occurs in TCP Reno. It has been demonstrated that TCP Vegas outperforms TCP Reno in many aspects. However, TCP Vegas suffers several problems that affect its congestion avoidance mechanism. One of the most important weaknesses in TCP Vegas is that alpha and beta depend on a good expected throughput estimate, which as we have seen, depends on a good minimum RTT estimate. In order to make the system more robust alpha and beta must be made responsive to network conditions (they are currently chosen statically). This paper proposes a modified Vegas algorithm, which can be adjusted to present good performance compared to other transmission control protocols (TCPs). In order to do this, we use PSO algorithm to tune alpha and beta. The simulation results validate the advantages of the proposed algorithm in term of performance.
Multi-stream signals separation based on space-time-isomeric (SPATIO) array using metasurface antennas

In spatial domain signal processing, it is necessary to equip more antennas at the receiver to improve spatial demultiplexing capability. However, increasing the number of antennas under restricted space will reduce antenna spacing and raise the channel correlation, making the number of signal streams spatially demultiplexed much smaller than that of antennas. This paper proposes a method to design a space-time-isomeric (SPATIO) array based on metasurface antennas under wireless multipath conditions. Each antenna in this array has a diﬀerent pattern and varies independently with time, reducing the channel correlation by superposing multipath at distinct positions and moments. Based on the SPATIO array, we present an array parameter design scheme based on inﬁnity norm minimization, which can maximize the received energy of each stream while separating multi-stream received signals. Simulation results illustrate the performance of the SPATIO array for multi-stream signal reception. Compared with conventional multiple-input multiple-output arrays, the proposed array can reduce the bit error rate by one order of magnitude under the same simulation conditions.
Antioxidant and Antimicrobial Properties of Yellow Mealworm Antioxidant and Antimicrobial Properties of Yellow Mealworm (Tenebrio molitor) Protein Concentrates and Hydrolysates Derived (Tenebrio molitor) Protein Concentrates and Hydrolysates Derived from Varied Dietary Regimens from Varied Dietary R

The larval stage of the darkling beetle, Tenebrio molitor , often referred to as the yellow mealworm, has received signi ﬁ cant attention as a promising source of high-quality protein for human consumption due to its exceptional nutritional pro ﬁ le. This study focused on assessing the antioxidant and antimicrobial activities of protein concentrates and hydrolysates obtained from mealworm specimens raised under distinct dietary conditions. One cohort of meal-worms was sustained on food waste comprising rice bran, milled corn, and carrots (self-bred) (SM), while another group was exclusively provided with rice bran (commercially fed) (CM). Enzymatic hydrolysis of mealworm protein was accomplished using protease enzymes (Maxipro NPU). The ﬁ ndings demonstrated that protein hydrolysates from yellow mealworms, particularly those reared on food waste, exhibited signi ﬁ cantly higher antioxidant and antimicrobial activities compared to protein concentrates. The results revealed that SM-based protein hydrolysates exhibited a DPPH free radical scavenging activity of 5.96 ± 0.95 mg of TEAC/g and a ferric reducing antioxidant activity (FRAP) assay value of 2.07 ± 0.07 mg of Fe (II)/g. Our study also highlighted that protein hydrolysates derived from both SM and CM showed an antimicrobial effect against tested strains Escherichia coli and Staphylococcus aureus bacterial strains compared to protein concentrates. In conclusion, our results highlighted enhanced antioxidant and antimicrobial properties of hydrolyzed mealworm protein samples in comparison to their non-hydrolyzed counterparts.
National Beef Tenderness Survey – 2022: Consumer Sensory Panel Evaluations and Warner-Bratzler Shear Force of Beef Steaks From Retail and Foodservice

: Beef retail steaks from establishments across 11 US cities and beef foodservice steaks from establishments in 6 US cities were evaluated by consumer sensory evaluations and Warner-Bratzler shear (WBS) force analyses. The retail tenderloin had the lowest ( P < 0.05) WBS force value compared to other retail cuts. The retail steak with the greatest ( P < 0.05) WBS force value was the top sirloin. Foodservice ribeye and top loin steaks had greater ( P < 0.05) WBS force values compared to the tenderloin. All retail top blade, bone-in ribeye, Porterhouse, and tenderloin steaks were categorized as “ very tender ” ( < 31.4 N). There were no ( P > 0.05) differences in WBS force values among USDA quality grade groups for foodservice steaks. Retail tenderloin steaks received the highest ( P < 0.05) consumer rating for overall like/dislike, tenderness like/dislike, tenderness level, flavor like/dislike, and juiciness like/dislike compared to all other retail cuts. There were no ( P > 0.05) differences among the 4 foodservice cuts for consumer sensory ratings of overall like/dislike, tenderness like/dislike, tenderness level, flavor like/dislike, and juiciness like/dislike. There were no ( P > 0.05) USDA quality grade differences for ribeye, top loin, top sirloin, and tenderloin foodservice steaks for overall like/dislike, tenderness like/dislike, tenderness level, flavor like/dislike, and juiciness like/dislike. Regardless of source (foodservice or retail), USDA grade group, or beef cut, measures of tenderness in this survey reveal ratings and values that should meet most consumer expectations in the marketplace.
Improving the t-SNE Algorithms for Cytometry and Other Technologies: Cen-Se' Mapping

SNE methods are a set of 9 to 10 interconnected algorithms that map high-dimensional data into low-dimensional space while minimizing loss of information. Each step in this process is important for producing high-quality maps. Cense′™ mapping not only enhances many of the steps in this process but also fundamentally changes the underlying mathematics to produce high-quality maps. The key mathematical enhancement is to leverage the Cauchy distribution for creating both high-dimensional and lowdimensional similarity matrices. This simple change eliminates the necessity of using perplexity and entropy and results in maps that optimally separate clusters defined in high-dimensional space. It also eliminates the loss of cluster resolution commonly seen with t-SNE with higher numbers of events. There is just one free parameter for Cen-se′ mapping, and that parameter rarely needs to change. Other enhancements include a relatively low memory footprint, highly threaded implementation, and a final classification step that can process millions of events in seconds. When the Cen-se′ mapping system is integrated with probability state modeling, the clusters of events are positioned in a reproducible manner and are colored, labeled, and enumerated automatically. We provide a step-by-step, simple example that describes how the Cen-se′ method works and differs from the t-SNE method. We present data from several experiments to compare the two mapping strategies on high-dimensional mass cytometry data. We provide a section on information theory to explain how the steepest gradient equations were formulated and how they control the movement of the low-dimensional points as the system renders the map Since existing implementations of the t-SNE algorithm can easily be modified with many of these enhancements, this work should result in more effective use of this very exciting and far-reaching new technology.
Community structure of woody plants on islands along a bioclimatic gradient

Understanding patterns of community structure and the causes for their variation can be furthered by comparative biogeographic analyses of island biotas. We used woody plant data at the local scale to investigate variations in species rarity, alpha, beta, and gamma diversity within and between three islands from the oceanic archipelagoes of Azores, Canaries and Mascarene. We used standardized protocols to sample ten 50 m × 50 m forest plots in each of the three islands with contrasting climate and regional species pools: Terceira (Azores), Tenerife (Canaries), and Reunion (Mascarene Islands). Occupancy frequency distributions and species abundance distributions were used to investigate rarity. The partitioning of beta diversity in a distance-decay framework was used to test for spatial patterns of community composition. Rarity was much more pronounced in the highly diverse islands of Tenerife and Reunion than in the regionally poorer island of Terceira. The number of species rose faster with increasing sample area in both Tenerife and Reunion. The slope of the species rank abundance curve was steeper in Terceira whereas the richer island assemblages approached a lognormal model. Compositional changes according to spatial distance were mostly due to replacement of species in Terceira and Reunion. Our results point to important differences in the community structure of Terceira, which is the less diverse and temperate region in comparison to Tenerife and Reunion which are highly diverse.